# Deeplearning4j

## EN 1.0.0-beta6

- [Eclipse DeepLearning4J](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/master.md)
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/quickstart.md): Quickstart for Java using Maven
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials.md): Deeplearning4j Tutorials
- [Quickstart with MNIST](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/quickstart-with-mnist.md)
- [MultiLayerNetwork And ComputationGraph](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/multilayernetwork-and-computationgraph.md)
- [Logistic Regression](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/logistic-regression.md)
- [Built-in Data Iterators](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/built-in-data-iterators.md)
- [Feed Forward Networks](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/feed-forward-networks.md)
- [Basic Autoencoder](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/basic-autoencoder.md): Anomaly Detection Using Reconstruction Error
- [Advanced Autoencoder](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/advanced-autoencoder.md): Trajectory Clustering Using AIS
- [Convolutional Networks](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/convolutional-networks.md): Train FaceNet Using Center Loss
- [Recurrent Networks](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/recurrent-networks.md): Sequence Classification Of Synthetic Control Data
- [Early Stopping](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/early-stopping.md)
- [Layers and Preprocessors](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/layers-and-preprocessors.md)
- [Hyperparameter Optimization](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/hyperparameter-optimization.md)
- [Using Multiple GPUs](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/using-multiple-gpus.md)
- [Clinical Time Series LSTM](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/clinical-time-series-lstm.md)
- [Sea Temperature Convolutional LSTM](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/sea-temperature-convolutional-lstm.md)
- [Sea Temperature Convolutional LSTM 2](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/sea-temperature-convolutional-lstm-example-2.md)
- [Instacart Multitask Example](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/instacart-multitask-example.md)
- [Instacart Single Task Example](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/instacart-single-task-example.md)
- [Cloud Detection Example](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/tutorials/cloud-detection-example.md)
- [Core Concepts](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/core-concepts.md): Introduction to Deeplearning4J concepts.
- [Cheat Sheet](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/cheat-sheet.md): Snippets and links for common functionality in Eclipse Deeplearning4j.
- [Examples Tour](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/examples-tour.md): Brief tour of available examples in DL4J.
- [Deep Learning Beginners](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/beginners.md): Road map for beginners new to deep learning.
- [Build from Source](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/build-from-source.md): Instructions to build all DL4J libraries from source.
- [Contribute](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/contribute.md): How to contribute to the Eclipse Deeplearning4j source code.
- [Eclipse Contributors](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/contribute/eclipse-contributors.md): IP/Copyright requirements for Eclipse Foundation Projects
- [Benchmark Guide](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/benchmark.md): General guidelines for benchmarking in DL4J and ND4J.
- [About](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/about.md): Facts and introduction to Eclipse Deeplearning4j, the top JVM deep learning framework.
- [Release Notes](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/getting-started/release-notes.md): New changes in each release of Eclipse Deeplearning4j.
- [Backends](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/backends.md): Hardware setup for Eclipse Deeplearning4j, including GPUs and CUDA.
- [CPU and AVX](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/backends/cpu.md): CPU and AVX support in ND4J/Deeplearning4j
- [cuDNN](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/backends/config-cudnn.md): Using the NVIDIA cuDNN library with DL4J.
- [Performance Issues](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/backends/performance-issues.md): How to Debug Performance Issues
- [Memory Management](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/config-memory.md): Setting available Memory/RAM for a DL4J application
- [Memory Workspaces](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/config-memory/config-workspaces.md): Workspaces are an efficient model for memory paging in DL4J.
- [Snapshots](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/config-snapshots.md): Using daily builds for access to latest Eclipse Deeplearning4j features.
- [Maven](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/maven.md): Configure the Maven build tool for Deeplearning4j.
- [SBT, Gradle, & Others](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/config/buildtools.md): Configure the build tools for Deeplearning4j.
- [Autoencoders](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/autoencoders.md)
- [Multilayer Network](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/multilayernetwork.md): Simple and sequential network configuration.
- [Computation Graph](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/computationgraph.md): How to build complex networks with DL4J computation graph.
- [Convolutional Neural Network](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/convolutional.md): Also known as CNN.
- [Recurrent Neural Network](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/recurrent.md): Recurrent Neural Network (RNN) implementations in DL4J.
- [Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/layers.md): Supported neural network layers.
- [Vertices](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/vertices.md): Computation graph nodes for advanced configuration.
- [Iterators](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/iterators.md): Data iteration tools for loading into neural networks.
- [Listeners](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/listeners.md): Adding hooks and listeners on DL4J models.
- [Custom Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/custom-layer.md): Extend DL4J functionality for custom layers.
- [Model Persistence](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/model-persistence.md): Saving and loading of neural networks.
- [Activations](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/activations.md): Special algorithms for gradient descent.
- [Updaters](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/models/updaters.md): Special algorithms for gradient descent.
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/model-zoo/overview.md): Prebuilt model architectures and weights for out-of-the-box application.
- [Zoo Models](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/model-zoo/zoo-models.md)
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/nd4j/overview.md): Comprehensive programming guide for ND4J. This user guide is designed to explain (and provide examples for) the main functionality in ND4J.
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/nd4j/quickstart.md): ND4J Key features and brief samples.
- [Backend](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/nd4j/backend.md)
- [Basics](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/nd4j/basics.md): Elementwise Operations And Basic Usage
- [Elementwise Operations](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/nd4j/elementwise.md)
- [Matrix Manipulation](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/nd4j/matrix-manipulation.md)
- [Syntax](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/nd4j/syntax.md)
- [Tensors](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/nd4j/tensor.md)
- [Importing TensorFlow models](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/samediff/importing-tensorflow.md)
- [Variables](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/samediff/variables.md): What types of variables are used in SameDiff, their properties and how to switch these types.
- [Ops](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/samediff/ops.md): What kind of operations is there in \`SameDiff\` and how to use them
- [Adding Ops](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/samediff/adding-ops.md): How to add differential functions and other ops to SameDiff graph.
- [Evaluation](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/tuning-and-training/evaluation.md): Tools and classes for evaluating neural network performance
- [Visualization](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/tuning-and-training/visualization.md): How to visualize, monitor and debug neural network learning.
- [Trouble Shooting](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/tuning-and-training/troubleshooting-training.md): Understanding common errors like NaNs and tuning hyperparameters.
- [Early Stopping](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/tuning-and-training/early-stopping.md): Terminate a training session given certain conditions.
- [t-SNE Visualization](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/tuning-and-training/tsne-visualization.md): Data visualizaiton with t-SNE with higher dimensional data.
- [Transfer Learning](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/tuning-and-training/transfer-learning.md)
- [Introduction/Getting Started](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/distributed-deep-learning/intro.md): Deeplearning4j on Spark: Introduction
- [Technical Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/distributed-deep-learning/technicalref.md): Deeplearning4j on Spark: Technical Explanation
- [Spark Guide](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/distributed-deep-learning/howto.md): Deeplearning4j on Spark: How To Guides
- [Spark Data Pipelines Guide](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/distributed-deep-learning/data-howto.md): Deeplearning4j on Spark: How To Build Data Pipelines
- [API Reference](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/distributed-deep-learning/apiref.md): This page provides the API reference for key classes required to do distributed training with DL4J on Spark. Make sure you have read the introduction guide for deeplearning4j Spark training.
- [Parameter Server](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/distributed-deep-learning/parameter-server.md): Deeplearning4j supports fast distributed training with Spark and a parameter server.
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/overview.md): Overview of model import.
- [Get Started](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/get-started.md): Getting started with model import.
- [Supported Features](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/supported-features.md): Supported Keras features.
- [Activations](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/supported-features/activations.md): Supported Keras activations.
- [Losses](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/supported-features/losses.md): Supported Keras loss functions.
- [Regularizers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/supported-features/regularizers.md): Supported Keras regularizers.
- [Initializers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/supported-features/initializers.md): Supported Keras weight initializers.
- [Constraints](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/supported-features/constraints.md): Supported Keras constraints.
- [Optimizers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/supported-features/optimizers.md): Supported Keras optimizers
- [Functional Model](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/model-functional.md): Importing the functional model.
- [Sequential Model](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/model-sequential.md): Importing the functional model.
- [API Reference](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference.md): Keras model import API
- [Core Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/core-layers.md)
- [Convolutional Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/convolutional-layers.md)
- [Embedding Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/embedding-layers.md)
- [Local Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/local-layers.md)
- [Noise Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/noise-layers.md)
- [Normalization Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/normalization-layers.md)
- [Pooling Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/pooling-layers.md)
- [Recurrent Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/recurrent-layers.md)
- [Wrapper Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/wrapper-layers.md)
- [Advanced Activations](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/keras-import/api-reference/advanced-activations.md)
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/arbiter/overview.md): Introduction to using Arbiter for hyperparameter optimization.
- [Layer Spaces](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/arbiter/layer-spaces.md): Set a search spaces for layers.
- [Parameter Spaces](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/arbiter/parameter-spaces.md): Set a search spaces for parameters.
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/overview.md): Overview of the vectorization and ETL library for DL4J.
- [Records](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/records.md): How to use data records in DataVec.
- [Reductions](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/reductions.md)
- [Schema](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/schema.md): Schemas for datasets and transformation.
- [Serialization](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/serialization.md): Data wrangling and mapping from one schema to another.
- [Transforms](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/transforms.md): Data wrangling and mapping from one schema to another.
- [Analysis](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/analysis.md): Gather statistics on datasets.
- [Readers](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/readers.md): Read individual records from different formats.
- [Conditions](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/conditions.md)
- [Executors](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/executors.md): Execute ETL and vectorization in a local instance.
- [Filters](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/filters.md): Selection of data using conditions.
- [Operations](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/operations.md): Implementations for advanced transformation.
- [Normalization](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/normalization.md)
- [Visualization](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/datavec/visualization.md)
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/language-processing/overview.md): Overview of language processing in DL4J
- [Word2Vec](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/language-processing/word2vec.md): Neural word embeddings for NLP in DL4J.
- [Doc2Vec](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/language-processing/doc2vec.md): Doc2Vec and arbitrary documents for language processing in DL4J.
- [Sentence Iteration](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/language-processing/sentence-iterator.md): Iteration of words, documents, and sentences for language processing in DL4J.
- [Tokenization](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/language-processing/tokenization.md): Breaking text into individual words for language processing in DL4J.
- [Vocabulary Cache](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/language-processing/vocabulary-cache.md): Mechanism for handling general NLP tasks in DL4J.
- [Setup](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/android/setup.md): Setting up and configuring Android Studio for DL4J.
- [Tutorial: First Steps](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/android/first-steps.md): Using Deep Learning and Neural Networks in Android Applications
- [Tutorial: Classifier](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/android/linear-classifier.md): How to create an IRIS classifier on Android using Eclipse Deeplearning4j.
- [Tutorial: Image Classifier](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/android/image-classification.md): How to create an Android Image Classification app with Eclipse Deeplearning4j.
- [FAQ](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/faq.md): Commonly asked questions about Eclipse Deeplearning4j, deep learning, and artificial intelligence.
- [Press](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/press.md): List of press articles on Eclipse Deeplearning4j.
- [Support](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/support.md): Open source and enterprise support for Deeplearning4j.
- [Why Deep Learning?](https://deeplearning4j.konduit.ai/en-1.0.0-beta6/why-deep-learning.md): When to choose deep learning vs. other algorithms.

## EN 1.0.0-beta7

- [Eclipse Deeplearning4j](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/master.md): Comprehensive documentation for Eclipse Deeplearning4j and its ecosystem — ND4J, SameDiff, DataVec, Keras Import, Python4J, and more
- [Ecosystem Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/core-concepts/ecosystem-overview.md): Overview of the Eclipse Deeplearning4j ecosystem — ND4J, DL4J, DataVec, SameDiff, Python4J, and OmniHub
- [Tensors and NDArrays](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/core-concepts/tensors-and-ndarrays.md): INDArray fundamentals — shape, rank, stride, DataType, creating arrays, views vs copies, and off-heap memory in ND4J
- [Data Pipelines](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/core-concepts/data-pipelines.md): Loading, transforming, and feeding data for training — RecordReader, DataSetIterator, normalization, and mini-batching
- [Neural Network Fundamentals](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/core-concepts/neural-net-fundamentals.md): Layers, activation functions, loss functions, weight initialization, and regularization in Deeplearning4j
- [The Training Loop](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/core-concepts/training-loop.md): Building, configuring, and training neural networks — NeuralNetConfiguration, updaters, fit(), listeners, and ComputationGraph
- [Evaluation](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/core-concepts/evaluation.md): Evaluating model performance — classification metrics, ROC curves, regression metrics, and evaluation during training
- [Memory and Workspaces](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/core-concepts/memory-and-workspaces.md): Off-heap memory management, JVM configuration, workspace modes, and troubleshooting memory issues in ND4J and DL4J
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview.md): Architecture, INDArray interface, memory model, data types, and backend system for the ND4J tensor library
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/quickstart.md): Hands-on quickstart guide for ND4J — creating arrays, basic operations, indexing, and shape manipulation
- [Creating NDArrays](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/creating-ndarrays.md): All methods for creating INDArrays — factory methods, from Java arrays, random, combining, and typed creation
- [Indexing and Slicing](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/indexing-and-slicing.md): Accessing and modifying elements, rows, columns, and sub-arrays of INDArrays using NDArrayIndex
- [Operations](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/operations.md): Scalar, element-wise, transform, reduction, broadcast, comparison, and linear algebra operations on INDArrays
- [Matrix Manipulation](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/matrix-manipulation.md): Reshaping, transposing, permuting, concatenating, sorting, and other shape manipulation operations on INDArrays
- [Data Types](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/data-types.md): The DataType enum, per-array typing, type casting, mixed precision, and migration from global data type
- [Activations](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/activations.md): Activation functions in ND4J — the Activation enum, IActivation interface, mathematical definitions, and usage in layers
- [Updaters](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/updaters.md): Optimization algorithms in ND4J — Adam, SGD, AdaGrad, learning rate schedules, and per-layer updater configuration
- [Loss Functions](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/loss-functions.md): All loss functions in ND4J — ILossFunction implementations, usage in output layers, weighted loss, and custom loss functions
- [Weight Initialization](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/weight-initialization.md): Weight initialization strategies in ND4J — WeightInit enum, IWeightInit interface, and choosing the right initializer
- [Serialization](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/serialization.md): Saving and loading INDArrays in binary, text, NumPy, and ByteBuffer formats
- [Workspaces](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/workspaces.md): MemoryWorkspace API — configuration, policies, nested workspaces, scope panic, and lifecycle management
- [Random Number Generation](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/random.md): Generating random INDArrays, probability distributions, seeding, and reproducibility in ND4J
- [New Operations Reference](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/new-operations.md): Complete reference for ~130 new native operations — fused attention, KV cache, PEFT linear layers, normalization, positional encoding, quantization, SSM, MoE, and audio/signal processing
- [Backends](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-1.md): How ND4J's backend system works — SPI mechanism, backend selection, and the relationship between nd4j-native and nd4j-cuda
- [CPU (nd4j-native)](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-1/cpu.md): Setting up the nd4j-native CPU backend — Maven dependencies, AVX2/AVX512 optimizations, OpenBLAS, MKL, and multi-threading
- [CUDA (nd4j-cuda)](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-1/cuda.md): Setting up the nd4j-cuda GPU backend — CUDA versions, cuDNN integration, multi-GPU, and GPU memory management
- [Hardware Backends (1.0.0-rewrite)](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-1/hardware-backends.md): GPU, TPU, DSP, and CPU acceleration backends — CUDA, TPU (PJRT), Hexagon (QNN), ZLUDA, ARM ACL, Apple Accelerate, cuDNN, MPS, MLIR, and multi-backend dispatch
- [SameDiff](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-2.md): Automatic differentiation framework in ND4J — define-and-run computation graphs, comparison with MultiLayerNetwork and ComputationGraph
- [Variables](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-2/variables.md): SDVariable types — VARIABLE, CONSTANT, PLACEHOLDER, ARRAY — data types, naming, and type conversion
- [Operations](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-2/operations.md): Op namespaces — sd.math, sd.nn, sd.cnn, sd.rnn, sd.loss, sd.random — and SDVariable methods
- [Training](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-2/training.md): Training SameDiff models — TrainingConfig, fit(), listeners, loss curves, and evaluation
- [Execution and Inference](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-2/execution.md): Running SameDiff graphs — exec(), output(), batchOutput(), placeholders, and InferenceSession
- [Serialization](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-2/serialization.md): Saving and loading SameDiff graphs in FlatBuffers format
- [DSP Execution Engine](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/nd4j/overview-2/dsp.md): Complete guide to the Dynamic Shape Plan execution engine — compiled graph runtime with CUDA graph capture/replay, Triton/NVRTC/PTX JIT, 26-pass optimizer, and multi-backend dispatch
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/quickstart.md): End-to-end quickstart guide — from Maven setup to training an MNIST classifier in Deeplearning4j
- [Core Concepts](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/concepts.md): Architecture overview of Deeplearning4j — MultiLayerNetwork, ComputationGraph, the training pipeline, and DL4J's relationship to ND4J
- [Beginners Guide](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/beginners.md): Getting started with deep learning and Deeplearning4j — prerequisites, learning path, and recommended resources
- [Cheat Sheet](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/cheat-sheet.md): Quick reference cheat sheet for Deeplearning4j — common configurations, layer types, and API patterns
- [Examples Tour](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/examples-tour.md): Tour of the dl4j-examples repository — classification, regression, CNN, RNN, and advanced examples
- [Neural Networks](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork.md): The MultiLayerNetwork API — building, configuring, training, evaluating, and using sequential neural networks
- [ComputationGraph](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/computationgraph.md): The ComputationGraph API — multi-input, multi-output, skip connections, and arbitrary DAG topologies
- [Layers Reference](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/layers.md): Complete reference for all layer types in Deeplearning4j — Dense, Activation, Dropout, Embedding, BatchNormalization, and more
- [Convolutional Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/convolutional.md): CNN layers in Deeplearning4j — Conv1D/2D/3D, pooling, deconvolution, depthwise, separable, and upsampling layers
- [Recurrent Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/recurrent.md): RNN layers in Deeplearning4j — LSTM, GRU, Bidirectional wrapper, masking, TBPTT, and sequence data handling
- [Autoencoders](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/autoencoders.md): Autoencoder and Variational Autoencoder layers in Deeplearning4j — architecture, configuration, and training
- [Graph Vertices](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/vertices.md): Vertex types for ComputationGraph — Merge, ElementWise, Subset, Stack, Reshape, and custom vertices
- [Data Iterators](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/iterators.md): DataSetIterator implementations — built-in iterators, custom iterators, async loading, and data splitting
- [Listeners](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/listeners.md): Training listeners — ScoreIterationListener, PerformanceListener, EvaluativeListener, CheckpointListener, and custom listeners
- [Evaluation](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/evaluation.md): Detailed evaluation guide — classification metrics, regression metrics, ROC curves, calibration, and multi-output evaluation
- [Model Persistence](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/model-persistence.md): Saving and loading neural networks — ModelSerializer, saving normalizers, and model format details
- [Transfer Learning](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/transfer-learning.md): Transfer learning in Deeplearning4j — TransferLearning.Builder, FineTuneConfiguration, freezing layers, and modifying pretrained networks
- [Early Stopping](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/early-stopping.md): Early stopping in Deeplearning4j — EarlyStoppingConfiguration, termination conditions, score calculators, and model savers
- [Custom Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/custom-layer.md): Writing custom layers in Deeplearning4j — extending Layer, SameDiff-backed custom layers, and custom graph vertices
- [Visualization](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/multilayernetwork/visualization.md): Visualizing training progress — VertxUIServer, StatsListener, the training dashboard, and t-SNE visualization
- [NLP](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview.md): Natural language processing in Deeplearning4j — Word2Vec, Doc2Vec, tokenization, and text processing pipeline
- [Word2Vec](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview/word2vec.md): Word2Vec in Deeplearning4j — Skip-Gram, CBOW, training word vectors, and using pretrained embeddings
- [Doc2Vec](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview/doc2vec.md): Document vectors with ParagraphVectors (Doc2Vec) — training document embeddings and document similarity
- [Sentence Iterators](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview/sentence-iterator.md): Text input for NLP — SentenceIterator, BasicLineIterator, FileSentenceIterator, and custom iterators
- [Tokenization](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview/tokenization.md): Tokenizer factories in Deeplearning4j — DefaultTokenizerFactory, custom tokenizers, and preprocessors
- [Vocabulary Cache](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview/vocabulary-cache.md): VocabCache in Deeplearning4j — how vocabulary is stored, InMemoryLookupCache, and vocabulary management
- [Distributed Training](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-1.md): When and how to use distributed training — architecture overview, Spark integration, and ParallelWrapper
- [Spark How-To](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-1/spark-howto.md): Step-by-step guide to distributed training with Apache Spark — setup, data loading, training, and evaluation
- [Spark Data Pipelines](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-1/spark-data-howto.md): Loading and preprocessing data on Apache Spark for distributed DL4J training
- [Spark API Reference](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-1/spark-api-reference.md): API reference for SparkDl4jMultiLayer, SparkComputationGraph, and TrainingMaster
- [Parameter Server](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-1/parameter-server.md): Gradient sharing via the Aeron-based parameter server for distributed training
- [Technical Reference](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-1/technical-reference.md): Technical details of distributed training — Strom ASGD algorithm, mesh networking, and fault tolerance
- [ParallelWrapper (Multi-GPU)](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-1/parallel-wrapper.md): Multi-GPU training with ParallelWrapper and high-throughput inference with ParallelInference in DL4J 1.0.0-M2.1.
- [Model Zoo](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-2.md): Pretrained models in Deeplearning4j — available architectures, using pretrained weights, and transfer learning from zoo models
- [Available Models](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-2/models.md): Complete list of pretrained models — VGG, ResNet, AlexNet, LeNet, YOLO, UNet, and other architectures
- [Training UI](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-3.md): Web-based training visualization — VertxUIServer, StatsListener, training dashboard, and t-SNE visualization
- [Troubleshooting](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/troubleshooting.md): Common training problems and solutions — NaN loss, slow convergence, overfitting, memory errors, and debugging tips
- [Benchmarking](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/benchmarking.md): How to benchmark DL4J and ND4J — OpProfiler, performance metrics, and comparing configurations
- [Building from Source](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/build-from-source.md): How to build Deeplearning4j from source — prerequisites, Maven build, libnd4j compilation, and common build issues
- [Contributing](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/contributing.md): How to contribute to Deeplearning4j — development setup, code style, pull requests, and community guidelines
- [LLM & VLM Stack](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/overview-4.md): Complete guide to the samediff-llm generation pipeline, KV cache management, speculative decoding, continuous batching, tokenizers, evaluation framework, and model editing
- [PEFT & RL Alignment Training](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/deeplearning4j/peft-and-rl.md): Parameter-efficient fine-tuning (12 methods), reinforcement learning alignment (9 trainers), mixed-precision training, knowledge distillation, and dataset curation
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/overview.md): DataVec ETL framework — purpose, architecture, and the data pipeline from raw data to DataSet
- [Schema](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/schema.md): Defining data schemas — Schema, ColumnType, SequenceSchema, and schema inference
- [Record Readers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/readers.md): RecordReader implementations — CSV, JSON, image, regex, line, and custom readers
- [Transforms](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/transforms.md): TransformProcess — data transformations, column operations, type conversions, and sequences
- [Conditions](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/conditions.md): Conditional operations in DataVec — filtering and transforming data based on conditions
- [Filters](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/filters.md): Data filtering in DataVec — removing records based on conditions
- [Normalization](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/normalization.md): Data normalization in DataVec — normalizer implementations and serialization
- [Operations](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/operations.md): DataVec operations — calculators, reducers, and data analysis tools
- [Reductions](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/reductions.md): DataVec reduction operations — aggregating, grouping, and summarizing records and sequences
- [Analysis](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/analysis.md): DataVec data analysis tools — profiling datasets, detecting quality issues, and computing statistics locally and on Spark
- [Serialization](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/serialization.md): Data serialization in DataVec — saving and loading schemas, transform processes, and normalized data
- [Executors](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/executors.md): Running DataVec transform processes — LocalTransformExecutor and SparkTransformExecutor
- [Records and Writables](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/records.md): DataVec record format — Writable types, Record, and the data representation layer
- [Image Data](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/image.md): Loading and preprocessing image data — ImageRecordReader, NativeImageLoader, and image transforms
- [Apache Arrow](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/datavec/arrow.md): Apache Arrow integration in DataVec — ArrowRecordReader and zero-copy data exchange
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview.md): All model import paths in DL4J — Keras to DL4J, TF/ONNX to SameDiff, and direct inference runtimes
- [Keras Import](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1.md): Importing Keras models into Deeplearning4j — supported features, limitations, and getting started
- [Getting Started](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/getting-started.md): Step-by-step guide to importing Keras models — saving in Python, loading in Java, and running inference
- [Functional Model](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/functional-model.md): Importing Keras Functional API models as ComputationGraph
- [Sequential Model](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/sequential-model.md): Importing Keras Sequential models as MultiLayerNetwork
- [API Reference](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/model-import-api.md): KerasModelImport API — all import methods and configuration options
- [Supported Features](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/supported-features.md): Full support matrix for Keras model import — layers, activations, losses, and optimizers
- [Core Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-core.md): DL4J equivalents and API reference for Keras core layers — Dense, Flatten, Dropout, Reshape, Merge, Permute, and more.
- [Convolutional Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-convolutional.md): DL4J equivalents and API reference for Keras convolutional layers — Conv1D, Conv2D, Conv3D, SeparableConv2D, transposed convolutions, cropping, upsampling, and zero-padding.
- [Recurrent Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-recurrent.md): DL4J equivalents and API reference for Keras recurrent layers — SimpleRNN, LSTM, and associated utilities.
- [Pooling Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-pooling.md): DL4J equivalents and API reference for Keras pooling layers — MaxPooling, AveragePooling, and GlobalPooling in 1D, 2D, and 3D variants.
- [Normalization Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-normalization.md): DL4J equivalents and API reference for Keras normalization layers — BatchNormalization.
- [Embedding Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-embeddings.md): DL4J equivalents and API reference for Keras embedding layers — Embedding mapped to EmbeddingSequenceLayer.
- [Advanced Activations](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-advanced-activations.md): DL4J equivalents and API reference for Keras advanced activation layers — LeakyReLU, PReLU, ELU, and ThresholdedReLU.
- [Wrapper Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-wrappers.md): DL4J equivalents and API reference for Keras wrapper layers — Bidirectional and TimeDistributed.
- [Noise Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-noise.md): DL4J equivalents and API reference for Keras noise layers — GaussianNoise, GaussianDropout, and AlphaDropout.
- [Local Layers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/layers-local.md): DL4J equivalents and API reference for Keras locally connected layers — LocallyConnected1D and LocallyConnected2D.
- [Activations](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/activations.md): Keras to DL4J activation function mapping for model import
- [Optimizers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/optimizers.md): Keras to DL4J optimizer mapping for model import
- [Losses](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/losses.md): Keras to DL4J loss function mapping for model import
- [Initializers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/initializers.md): Mapping of Keras weight initializers to DL4J WeightInit implementations for model import in Eclipse Deeplearning4j 1.0.0-M2.1.
- [Constraints](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/constraints.md): Mapping of Keras weight constraints to DL4J LayerConstraint implementations for model import in Eclipse Deeplearning4j 1.0.0-M2.1.
- [Regularizers](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/regularizers.md): Mapping of Keras regularizers to DL4J regularization for model import in Eclipse Deeplearning4j 1.0.0-M2.1.
- [Backend](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-1/backend.md): DL4J Keras model import is backend-agnostic — models trained with TensorFlow, Theano, or CNTK backends can all be imported into Eclipse Deeplearning4j 1.0.0-M2.1.
- [SameDiff Import](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-2.md): Importing TensorFlow and ONNX models into SameDiff — architecture, supported ops, and usage
- [TensorFlow Import](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-2/tensorflow.md): Importing TensorFlow frozen graphs and SavedModels into SameDiff
- [ONNX Import](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-2/onnx.md): Importing ONNX models into SameDiff
- [ONNX Import & Export (Expanded)](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-2/onnx-expanded.md): ~120 new ONNX op implementations including Microsoft LLM contrib ops, ONNX ML domain classifiers, quantized inference ops, and bidirectional SameDiff-to-ONNX export
- [GGML/GGUF Import](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-3.md): Import quantized LLMs from GGUF files — architecture handlers, quantization codecs, adaptive quantization, round-trip export, and pipeline modules
- [ONNX Runtime](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-4.md): Direct ONNX model inference via ONNX Runtime 1.10 — no conversion to SameDiff required
- [TensorFlow](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-5.md): Running TensorFlow frozen graphs directly via JavaCPP TF bindings
- [TensorFlow Lite](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-5/tensorflow-lite.md): TensorFlow Lite 2.8 inference for mobile and edge deployment
- [Apache TVM](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview-6.md): Apache TVM 0.8 integration for optimized model inference
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/python4j/overview.md): Embedding CPython in JVM applications — what Python4J is, use cases, and architecture
- [Getting Started](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/python4j/getting-started.md): Setting up Python4J — Maven dependencies, executing Python code, and variable I/O
- [NumPy Bridge](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/python4j/numpy-bridge.md): Zero-copy data exchange between INDArray and numpy.ndarray via python4j-numpy
- [Advanced Usage](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/python4j/advanced.md): Advanced Python4J features — GIL management, context managers, garbage collection, and subprocess mode
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/omnihub/overview.md): OmniHub model registry — downloading and using pretrained models from the DL4J ecosystem
- [Usage](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/omnihub/usage.md): Downloading and loading models from OmniHub — OmniHubUtils API
- [Available Models](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/omnihub/available-models.md): Pretrained models available through OmniHub — DL4J and SameDiff model catalog
- [Maven Setup](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/configuration/maven.md): Maven dependencies for Deeplearning4j — BOM, backend selection, platform classifiers, and version management
- [Build Tools (Gradle, SBT)](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/configuration/build-tools.md): Configuring Deeplearning4j with Gradle, SBT, and other build tools
- [GPU and CPU Setup](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/configuration/gpu-cpu.md): Configuring GPU and CPU backends — CUDA setup, multi-GPU, CPU optimizations, and backend switching
- [cuDNN](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/configuration/cudnn.md): cuDNN integration — installation, configuration, and performance benefits
- [Memory Configuration](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/configuration/memory.md): JVM memory flags, off-heap configuration, and memory management for ND4J and DL4J
- [Workspaces](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/configuration/workspaces.md): WorkspaceMode configuration for training and inference memory management
- [Performance Debugging](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/configuration/performance-debugging.md): Diagnosing and resolving performance issues in DL4J and ND4J — profiling, OpProfiler, ETL bottlenecks, GC overhead, and backend verification.
- [Snapshots and Daily Builds](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/configuration/snapshots.md): Using nightly snapshot builds of Eclipse Deeplearning4j 1.0.0-M2.1 — repository configuration, version identifiers, Maven and Gradle setup.
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/arbiter/overview.md): Arbiter hyperparameter optimization for DL4J — architecture, search strategies, configuration, and usage.
- [Layer Spaces](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/arbiter/layer-spaces.md): Layer parameter spaces in Arbiter — configuration of per-layer hyperparameter search spaces for DL4J neural networks.
- [Parameter Spaces](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/arbiter/parameter-spaces.md): Parameter space types for Arbiter hyperparameter search — continuous, integer, discrete, boolean, fixed, and composite spaces.
- [Visualization](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/arbiter/visualization.md): Monitoring Arbiter hyperparameter optimization runs with ArbiterStatusListener and the UIServer.
- [1.0.0-rewrite](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/release-notes/1.0.0-rewrite.md): Release notes for the Deeplearning4j 1.0.0-rewrite: a transitional release introducing the DSP execution engine, LLM/VLM stack, GGUF import, PEFT/RL alignment, and massive hardware expansion. Namespac

## EN 1.0.0-M1.1

- [Deeplearning4j Suite Overview](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/master.md): Introduction to core Deeplearning4j concepts.
- [Release Notes](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes.md)
- [1.0.0-M1.1](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-m1.1.md)
- [1.0.0-M1](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-m1.md)
- [1.0.0-beta7](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-beta7.md)
- [1.0.0-beta6](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-beta6.md)
- [1.0.0-beta5](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-beta5.md)
- [1.0.0-beta4](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-beta4.md)
- [1.0.0-beta3](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-beta3.md)
- [1.0.0-beta2](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-beta2.md)
- [1.0.0-beta](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-beta.md)
- [1.0.0-alpha](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/1.0.0-alpha.md)
- [0.9.1](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.9.1.md)
- [0.9.0](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.9.0.md)
- [0.8.0](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.8.0.md)
- [0.7.2](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.7.2.md)
- [0.7.1](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.7.1.md)
- [0.7.0](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.7.0.md)
- [0.6.0](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.6.0.md)
- [0.5.0](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.5.0.md)
- [0.4.0](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/release-notes/0.4.0.md)
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/tutorials.md)
- [Beginners](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/tutorials/beginners.md): Road map for beginners new to deep learning.
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/tutorials/quickstart.md): Quickstart for Java using Maven
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides.md)
- [Import in to your favorite IDE](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/import-in-to-your-favorite-ide.md)
- [Contribute](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/contribute.md): How to contribute to the Eclipse Deeplearning4j source code.
- [Eclipse Contributors](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/contribute/eclipse-contributors.md): IP/Copyright requirements for Eclipse Foundation Projects
- [Developer Docs](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/developer-docs.md)
- [Github Actions/Build Infra](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/developer-docs/github-actions-build-infra.md): Github actions Configuration Overview
- [Javacpp](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/developer-docs/javacpp.md): DL4J and Javacpp
- [Release](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/developer-docs/release.md): How to conduct a release to Maven Central
- [Testing](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/developer-docs/testing.md): How to conduct a release to Maven Central
- [Build From Source](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/build-from-source.md): Instructions to build all DL4J libraries from source.
- [Benchmark](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/benchmark.md): General guidelines for benchmarking in DL4J and ND4J.
- [Beginners](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/how-to-guides/beginners.md): Road map for beginners new to deep learning.
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/reference.md)
- [Examples Tour](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/reference/examples-tour.md): Brief tour of available examples in DL4J.
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation.md)
- [The core workflow](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/the-core-workflow.md): An overview of the core deeplearning4j workflow
- [Configuration](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/configuration.md)
- [Backends](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/configuration/backends.md): Hardware setup for Eclipse Deeplearning4j, including GPUs and CUDA.
- [Performance Issues](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/configuration/backends/performance-issues.md): How to Debug Performance Issues
- [CPU](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/configuration/backends/cpu.md): CPU and AVX support in ND4J/Deeplearning4j
- [Cudnn](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/configuration/backends/cudnn.md): Using the NVIDIA cuDNN library with DL4J.
- [Memory](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/configuration/memory.md): Setting available Memory/RAM for a DL4J application
- [Workspaces](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/configuration/memory/workspaces.md): Workspaces are an efficient model for memory paging in DL4J.
- [Build Tools](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/build-tools.md): Configure the build tools for Deeplearning4j.
- [Snapshots](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/snapshots.md): Using daily builds for access to latest Eclipse Deeplearning4j features.
- [Maven](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/multi-project/explanation/maven.md): Configure the Maven build tool for Deeplearning4j.
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/tutorials.md)
- [Quick Start](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/tutorials/quick-start.md): Quickstart for Java using Maven
- [Language Processing](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/tutorials/language-processing.md): Overview of language processing in DL4J
- [Doc2Vec](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/tutorials/language-processing/doc2vec.md): Doc2Vec and arbitrary documents for language processing in DL4J.
- [Sentence Iterator](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/tutorials/language-processing/sentence-iterator.md): Iteration of words, documents, and sentences for language processing in DL4J.
- [Tokenization](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/tutorials/language-processing/tokenization.md): Breaking text into individual words for language processing in DL4J.
- [Vocabulary Cache](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/tutorials/language-processing/vocabulary-cache.md): Mechanism for handling general NLP tasks in DL4J.
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides.md)
- [Custom Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/custom-layers.md): Extend DL4J functionality for custom layers.
- [Keras Import](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import.md): Overview of model import.
- [Functional Models](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/functional-models.md): Importing the functional model.
- [Sequential Models](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/sequential-models.md): Importing the functional model.
- [Custom Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/custom-layers.md): How to implement custom Keras layers for import in Deeplearning4J.
- [Keras Import API Overview](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference.md): Keras model import API
- [Advanced Activations](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/advanced-activations.md)
- [Convolutional Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/convolutional-layers.md)
- [Core Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/core-layers.md)
- [Embedding Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/embedding-layers.md)
- [Local Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/local-layers.md)
- [Noise Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/noise-layers.md)
- [Normalization Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/normalization-layers.md)
- [Pooling Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/pooling-layers.md)
- [Recurrent Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/recurrent-layers.md)
- [Wrapper Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/api-reference/wrapper-layers.md)
- [Supported Features Overview](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/supported-features.md): Supported Keras features.
- [Activations](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/supported-features/activations.md): Supported Keras activations.
- [Constraints](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/supported-features/constraints.md): Supported Keras constraints.
- [Initializers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/supported-features/initializers.md): Supported Keras weight initializers.
- [Losses](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/supported-features/losses.md): Supported Keras loss functions.
- [Optimizers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/supported-features/optimizers.md): Supported Keras optimizers
- [Regularizers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/keras-import/supported-features/regularizers.md): Supported Keras regularizers.
- [Tuning and Training](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/tuning-and-training.md)
- [Visualization](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/tuning-and-training/visualization.md): How to visualize, monitor and debug neural network learning.
- [Troubleshooting Training](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/tuning-and-training/troubleshooting-training.md): Understanding common errors like NaNs and tuning hyperparameters.
- [Early Stopping](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/tuning-and-training/early-stopping.md): Terminate a training session given certain conditions.
- [Evaluation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/tuning-and-training/evaluation.md): Tools and classes for evaluating neural network performance
- [Transfer Learning](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/how-to-guides/tuning-and-training/transfer-learning.md)
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference.md)
- [Model Zoo](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/model-zoo.md): Prebuilt model architectures and weights for out-of-the-box application.
- [Zoo Models](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/model-zoo/zoo-models.md)
- [Activations](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/activations.md): Special algorithms for gradient descent.
- [Auto Encoders](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/auto-encoders.md)
- [Computation Graph](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/computation-graph.md): How to build complex networks with DL4J computation graph.
- [Convolutional Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/convolutional-layers.md): Also known as CNN.
- [DataSet Iterators](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/dataset-iterators.md): Data iteration tools for loading into neural networks.
- [Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/layers.md): Supported neural network layers.
- [Model Listeners](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/model-listeners.md): Adding hooks and listeners on DL4J models.
- [Saving and Loading Models](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/saving-and-loading-models.md): Saving and loading of neural networks.
- [Multi Layer Network](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/multi-layer-network.md): Simple and sequential network configuration.
- [Recurrent Layers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/recurrent-layers.md): Recurrent Neural Network (RNN) implementations in DL4J.
- [Updaters/Optimizers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/updaters-optimizers.md): Special algorithms for gradient descent.
- [Vertices](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/vertices.md): Computation graph nodes for advanced configuration.
- [Word2vec/Glove/Doc2Vec](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/reference/word2vec-glove-doc2vec.md): Neural word embeddings for NLP in DL4J.
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/deeplearning4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/tutorials.md)
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/tutorials/overview.md): Overview of the vectorization and ETL library for DL4J.
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/how-to-guides.md)
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference.md)
- [Analysis](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/analysis.md): Gather statistics on datasets.
- [Conditions](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/conditions.md)
- [Executors](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/executors.md): Execute ETL and vectorization in a local instance.
- [Filters](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/filters.md): Selection of data using conditions.
- [Normalization](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/normalization.md)
- [Operations](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/operations.md): Implementations for advanced transformation.
- [Transforms](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/transforms.md): Data wrangling and mapping from one schema to another.
- [Readers](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/readers.md): Read individual records from different formats.
- [Records](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/records.md): How to use data records in DataVec.
- [Reductions](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/reductions.md)
- [Schemas](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/schemas.md): Schemas for datasets and transformation.
- [Serialization](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/serialization.md): Data wrangling and mapping from one schema to another.
- [Visualization](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/reference/visualization.md)
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/datavec/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/tutorials.md)
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/tutorials/quickstart.md): Comprehensive programming guide for ND4J. This user guide is designed to explain (and provide examples for) the main functionality in ND4J.
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/how-to-guides.md)
- [Other Framework Interop](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/how-to-guides/other-framework-interop.md)
- [Tensorflow](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/how-to-guides/other-framework-interop/tensorflow.md): Tensorflow interop Key features and brief samples.
- [TVM](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/how-to-guides/other-framework-interop/tvm.md): TVM Key features and brief samples.
- [Onnx](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/how-to-guides/other-framework-interop/onnx.md): Onnx interop Key features and brief samples.
- [Matrix Manipulation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/how-to-guides/matrix-manipulation.md)
- [Element wise Operations](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/how-to-guides/element-wise-operations.md)
- [Basics](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/how-to-guides/basics.md): Elementwise Operations And Basic Usage
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/reference.md): Comprehensive programming guide for ND4J. This user guide is designed to explain (and provide examples for) the main functionality in ND4J.
- [Op Descriptor Format](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/reference/op-descriptor-format.md): Model import framework overview and examples
- [Tensor](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/reference/tensor.md)
- [Syntax](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/reference/syntax.md)
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/nd4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/tutorials.md)
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/tutorials/quickstart.md): Samediff Quickstart
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/how-to-guides.md)
- [Importing Tensorflow](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/how-to-guides/importing-tensorflow.md)
- [Adding Operations](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/how-to-guides/adding-operations.md): How to add differential functions and other ops to SameDiff graph.
- [codegen](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/how-to-guides/adding-operations/codegen.md)
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference.md)
- [Operation Namespaces](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces.md)
- [Base Operations](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/base-operations.md)
- [Bitwise](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/bitwise.md)
- [CNN](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/cnn.md)
- [Image](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/image.md)
- [LinAlg](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/linalg.md)
- [Loss](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/loss.md)
- [Math](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/math.md)
- [NN](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/nn.md)
- [Random](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/random.md)
- [RNN](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/operation-namespaces/rnn.md)
- [Variables](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/reference/variables.md): What types of variables are used in SameDiff, their properties and how to switch these types.
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/explanation.md)
- [Model Import Framework](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/samediff/explanation/model-import-framework.md): Model import framework overview and examples
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/how-to-guides.md)
- [Building on Windows](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/how-to-guides/building-on-windows.md)
- [Building for raspberry pi or Jetson Nano](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/how-to-guides/building-for-raspberry-pi-or-jetson-nano.md)
- [Building on ios](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/how-to-guides/building-on-ios.md)
- [How to Add Operations](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/how-to-guides/how-to-add-operations.md)
- [How to Setup CLion](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/how-to-guides/how-to-setup-clion.md): Setting up clion for modifying the libnd4j code base
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/reference.md)
- [Understanding graph execution](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/reference/understanding-graph-execution.md)
- [Overview of working with libnd4j](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/reference/tutorials.md)
- [Helpers Overview (CUDNN, OneDNN,Armcompute)](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/reference/helpers-overview-cudnn-onednn-armcompute.md)
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/libnd4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/tutorials.md)
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/tutorials/quickstart.md): Python4j Key features and brief samples.
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/how-to-guides.md)
- [Write Python Script](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/how-to-guides/write-python-script.md): How to write a python script for python4j
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/reference.md)
- [Python Types](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/reference/python-types.md): Python4j and python types
- [Python Path](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/reference/python-path.md): Python4j and custom python path
- [Garbage Collection](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/reference/garbage-collection.md): Python4j Garbage Collection and interactions with the JVM
- [Python Script Execution](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/reference/python-script-execution.md): Python4j Python Script Execution
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/python4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/rl4j/tutorials.md)
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/rl4j/how-to-guides.md)
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/rl4j/reference.md)
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/rl4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/tutorials.md)
- [DL4J on Spark Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/tutorials/dl4j-on-spark-quickstart.md): Deeplearning4j on Spark: Introduction
- [How To Guides](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/how-to-guides.md)
- [How To](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/how-to-guides/how-to.md): Deeplearning4j on Spark: How To Build Data Pipelines
- [Data How To](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/how-to-guides/data-how-to-1.md): Deeplearning4j on Spark: How To Guides
- [Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/reference.md)
- [Parameter Server](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/reference/parameter-server.md): Deeplearning4j supports fast distributed training with Spark and a parameter server.
- [Technical Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/reference/technical-reference.md): Deeplearning4j on Spark: Technical Explanation
- [Explanation](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/explanation.md)
- [Spark API Reference](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/spark/explanation/spark-api-reference.md): This page provides the API reference for key classes required to do distributed training with DL4J on Spark. Make sure you have read the introduction guide for deeplearning4j Spark training.
- [codegen](https://deeplearning4j.konduit.ai/en-1.0.0-m1.1/codegen.md)

## ZH 1.0.0-beta6

- [核心概念](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/master.md): 本页将向您全面介绍如何运行DL4J示例，启动您自己的项目。建议您加入我们的QQ交流群。您可以在QQ上请求帮助、提出反馈，不过也请您在遇到问题时先参考本指南中已列出的疑难解答。如果您是初次接触深度学习，我们准备了一份初学者学习计划，包括课程、阅读材料和其他资源的链接。
- [快速入门](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/kuai-su-ru-men.md): java与maven快速入门
- [速查表](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/su-cha-biao.md): 提供Eclipse Deeplearning4j里通用的功能与代码片段
- [示例教程](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/shi-li-jiao-cheng.md): 简要介绍DL4J中的可用示例。
- [初学者](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/chu-xue-zhe.md): 新手深度学习的路线图。
- [Eclipse贡献者](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/eclipse-gong-xian-zhe.md): Eclipse基金会项目的IP/版权要求
- [从源码构建](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/cong-yuan-ma-gou-jian.md): 从源代码构建所有DL4J库的说明。
- [贡献](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/gong-xian.md): 如何贡献Eclipse Deeplearning4j源代码。
- [基准测试准则](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/ji-zhun-ce-shi-zhun-ze.md): DL4J和ND4J中基准通用准则。
- [关于](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/guan-yu.md): 顶尖的JVM深度学习框架：Eclipse Deeplearning4j的事实与介绍
- [发行说明](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/kai-shi/fa-xing-shuo-ming.md): Eclipse Deeplearning4j的每个版本都有新的变化。
- [GPU/CPU设置](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/pei-zhi/gpucpu-she-zhi.md): Eclipse Deeplearning4J的硬件设置，包括GPU和CUDA。
- [CPU 与 AVX](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/pei-zhi/cpu-yu-avx.md): ND4J/Deeplearning4j中的 CPU与AVX支持
- [内存管理](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/pei-zhi/nei-cun-guan-li.md): 为DL4J应用程序设置可用内存/RAM
- [Maven](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/pei-zhi/maven.md): 为Deeplearning4j配置Maven构建工具。
- [SBT/Gradle和其它构建工具](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/pei-zhi/sbtgradle-he-qi-ta-gou-jian-gong-ju.md): 为Deeplearning4j配置构建工具。
- [cuDNN](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/pei-zhi/cudnn.md): 在 DL4J中使用NVIDIA cuDNN 库
- [快照](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/pei-zhi/kuai-zhao.md): 使用每日版本访问最新的Eclipse Deeplearning4J功能。
- [内存工作间](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/pei-zhi/nei-cun-gong-zuo-jian.md): 在DL4J中，工作间是一种有效的内存分页模型。
- [快速入门](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/nd4j/kuai-su-ru-men.md): ND4J的主要功能和简要示例。
- [概述](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/nd4j/gai-shu.md): ND4J综合编程指南。
- [变量](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/samediff/bian-liang.md): SameDiff中使用的变量类型、它们的属性以及如何切换这些类型。
- [操作](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/samediff/cao-zuo.md): “SameDiff”中有哪些操作以及如何使用它们
- [添加操作](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/samediff/tian-jia-cao-zuo.md): 如何在SameDiff图中添加微分函数和其他操作。
- [故障排查](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/tiao-you-yu-xun-lian/gu-zhang-pai-cha.md): 了解常见错误如NaNs和调整超参数。
- [可视化](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/tiao-you-yu-xun-lian/ke-shi-hua.md): 如何可视化、监控和调试神经网络学习。
- [评估](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/tiao-you-yu-xun-lian/ping-gu.md): 评估神经网络性能的工具和类
- [迁移学习](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/tiao-you-yu-xun-lian/qian-yi-xue-xi.md)
- [早停](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/tiao-you-yu-xun-lian/zao-ting.md): 在特定条件下终止训练。
- [T-SNE数据可视化](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/tiao-you-yu-xun-lian/tsne-shu-ju-ke-shi-hua.md): 高维数据的t-SNE可视化。
- [介绍与入门](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/fen-bu-shi-shen-du-xue-xi/jie-shao-yu-ru-men.md): Spark上的DL4J: 介绍
- [在Spark上使用DL4J：操作指南](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/fen-bu-shi-shen-du-xue-xi/zai-spark-shang-shi-yong-dl4j-cao-zuo-zhi-nan.md)
- [技术说明](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/fen-bu-shi-shen-du-xue-xi/ji-shu-shuo-ming.md): Spark上的DL4J: 技术说明
- [Spark数据管道指南](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/fen-bu-shi-shen-du-xue-xi/spark-shu-ju-guan-dao-zhi-nan.md): Spark上的DL4J: 如何构建数据管道
- [API参考](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/fen-bu-shi-shen-du-xue-xi/api-can-kao.md): 本页提供了在Spark上使用DL4J进行分布式训练所需的关键类的API参考。确保您已经阅读了深入DL4J Spark训练入门指南。
- [参数服务器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/fen-bu-shi-shen-du-xue-xi/can-shu-fu-wu-qi.md): DL4J支持使用Spark和参数服务器进行快速分布式训练。
- [概述](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/gai-shu.md): 模型导入概述
- [入门](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/ru-men.md): 模型导入入门。
- [支持功能](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/zhi-chi-gong-neng.md): 支持的Keras功能。
- [正则化器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/zhi-chi-gong-neng/zheng-ze-hua-qi.md): 支持的Keras正则化器
- [损失](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/zhi-chi-gong-neng/sun-shi.md): 支持的损失函数
- [初始化器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/zhi-chi-gong-neng/chu-shi-hua-qi.md): 支持的Keras权重初始化器
- [约束](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/zhi-chi-gong-neng/yue-shu.md): 已支持的Keras约束。
- [激活](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/zhi-chi-gong-neng/ji-huo.md): 支持的Keras激活。
- [优化器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/zhi-chi-gong-neng/you-hua-qi.md): 已支持的Keras优化器
- [Functional模型](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/functional-mo-xing.md): 导入functional模型
- [Sequential模型](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/keras-dao-ru/sequential-mo-xing.md): 导入functional模型。
- [概述](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/arbiter/gai-shu.md): 超参数优化中使用Arbiter的介绍。
- [层空间](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/arbiter/ceng-kong-jian.md)
- [参数空间](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/arbiter/can-shu-kong-jian.md)
- [概述](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/gai-shu.md): DL4J向量化和ETL库概述。
- [记录](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/ji-lu.md): 在DataVec中如何使用数据记录
- [概要](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/gai-yao.md): 数据集和转换概要
- [序列化](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/xu-lie-hua.md): 从一个概要到另一个概要的数据整理和映射。
- [转换](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/zhuan-huan.md)
- [分析](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/fen-xi.md): 收集数据集的统计信息。
- [读取器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/du-qu-qi.md): 阅读不同格式的单独记录。
- [执行器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/zhi-hang-qi.md): 在本地实例中执行ETL和向量化。
- [过滤器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/guo-lv-qi.md): 数据使用条件的选择。
- [运算](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/datavec/yun-suan.md): 高级转换的实现。
- [概述](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yu-yan-chu-li/gai-shu.md): DL4J语言处理概述
- [Word2Vec](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yu-yan-chu-li/word2vec.md): DL4J中NLP神经词嵌入
- [Doc2Vec](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yu-yan-chu-li/doc2vec.md): 在DL4J中用于语言处理的Doc2Vec和任意文档。
- [SentenceIterator](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yu-yan-chu-li/sentenceiterator.md): DL4J中用于语言处理的单词、文档和句子的迭代。
- [Tokenization](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yu-yan-chu-li/tokenization.md): 在DL4J中把文本分解成单个单词进行语言处理。
- [Vocabulary Cache](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yu-yan-chu-li/vocabulary-cache.md): DL4J中处理一般NLP任务的机制。
- [计算图](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/ji-suan-tu.md): 如何用DL4J计算图构造复杂网络。
- [多层网络](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/duo-ceng-wang-luo.md): 简单和序列网络配置。
- [循环神经网络](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/xun-huan-shen-jing-wang-luo.md): 循环神经网络在DL4J中的实现。
- [层](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/ceng.md): 已支持的神经网络层
- [顶点](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/ding-dian.md): 高级配置的计算图节点。
- [迭代器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/die-dai-qi.md): 用于加载到神经网络的数据迭代工具。
- [监听器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/jian-ting-qi.md): 在DL4J模型上添加钩子和监听器。
- [自定义层](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/zi-ding-yi-ceng.md): 为自定义层扩展DL4J功能。
- [模型持久化](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/mo-xing-chi-jiu-hua.md): 神经网络的存储与加载。
- [动物园用法](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/dong-wu-yuan-yong-fa.md): 为开箱即用应用程序预先构建的模型架构和权重。
- [激活](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/ji-huo.md): 梯度下降的特殊算法。
- [更新器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/mo-xing/geng-xin-qi.md): 梯度下降的特殊算法。
- [Android概述](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yi-dong-duan/android-gai-shu.md): 在Android应用中使用深度学习和神经网络
- [Android先决条件](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yi-dong-duan/android-xian-jue-tiao-jian.md): 为DL4J设置和配置Android Studio。
- [Android分类器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yi-dong-duan/android-fen-lei-qi.md): 如何使用Eclipse Deeplearning4j在Android上创建IRIS分类器。
- [Android图片分类器](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/yi-dong-duan/android-tu-pian-fen-lei-qi.md): 如何使用Eclipse Deeplearning4j创建Android图像分类应用程序。
- [FAQ](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/faq.md): 关于Eclipse DeepLearning4J、深度学习和人工智能的常见问题。
- [新闻](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/xin-wen.md): Eclipse DeepLearning4J上的新闻文章列表。
- [支持](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/zhi-chi.md): Deeplearning4j的开源和企业支持。
- [为什么要深度学习？](https://deeplearning4j.konduit.ai/zhong-wen-v1.0.0/wei-shen-me-yao-shen-du-xue-xi.md): 与其他算法相比，何时选择深度学习。

## EN 1.0.0-M2

- [Deeplearning4j Suite Overview](https://deeplearning4j.konduit.ai/1.0.0-m2/master.md): Introduction to core Deeplearning4j concepts.
- [Release Notes](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes.md)
- [1.0.0-M2](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-m2.md)
- [1.0.0-M1.1](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-m1.1.md)
- [1.0.0-M1](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-m1.md)
- [1.0.0-beta7](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-beta7.md)
- [1.0.0-beta6](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-beta6.md)
- [1.0.0-beta5](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-beta5.md)
- [1.0.0-beta4](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-beta4.md)
- [1.0.0-beta3](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-beta3.md)
- [1.0.0-beta2](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-beta2.md)
- [1.0.0-beta](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-beta.md)
- [1.0.0-alpha](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.0.0-alpha.md)
- [0.9.1](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.9.1.md)
- [0.9.0](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.9.0.md)
- [0.8.0](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.8.0.md)
- [0.7.2](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.7.2.md)
- [0.7.1](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.7.1.md)
- [0.7.0](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.7.0.md)
- [0.6.0](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.6.0.md)
- [0.5.0](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.5.0.md)
- [0.4.0](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/0.4.0.md)
- [1.00-M2.2](https://deeplearning4j.konduit.ai/1.0.0-m2/release-notes/1.00-m2.2.md)
- [Tutorials](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/tutorials.md)
- [Beginners](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/tutorials/beginners.md): Road map for beginners new to deep learning.
- [Quickstart](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/tutorials/quickstart.md): Quickstart for Java using Maven
- [How To Guides](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides.md)
- [Import in to your favorite IDE](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/import-in-to-your-favorite-ide.md)
- [Contribute](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/contribute.md): How to contribute to the Eclipse Deeplearning4j source code.
- [Eclipse Contributors](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/contribute/eclipse-contributors.md): IP/Copyright requirements for Eclipse Foundation Projects
- [Developer Docs](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/developer-docs.md)
- [Github Actions/Build Infra](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/developer-docs/github-actions-build-infra.md): Github actions Configuration Overview
- [Javacpp](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/developer-docs/javacpp.md): DL4J and Javacpp
- [Release](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/developer-docs/release.md): How to conduct a release to Maven Central
- [Testing](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/developer-docs/testing.md): How to conduct a release to Maven Central
- [Build From Source](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/build-from-source.md): Instructions to build all DL4J libraries from source.
- [Benchmark](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/benchmark.md): General guidelines for benchmarking in DL4J and ND4J.
- [Beginners](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/how-to-guides/beginners.md): Road map for beginners new to deep learning.
- [Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/reference.md)
- [Examples Tour](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/reference/examples-tour.md): Brief tour of available examples in DL4J.
- [Explanation](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation.md)
- [The core workflow](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/the-core-workflow.md): An overview of the core deeplearning4j workflow
- [Configuration](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/configuration.md)
- [Backends](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/configuration/backends.md): Hardware setup for Eclipse Deeplearning4j, including GPUs and CUDA.
- [Performance Issues](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/configuration/backends/performance-issues.md): How to Debug Performance Issues
- [CPU](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/configuration/backends/cpu.md): CPU and AVX support in ND4J/Deeplearning4j
- [Cudnn](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/configuration/backends/cudnn.md): Using the NVIDIA cuDNN library with DL4J.
- [Memory](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/configuration/memory.md): Setting available Memory/RAM for a DL4J application
- [Workspaces](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/configuration/memory/workspaces.md): Workspaces are an efficient model for memory paging in DL4J.
- [Build Tools](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/build-tools.md): Configure the build tools for Deeplearning4j.
- [Snapshots](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/snapshots.md): Using daily builds for access to latest Eclipse Deeplearning4j features.
- [Maven](https://deeplearning4j.konduit.ai/1.0.0-m2/multi-project/explanation/maven.md): Configure the Maven build tool for Deeplearning4j.
- [Tutorials](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/tutorials.md)
- [Quick Start](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/tutorials/quick-start.md): Quickstart for Java using Maven
- [Language Processing](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/tutorials/language-processing.md): Overview of language processing in DL4J
- [Doc2Vec](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/tutorials/language-processing/doc2vec.md): Doc2Vec and arbitrary documents for language processing in DL4J.
- [Sentence Iterator](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/tutorials/language-processing/sentence-iterator.md): Iteration of words, documents, and sentences for language processing in DL4J.
- [Tokenization](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/tutorials/language-processing/tokenization.md): Breaking text into individual words for language processing in DL4J.
- [Vocabulary Cache](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/tutorials/language-processing/vocabulary-cache.md): Mechanism for handling general NLP tasks in DL4J.
- [How To Guides](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides.md)
- [Custom Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/custom-layers.md): Extend DL4J functionality for custom layers.
- [Keras Import](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import.md): Overview of model import.
- [Functional Models](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/functional-models.md): Importing the functional model.
- [Sequential Models](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/sequential-models.md): Importing the functional model.
- [Custom Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/custom-layers.md): How to implement custom Keras layers for import in Deeplearning4J.
- [Keras Import API Overview](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference.md): Keras model import API
- [Advanced Activations](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/advanced-activations.md)
- [Convolutional Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/convolutional-layers.md)
- [Core Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/core-layers.md)
- [Embedding Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/embedding-layers.md)
- [Local Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/local-layers.md)
- [Noise Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/noise-layers.md)
- [Normalization Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/normalization-layers.md)
- [Pooling Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/pooling-layers.md)
- [Recurrent Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/recurrent-layers.md)
- [Wrapper Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/api-reference/wrapper-layers.md)
- [Supported Features Overview](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/supported-features.md): Supported Keras features.
- [Activations](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/supported-features/activations.md): Supported Keras activations.
- [Constraints](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/supported-features/constraints.md): Supported Keras constraints.
- [Initializers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/supported-features/initializers.md): Supported Keras weight initializers.
- [Losses](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/supported-features/losses.md): Supported Keras loss functions.
- [Optimizers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/supported-features/optimizers.md): Supported Keras optimizers
- [Regularizers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/keras-import/supported-features/regularizers.md): Supported Keras regularizers.
- [Tuning and Training](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/tuning-and-training.md)
- [Visualization](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/tuning-and-training/visualization.md): How to visualize, monitor and debug neural network learning.
- [Troubleshooting Training](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/tuning-and-training/troubleshooting-training.md): Understanding common errors like NaNs and tuning hyperparameters.
- [Early Stopping](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/tuning-and-training/early-stopping.md): Terminate a training session given certain conditions.
- [Evaluation](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/tuning-and-training/evaluation.md): Tools and classes for evaluating neural network performance
- [Transfer Learning](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/how-to-guides/tuning-and-training/transfer-learning.md)
- [Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference.md)
- [Model Zoo](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/model-zoo.md): Prebuilt model architectures and weights for out-of-the-box application.
- [Zoo Models](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/model-zoo/zoo-models.md)
- [Activations](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/activations.md): Special algorithms for gradient descent.
- [Auto Encoders](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/auto-encoders.md)
- [Computation Graph](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/computation-graph.md): How to build complex networks with DL4J computation graph.
- [Convolutional Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/convolutional-layers.md): Also known as CNN.
- [DataSet Iterators](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/dataset-iterators.md): Data iteration tools for loading into neural networks.
- [Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/layers.md): Supported neural network layers.
- [Model Listeners](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/model-listeners.md): Adding hooks and listeners on DL4J models.
- [Saving and Loading Models](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/saving-and-loading-models.md): Saving and loading of neural networks.
- [Multi Layer Network](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/multi-layer-network.md): Simple and sequential network configuration.
- [Recurrent Layers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/recurrent-layers.md): Recurrent Neural Network (RNN) implementations in DL4J.
- [Updaters/Optimizers](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/updaters-optimizers.md): Special algorithms for gradient descent.
- [Vertices](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/vertices.md): Computation graph nodes for advanced configuration.
- [Word2vec/Glove/Doc2Vec](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/reference/word2vec-glove-doc2vec.md): Neural word embeddings for NLP in DL4J.
- [Explanation](https://deeplearning4j.konduit.ai/1.0.0-m2/deeplearning4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/tutorials.md)
- [Overview](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/tutorials/overview.md): Overview of the vectorization and ETL library for DL4J.
- [How To Guides](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/how-to-guides.md)
- [Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference.md)
- [Analysis](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/analysis.md): Gather statistics on datasets.
- [Conditions](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/conditions.md)
- [Executors](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/executors.md): Execute ETL and vectorization in a local instance.
- [Filters](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/filters.md): Selection of data using conditions.
- [Normalization](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/normalization.md)
- [Operations](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/operations.md): Implementations for advanced transformation.
- [Transforms](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/transforms.md): Data wrangling and mapping from one schema to another.
- [Readers](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/readers.md): Read individual records from different formats.
- [Records](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/records.md): How to use data records in DataVec.
- [Reductions](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/reductions.md)
- [Schemas](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/schemas.md): Schemas for datasets and transformation.
- [Serialization](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/serialization.md): Data wrangling and mapping from one schema to another.
- [Visualization](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/reference/visualization.md)
- [Explanation](https://deeplearning4j.konduit.ai/1.0.0-m2/datavec/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/tutorials.md)
- [Quickstart](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/tutorials/quickstart.md): Comprehensive programming guide for ND4J. This user guide is designed to explain (and provide examples for) the main functionality in ND4J.
- [How To Guides](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/how-to-guides.md)
- [Other Framework Interop](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/how-to-guides/other-framework-interop.md)
- [Tensorflow](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/how-to-guides/other-framework-interop/tensorflow.md): Tensorflow interop Key features and brief samples.
- [TVM](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/how-to-guides/other-framework-interop/tvm.md): TVM Key features and brief samples.
- [Onnx](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/how-to-guides/other-framework-interop/onnx.md): Onnx interop Key features and brief samples.
- [Matrix Manipulation](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/how-to-guides/matrix-manipulation.md)
- [Element wise Operations](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/how-to-guides/element-wise-operations.md)
- [Basics](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/how-to-guides/basics.md): Elementwise Operations And Basic Usage
- [Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/reference.md): Comprehensive programming guide for ND4J. This user guide is designed to explain (and provide examples for) the main functionality in ND4J.
- [Op Descriptor Format](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/reference/op-descriptor-format.md): Model import framework overview and examples
- [Tensor](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/reference/tensor.md)
- [Syntax](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/reference/syntax.md)
- [Explanation](https://deeplearning4j.konduit.ai/1.0.0-m2/nd4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/tutorials.md)
- [Quickstart](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/tutorials/quickstart.md): Samediff Quickstart
- [How To Guides](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/how-to-guides.md)
- [Importing Tensorflow](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/how-to-guides/importing-tensorflow.md)
- [Adding Operations](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/how-to-guides/adding-operations.md): How to add differential functions and other ops to SameDiff graph.
- [codegen](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/how-to-guides/adding-operations/codegen.md)
- [Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference.md)
- [Operation Namespaces](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces.md)
- [Base Operations](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/base-operations.md)
- [Bitwise](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/bitwise.md)
- [CNN](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/cnn.md)
- [Image](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/image.md)
- [LinAlg](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/linalg.md)
- [Loss](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/loss.md)
- [Math](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/math.md)
- [NN](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/nn.md)
- [Random](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/random.md)
- [RNN](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/operation-namespaces/rnn.md)
- [Variables](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/reference/variables.md): What types of variables are used in SameDiff, their properties and how to switch these types.
- [Explanation](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/explanation.md)
- [Model Import Framework](https://deeplearning4j.konduit.ai/1.0.0-m2/samediff/explanation/model-import-framework.md): Model import framework overview and examples
- [How To Guides](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/how-to-guides.md)
- [Building on Windows](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/how-to-guides/building-on-windows.md)
- [Building for raspberry pi or Jetson Nano](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/how-to-guides/building-for-raspberry-pi-or-jetson-nano.md)
- [Building on ios](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/how-to-guides/building-on-ios.md)
- [How to Add Operations](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/how-to-guides/how-to-add-operations.md)
- [How to Setup CLion](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/how-to-guides/how-to-setup-clion.md): Setting up clion for modifying the libnd4j code base
- [Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/reference.md)
- [Understanding graph execution](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/reference/understanding-graph-execution.md)
- [Overview of working with libnd4j](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/reference/tutorials.md)
- [Helpers Overview (CUDNN, OneDNN,Armcompute)](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/reference/helpers-overview-cudnn-onednn-armcompute.md)
- [Explanation](https://deeplearning4j.konduit.ai/1.0.0-m2/libnd4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/tutorials.md)
- [Quickstart](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/tutorials/quickstart.md): Python4j Key features and brief samples.
- [How To Guides](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/how-to-guides.md)
- [Write Python Script](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/how-to-guides/write-python-script.md): How to write a python script for python4j
- [Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/reference.md)
- [Python Types](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/reference/python-types.md): Python4j and python types
- [Python Path](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/reference/python-path.md): Python4j and custom python path
- [Garbage Collection](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/reference/garbage-collection.md): Python4j Garbage Collection and interactions with the JVM
- [Python Script Execution](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/reference/python-script-execution.md): Python4j Python Script Execution
- [Explanation](https://deeplearning4j.konduit.ai/1.0.0-m2/python4j/explanation.md)
- [Tutorials](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/tutorials.md)
- [DL4J on Spark Quickstart](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/tutorials/dl4j-on-spark-quickstart.md): Deeplearning4j on Spark: Introduction
- [How To Guides](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/how-to-guides.md)
- [How To](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/how-to-guides/how-to.md): Deeplearning4j on Spark: How To Build Data Pipelines
- [Data How To](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/how-to-guides/data-how-to-1.md): Deeplearning4j on Spark: How To Guides
- [Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/reference.md)
- [Parameter Server](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/reference/parameter-server.md): Deeplearning4j supports fast distributed training with Spark and a parameter server.
- [Technical Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/reference/technical-reference.md): Deeplearning4j on Spark: Technical Explanation
- [Explanation](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/explanation.md)
- [Spark API Reference](https://deeplearning4j.konduit.ai/1.0.0-m2/spark/explanation/spark-api-reference.md): This page provides the API reference for key classes required to do distributed training with DL4J on Spark. Make sure you have read the introduction guide for deeplearning4j Spark training.
- [codegen](https://deeplearning4j.konduit.ai/1.0.0-m2/codegen.md)

## EN 1.0.0-M2.1

- [Eclipse Deeplearning4j](https://deeplearning4j.konduit.ai/master.md): Comprehensive documentation for Eclipse Deeplearning4j and its ecosystem — ND4J, SameDiff, DataVec, Keras Import, Python4J, and more
- [Ecosystem Overview](https://deeplearning4j.konduit.ai/core-concepts/ecosystem-overview.md): Overview of the Eclipse Deeplearning4j ecosystem — ND4J, DL4J, DataVec, SameDiff, Python4J, and OmniHub
- [Tensors and NDArrays](https://deeplearning4j.konduit.ai/core-concepts/tensors-and-ndarrays.md): INDArray fundamentals — shape, rank, stride, DataType, creating arrays, views vs copies, and off-heap memory in ND4J
- [Data Pipelines](https://deeplearning4j.konduit.ai/core-concepts/data-pipelines.md): Loading, transforming, and feeding data for training — RecordReader, DataSetIterator, normalization, and mini-batching
- [Neural Network Fundamentals](https://deeplearning4j.konduit.ai/core-concepts/neural-net-fundamentals.md): Layers, activation functions, loss functions, weight initialization, and regularization in Deeplearning4j
- [The Training Loop](https://deeplearning4j.konduit.ai/core-concepts/training-loop.md): Building, configuring, and training neural networks — NeuralNetConfiguration, updaters, fit(), listeners, and ComputationGraph
- [Evaluation](https://deeplearning4j.konduit.ai/core-concepts/evaluation.md): Evaluating model performance — classification metrics, ROC curves, regression metrics, and evaluation during training
- [Memory and Workspaces](https://deeplearning4j.konduit.ai/core-concepts/memory-and-workspaces.md): Off-heap memory management, JVM configuration, workspace modes, and troubleshooting memory issues in ND4J and DL4J
- [Overview](https://deeplearning4j.konduit.ai/nd4j/overview.md): Architecture, INDArray interface, memory model, data types, and backend system for the ND4J tensor library
- [Quickstart](https://deeplearning4j.konduit.ai/nd4j/quickstart.md): Hands-on quickstart guide for ND4J — creating arrays, basic operations, indexing, and shape manipulation
- [Creating NDArrays](https://deeplearning4j.konduit.ai/nd4j/creating-ndarrays.md): All methods for creating INDArrays — factory methods, from Java arrays, random, combining, and typed creation
- [Indexing and Slicing](https://deeplearning4j.konduit.ai/nd4j/indexing-and-slicing.md): Accessing and modifying elements, rows, columns, and sub-arrays of INDArrays using NDArrayIndex
- [Operations](https://deeplearning4j.konduit.ai/nd4j/operations.md): Scalar, element-wise, transform, reduction, broadcast, comparison, and linear algebra operations on INDArrays
- [Matrix Manipulation](https://deeplearning4j.konduit.ai/nd4j/matrix-manipulation.md): Reshaping, transposing, permuting, concatenating, sorting, and other shape manipulation operations on INDArrays
- [Data Types](https://deeplearning4j.konduit.ai/nd4j/data-types.md): The DataType enum, per-array typing, type casting, mixed precision, and migration from global data type
- [Activations](https://deeplearning4j.konduit.ai/nd4j/activations.md): Activation functions in ND4J — the Activation enum, IActivation interface, mathematical definitions, and usage in layers
- [Updaters](https://deeplearning4j.konduit.ai/nd4j/updaters.md): Optimization algorithms in ND4J — Adam, SGD, AdaGrad, learning rate schedules, and per-layer updater configuration
- [Loss Functions](https://deeplearning4j.konduit.ai/nd4j/loss-functions.md): All loss functions in ND4J — ILossFunction implementations, usage in output layers, weighted loss, and custom loss functions
- [Weight Initialization](https://deeplearning4j.konduit.ai/nd4j/weight-initialization.md): Weight initialization strategies in ND4J — WeightInit enum, IWeightInit interface, and choosing the right initializer
- [Serialization](https://deeplearning4j.konduit.ai/nd4j/serialization.md): Saving and loading INDArrays in binary, text, NumPy, and ByteBuffer formats
- [Workspaces](https://deeplearning4j.konduit.ai/nd4j/workspaces.md): MemoryWorkspace API — configuration, policies, nested workspaces, scope panic, and lifecycle management
- [Random Number Generation](https://deeplearning4j.konduit.ai/nd4j/random.md): Generating random INDArrays, probability distributions, seeding, and reproducibility in ND4J
- [New Operations Reference](https://deeplearning4j.konduit.ai/nd4j/new-operations.md): Complete reference for ~130 new native operations — fused attention, KV cache, PEFT linear layers, normalization, positional encoding, quantization, SSM, MoE, and audio/signal processing
- [Backends](https://deeplearning4j.konduit.ai/nd4j/overview-1.md): How ND4J's backend system works — SPI mechanism, backend selection, and the relationship between nd4j-native and nd4j-cuda
- [CPU (nd4j-native)](https://deeplearning4j.konduit.ai/nd4j/overview-1/cpu.md): Setting up the nd4j-native CPU backend — Maven dependencies, AVX2/AVX512 optimizations, OpenBLAS, MKL, and multi-threading
- [CUDA (nd4j-cuda)](https://deeplearning4j.konduit.ai/nd4j/overview-1/cuda.md): Setting up the nd4j-cuda GPU backend — CUDA versions, cuDNN integration, multi-GPU, and GPU memory management
- [Hardware Backends (1.0.0-rewrite)](https://deeplearning4j.konduit.ai/nd4j/overview-1/hardware-backends.md): GPU, TPU, DSP, and CPU acceleration backends — CUDA, TPU (PJRT), Hexagon (QNN), ZLUDA, ARM ACL, Apple Accelerate, cuDNN, MPS, MLIR, and multi-backend dispatch
- [SameDiff](https://deeplearning4j.konduit.ai/nd4j/overview-2.md): Automatic differentiation framework in ND4J — define-and-run computation graphs, comparison with MultiLayerNetwork and ComputationGraph
- [Variables](https://deeplearning4j.konduit.ai/nd4j/overview-2/variables.md): SDVariable types — VARIABLE, CONSTANT, PLACEHOLDER, ARRAY — data types, naming, and type conversion
- [Operations](https://deeplearning4j.konduit.ai/nd4j/overview-2/operations.md): Op namespaces — sd.math, sd.nn, sd.cnn, sd.rnn, sd.loss, sd.random — and SDVariable methods
- [Training](https://deeplearning4j.konduit.ai/nd4j/overview-2/training.md): Training SameDiff models — TrainingConfig, fit(), listeners, loss curves, and evaluation
- [Execution and Inference](https://deeplearning4j.konduit.ai/nd4j/overview-2/execution.md): Running SameDiff graphs — exec(), output(), batchOutput(), placeholders, and InferenceSession
- [Serialization](https://deeplearning4j.konduit.ai/nd4j/overview-2/serialization.md): Saving and loading SameDiff graphs in FlatBuffers format
- [DSP Execution Engine](https://deeplearning4j.konduit.ai/nd4j/overview-2/dsp.md): Complete guide to the Dynamic Shape Plan execution engine — compiled graph runtime with CUDA graph capture/replay, Triton/NVRTC/PTX JIT, 26-pass optimizer, and multi-backend dispatch
- [Quickstart](https://deeplearning4j.konduit.ai/deeplearning4j/quickstart.md): End-to-end quickstart guide — from Maven setup to training an MNIST classifier in Deeplearning4j
- [Core Concepts](https://deeplearning4j.konduit.ai/deeplearning4j/concepts.md): Architecture overview of Deeplearning4j — MultiLayerNetwork, ComputationGraph, the training pipeline, and DL4J's relationship to ND4J
- [Beginners Guide](https://deeplearning4j.konduit.ai/deeplearning4j/beginners.md): Getting started with deep learning and Deeplearning4j — prerequisites, learning path, and recommended resources
- [Cheat Sheet](https://deeplearning4j.konduit.ai/deeplearning4j/cheat-sheet.md): Quick reference cheat sheet for Deeplearning4j — common configurations, layer types, and API patterns
- [Examples Tour](https://deeplearning4j.konduit.ai/deeplearning4j/examples-tour.md): Tour of the dl4j-examples repository — classification, regression, CNN, RNN, and advanced examples
- [Neural Networks](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork.md): The MultiLayerNetwork API — building, configuring, training, evaluating, and using sequential neural networks
- [ComputationGraph](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/computationgraph.md): The ComputationGraph API — multi-input, multi-output, skip connections, and arbitrary DAG topologies
- [Layers Reference](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/layers.md): Complete reference for all layer types in Deeplearning4j — Dense, Activation, Dropout, Embedding, BatchNormalization, and more
- [Convolutional Layers](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/convolutional.md): CNN layers in Deeplearning4j — Conv1D/2D/3D, pooling, deconvolution, depthwise, separable, and upsampling layers
- [Recurrent Layers](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/recurrent.md): RNN layers in Deeplearning4j — LSTM, GRU, Bidirectional wrapper, masking, TBPTT, and sequence data handling
- [Autoencoders](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/autoencoders.md): Autoencoder and Variational Autoencoder layers in Deeplearning4j — architecture, configuration, and training
- [Graph Vertices](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/vertices.md): Vertex types for ComputationGraph — Merge, ElementWise, Subset, Stack, Reshape, and custom vertices
- [Data Iterators](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/iterators.md): DataSetIterator implementations — built-in iterators, custom iterators, async loading, and data splitting
- [Listeners](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/listeners.md): Training listeners — ScoreIterationListener, PerformanceListener, EvaluativeListener, CheckpointListener, and custom listeners
- [Evaluation](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/evaluation.md): Detailed evaluation guide — classification metrics, regression metrics, ROC curves, calibration, and multi-output evaluation
- [Model Persistence](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/model-persistence.md): Saving and loading neural networks — ModelSerializer, saving normalizers, and model format details
- [Transfer Learning](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/transfer-learning.md): Transfer learning in Deeplearning4j — TransferLearning.Builder, FineTuneConfiguration, freezing layers, and modifying pretrained networks
- [Early Stopping](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/early-stopping.md): Early stopping in Deeplearning4j — EarlyStoppingConfiguration, termination conditions, score calculators, and model savers
- [Custom Layers](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/custom-layer.md): Writing custom layers in Deeplearning4j — extending Layer, SameDiff-backed custom layers, and custom graph vertices
- [Visualization](https://deeplearning4j.konduit.ai/deeplearning4j/multilayernetwork/visualization.md): Visualizing training progress — VertxUIServer, StatsListener, the training dashboard, and t-SNE visualization
- [NLP](https://deeplearning4j.konduit.ai/deeplearning4j/overview.md): Natural language processing in Deeplearning4j — Word2Vec, Doc2Vec, tokenization, and text processing pipeline
- [Word2Vec](https://deeplearning4j.konduit.ai/deeplearning4j/overview/word2vec.md): Word2Vec in Deeplearning4j — Skip-Gram, CBOW, training word vectors, and using pretrained embeddings
- [Doc2Vec](https://deeplearning4j.konduit.ai/deeplearning4j/overview/doc2vec.md): Document vectors with ParagraphVectors (Doc2Vec) — training document embeddings and document similarity
- [Sentence Iterators](https://deeplearning4j.konduit.ai/deeplearning4j/overview/sentence-iterator.md): Text input for NLP — SentenceIterator, BasicLineIterator, FileSentenceIterator, and custom iterators
- [Tokenization](https://deeplearning4j.konduit.ai/deeplearning4j/overview/tokenization.md): Tokenizer factories in Deeplearning4j — DefaultTokenizerFactory, custom tokenizers, and preprocessors
- [Vocabulary Cache](https://deeplearning4j.konduit.ai/deeplearning4j/overview/vocabulary-cache.md): VocabCache in Deeplearning4j — how vocabulary is stored, InMemoryLookupCache, and vocabulary management
- [Distributed Training](https://deeplearning4j.konduit.ai/deeplearning4j/overview-1.md): When and how to use distributed training — architecture overview, Spark integration, and ParallelWrapper
- [Spark How-To](https://deeplearning4j.konduit.ai/deeplearning4j/overview-1/spark-howto.md): Step-by-step guide to distributed training with Apache Spark — setup, data loading, training, and evaluation
- [Spark Data Pipelines](https://deeplearning4j.konduit.ai/deeplearning4j/overview-1/spark-data-howto.md): Loading and preprocessing data on Apache Spark for distributed DL4J training
- [Spark API Reference](https://deeplearning4j.konduit.ai/deeplearning4j/overview-1/spark-api-reference.md): API reference for SparkDl4jMultiLayer, SparkComputationGraph, and TrainingMaster
- [Parameter Server](https://deeplearning4j.konduit.ai/deeplearning4j/overview-1/parameter-server.md): Gradient sharing via the Aeron-based parameter server for distributed training
- [Technical Reference](https://deeplearning4j.konduit.ai/deeplearning4j/overview-1/technical-reference.md): Technical details of distributed training — Strom ASGD algorithm, mesh networking, and fault tolerance
- [ParallelWrapper (Multi-GPU)](https://deeplearning4j.konduit.ai/deeplearning4j/overview-1/parallel-wrapper.md): Multi-GPU training with ParallelWrapper and high-throughput inference with ParallelInference in DL4J 1.0.0-M2.1.
- [Model Zoo](https://deeplearning4j.konduit.ai/deeplearning4j/overview-2.md): Pretrained models in Deeplearning4j — available architectures, using pretrained weights, and transfer learning from zoo models
- [Available Models](https://deeplearning4j.konduit.ai/deeplearning4j/overview-2/models.md): Complete list of pretrained models — VGG, ResNet, AlexNet, LeNet, YOLO, UNet, and other architectures
- [Training UI](https://deeplearning4j.konduit.ai/deeplearning4j/overview-3.md): Web-based training visualization — VertxUIServer, StatsListener, training dashboard, and t-SNE visualization
- [Troubleshooting](https://deeplearning4j.konduit.ai/deeplearning4j/troubleshooting.md): Common training problems and solutions — NaN loss, slow convergence, overfitting, memory errors, and debugging tips
- [Benchmarking](https://deeplearning4j.konduit.ai/deeplearning4j/benchmarking.md): How to benchmark DL4J and ND4J — OpProfiler, performance metrics, and comparing configurations
- [Building from Source](https://deeplearning4j.konduit.ai/deeplearning4j/build-from-source.md): How to build Deeplearning4j from source — prerequisites, Maven build, libnd4j compilation, and common build issues
- [Contributing](https://deeplearning4j.konduit.ai/deeplearning4j/contributing.md): How to contribute to Deeplearning4j — development setup, code style, pull requests, and community guidelines
- [LLM & VLM Stack](https://deeplearning4j.konduit.ai/deeplearning4j/overview-4.md): Complete guide to the samediff-llm generation pipeline, KV cache management, speculative decoding, continuous batching, tokenizers, evaluation framework, and model editing
- [PEFT & RL Alignment Training](https://deeplearning4j.konduit.ai/deeplearning4j/peft-and-rl.md): Parameter-efficient fine-tuning (12 methods), reinforcement learning alignment (9 trainers), mixed-precision training, knowledge distillation, and dataset curation
- [Overview](https://deeplearning4j.konduit.ai/datavec/overview.md): DataVec ETL framework — purpose, architecture, and the data pipeline from raw data to DataSet
- [Schema](https://deeplearning4j.konduit.ai/datavec/schema.md): Defining data schemas — Schema, ColumnType, SequenceSchema, and schema inference
- [Record Readers](https://deeplearning4j.konduit.ai/datavec/readers.md): RecordReader implementations — CSV, JSON, image, regex, line, and custom readers
- [Transforms](https://deeplearning4j.konduit.ai/datavec/transforms.md): TransformProcess — data transformations, column operations, type conversions, and sequences
- [Conditions](https://deeplearning4j.konduit.ai/datavec/conditions.md): Conditional operations in DataVec — filtering and transforming data based on conditions
- [Filters](https://deeplearning4j.konduit.ai/datavec/filters.md): Data filtering in DataVec — removing records based on conditions
- [Normalization](https://deeplearning4j.konduit.ai/datavec/normalization.md): Data normalization in DataVec — normalizer implementations and serialization
- [Operations](https://deeplearning4j.konduit.ai/datavec/operations.md): DataVec operations — calculators, reducers, and data analysis tools
- [Reductions](https://deeplearning4j.konduit.ai/datavec/reductions.md): DataVec reduction operations — aggregating, grouping, and summarizing records and sequences
- [Analysis](https://deeplearning4j.konduit.ai/datavec/analysis.md): DataVec data analysis tools — profiling datasets, detecting quality issues, and computing statistics locally and on Spark
- [Serialization](https://deeplearning4j.konduit.ai/datavec/serialization.md): Data serialization in DataVec — saving and loading schemas, transform processes, and normalized data
- [Executors](https://deeplearning4j.konduit.ai/datavec/executors.md): Running DataVec transform processes — LocalTransformExecutor and SparkTransformExecutor
- [Records and Writables](https://deeplearning4j.konduit.ai/datavec/records.md): DataVec record format — Writable types, Record, and the data representation layer
- [Image Data](https://deeplearning4j.konduit.ai/datavec/image.md): Loading and preprocessing image data — ImageRecordReader, NativeImageLoader, and image transforms
- [Apache Arrow](https://deeplearning4j.konduit.ai/datavec/arrow.md): Apache Arrow integration in DataVec — ArrowRecordReader and zero-copy data exchange
- [Overview](https://deeplearning4j.konduit.ai/model-import/overview.md): All model import paths in DL4J — Keras to DL4J, TF/ONNX to SameDiff, and direct inference runtimes
- [Keras Import](https://deeplearning4j.konduit.ai/model-import/overview-1.md): Importing Keras models into Deeplearning4j — supported features, limitations, and getting started
- [Getting Started](https://deeplearning4j.konduit.ai/model-import/overview-1/getting-started.md): Step-by-step guide to importing Keras models — saving in Python, loading in Java, and running inference
- [Functional Model](https://deeplearning4j.konduit.ai/model-import/overview-1/functional-model.md): Importing Keras Functional API models as ComputationGraph
- [Sequential Model](https://deeplearning4j.konduit.ai/model-import/overview-1/sequential-model.md): Importing Keras Sequential models as MultiLayerNetwork
- [API Reference](https://deeplearning4j.konduit.ai/model-import/overview-1/model-import-api.md): KerasModelImport API — all import methods and configuration options
- [Supported Features](https://deeplearning4j.konduit.ai/model-import/overview-1/supported-features.md): Full support matrix for Keras model import — layers, activations, losses, and optimizers
- [Core Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-core.md): DL4J equivalents and API reference for Keras core layers — Dense, Flatten, Dropout, Reshape, Merge, Permute, and more.
- [Convolutional Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-convolutional.md): DL4J equivalents and API reference for Keras convolutional layers — Conv1D, Conv2D, Conv3D, SeparableConv2D, transposed convolutions, cropping, upsampling, and zero-padding.
- [Recurrent Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-recurrent.md): DL4J equivalents and API reference for Keras recurrent layers — SimpleRNN, LSTM, and associated utilities.
- [Pooling Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-pooling.md): DL4J equivalents and API reference for Keras pooling layers — MaxPooling, AveragePooling, and GlobalPooling in 1D, 2D, and 3D variants.
- [Normalization Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-normalization.md): DL4J equivalents and API reference for Keras normalization layers — BatchNormalization.
- [Embedding Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-embeddings.md): DL4J equivalents and API reference for Keras embedding layers — Embedding mapped to EmbeddingSequenceLayer.
- [Advanced Activations](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-advanced-activations.md): DL4J equivalents and API reference for Keras advanced activation layers — LeakyReLU, PReLU, ELU, and ThresholdedReLU.
- [Wrapper Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-wrappers.md): DL4J equivalents and API reference for Keras wrapper layers — Bidirectional and TimeDistributed.
- [Noise Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-noise.md): DL4J equivalents and API reference for Keras noise layers — GaussianNoise, GaussianDropout, and AlphaDropout.
- [Local Layers](https://deeplearning4j.konduit.ai/model-import/overview-1/layers-local.md): DL4J equivalents and API reference for Keras locally connected layers — LocallyConnected1D and LocallyConnected2D.
- [Activations](https://deeplearning4j.konduit.ai/model-import/overview-1/activations.md): Keras to DL4J activation function mapping for model import
- [Optimizers](https://deeplearning4j.konduit.ai/model-import/overview-1/optimizers.md): Keras to DL4J optimizer mapping for model import
- [Losses](https://deeplearning4j.konduit.ai/model-import/overview-1/losses.md): Keras to DL4J loss function mapping for model import
- [Initializers](https://deeplearning4j.konduit.ai/model-import/overview-1/initializers.md): Mapping of Keras weight initializers to DL4J WeightInit implementations for model import in Eclipse Deeplearning4j 1.0.0-M2.1.
- [Constraints](https://deeplearning4j.konduit.ai/model-import/overview-1/constraints.md): Mapping of Keras weight constraints to DL4J LayerConstraint implementations for model import in Eclipse Deeplearning4j 1.0.0-M2.1.
- [Regularizers](https://deeplearning4j.konduit.ai/model-import/overview-1/regularizers.md): Mapping of Keras regularizers to DL4J regularization for model import in Eclipse Deeplearning4j 1.0.0-M2.1.
- [Backend](https://deeplearning4j.konduit.ai/model-import/overview-1/backend.md): DL4J Keras model import is backend-agnostic — models trained with TensorFlow, Theano, or CNTK backends can all be imported into Eclipse Deeplearning4j 1.0.0-M2.1.
- [SameDiff Import](https://deeplearning4j.konduit.ai/model-import/overview-2.md): Importing TensorFlow and ONNX models into SameDiff — architecture, supported ops, and usage
- [TensorFlow Import](https://deeplearning4j.konduit.ai/model-import/overview-2/tensorflow.md): Importing TensorFlow frozen graphs and SavedModels into SameDiff
- [ONNX Import](https://deeplearning4j.konduit.ai/model-import/overview-2/onnx.md): Importing ONNX models into SameDiff
- [ONNX Import & Export (Expanded)](https://deeplearning4j.konduit.ai/model-import/overview-2/onnx-expanded.md): ~120 new ONNX op implementations including Microsoft LLM contrib ops, ONNX ML domain classifiers, quantized inference ops, and bidirectional SameDiff-to-ONNX export
- [GGML/GGUF Import](https://deeplearning4j.konduit.ai/model-import/overview-3.md): Import quantized LLMs from GGUF files — architecture handlers, quantization codecs, adaptive quantization, round-trip export, and pipeline modules
- [ONNX Runtime](https://deeplearning4j.konduit.ai/model-import/overview-4.md): Direct ONNX model inference via ONNX Runtime 1.10 — no conversion to SameDiff required
- [TensorFlow](https://deeplearning4j.konduit.ai/model-import/overview-5.md): Running TensorFlow frozen graphs directly via JavaCPP TF bindings
- [TensorFlow Lite](https://deeplearning4j.konduit.ai/model-import/overview-5/tensorflow-lite.md): TensorFlow Lite 2.8 inference for mobile and edge deployment
- [Apache TVM](https://deeplearning4j.konduit.ai/model-import/overview-6.md): Apache TVM 0.8 integration for optimized model inference
- [Overview](https://deeplearning4j.konduit.ai/python4j/overview.md): Embedding CPython in JVM applications — what Python4J is, use cases, and architecture
- [Getting Started](https://deeplearning4j.konduit.ai/python4j/getting-started.md): Setting up Python4J — Maven dependencies, executing Python code, and variable I/O
- [NumPy Bridge](https://deeplearning4j.konduit.ai/python4j/numpy-bridge.md): Zero-copy data exchange between INDArray and numpy.ndarray via python4j-numpy
- [Advanced Usage](https://deeplearning4j.konduit.ai/python4j/advanced.md): Advanced Python4J features — GIL management, context managers, garbage collection, and subprocess mode
- [Overview](https://deeplearning4j.konduit.ai/omnihub/overview.md): OmniHub model registry — downloading and using pretrained models from the DL4J ecosystem
- [Usage](https://deeplearning4j.konduit.ai/omnihub/usage.md): Downloading and loading models from OmniHub — OmniHubUtils API
- [Available Models](https://deeplearning4j.konduit.ai/omnihub/available-models.md): Pretrained models available through OmniHub — DL4J and SameDiff model catalog
- [Maven Setup](https://deeplearning4j.konduit.ai/configuration/maven.md): Maven dependencies for Deeplearning4j — BOM, backend selection, platform classifiers, and version management
- [Build Tools (Gradle, SBT)](https://deeplearning4j.konduit.ai/configuration/build-tools.md): Configuring Deeplearning4j with Gradle, SBT, and other build tools
- [GPU and CPU Setup](https://deeplearning4j.konduit.ai/configuration/gpu-cpu.md): Configuring GPU and CPU backends — CUDA setup, multi-GPU, CPU optimizations, and backend switching
- [cuDNN](https://deeplearning4j.konduit.ai/configuration/cudnn.md): cuDNN integration — installation, configuration, and performance benefits
- [Memory Configuration](https://deeplearning4j.konduit.ai/configuration/memory.md): JVM memory flags, off-heap configuration, and memory management for ND4J and DL4J
- [Workspaces](https://deeplearning4j.konduit.ai/configuration/workspaces.md): WorkspaceMode configuration for training and inference memory management
- [Performance Debugging](https://deeplearning4j.konduit.ai/configuration/performance-debugging.md): Diagnosing and resolving performance issues in DL4J and ND4J — profiling, OpProfiler, ETL bottlenecks, GC overhead, and backend verification.
- [Snapshots and Daily Builds](https://deeplearning4j.konduit.ai/configuration/snapshots.md): Using nightly snapshot builds of Eclipse Deeplearning4j 1.0.0-M2.1 — repository configuration, version identifiers, Maven and Gradle setup.
- [Overview](https://deeplearning4j.konduit.ai/arbiter/overview.md): Arbiter hyperparameter optimization for DL4J — architecture, search strategies, configuration, and usage.
- [Layer Spaces](https://deeplearning4j.konduit.ai/arbiter/layer-spaces.md): Layer parameter spaces in Arbiter — configuration of per-layer hyperparameter search spaces for DL4J neural networks.
- [Parameter Spaces](https://deeplearning4j.konduit.ai/arbiter/parameter-spaces.md): Parameter space types for Arbiter hyperparameter search — continuous, integer, discrete, boolean, fixed, and composite spaces.
- [Visualization](https://deeplearning4j.konduit.ai/arbiter/visualization.md): Monitoring Arbiter hyperparameter optimization runs with ArbiterStatusListener and the UIServer.
- [1.0.0-rewrite](https://deeplearning4j.konduit.ai/release-notes/1.0.0-rewrite.md): Release notes for the Deeplearning4j 1.0.0-rewrite: a transitional release introducing the DSP execution engine, LLM/VLM stack, GGUF import, PEFT/RL alignment, and massive hardware expansion. Namespac

## EN 1.0.0-rewrite

- [Eclipse Deeplearning4j](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/master.md): Comprehensive documentation for Eclipse Deeplearning4j and its ecosystem — ND4J, SameDiff, DataVec, Keras Import, Python4J, and more
- [Ecosystem Overview](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/core-concepts/ecosystem-overview.md): Overview of the Eclipse Deeplearning4j ecosystem — ND4J, DL4J, DataVec, SameDiff, Python4J, and OmniHub
- [Tensors and NDArrays](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/core-concepts/tensors-and-ndarrays.md): INDArray fundamentals — shape, rank, stride, DataType, creating arrays, views vs copies, and off-heap memory in ND4J
- [Data Pipelines](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/core-concepts/data-pipelines.md): Loading, transforming, and feeding data for training — RecordReader, DataSetIterator, normalization, and mini-batching
- [Neural Network Fundamentals](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/core-concepts/neural-net-fundamentals.md): Layers, activation functions, loss functions, weight initialization, and regularization in Deeplearning4j
- [The Training Loop](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/core-concepts/training-loop.md): Building, configuring, and training neural networks — NeuralNetConfiguration, updaters, fit(), listeners, and ComputationGraph
- [Evaluation](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/core-concepts/evaluation.md): Evaluating model performance — classification metrics, ROC curves, regression metrics, and evaluation during training
- [Memory and Workspaces](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/core-concepts/memory-and-workspaces.md): Off-heap memory management, JVM configuration, workspace modes, and troubleshooting memory issues in ND4J and DL4J
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview.md): Architecture, INDArray interface, memory model, data types, and backend system for the ND4J tensor library
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/quickstart.md): Hands-on quickstart guide for ND4J — creating arrays, basic operations, indexing, and shape manipulation
- [Creating NDArrays](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/creating-ndarrays.md): All methods for creating INDArrays — factory methods, from Java arrays, random, combining, and typed creation
- [Indexing and Slicing](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/indexing-and-slicing.md): Accessing and modifying elements, rows, columns, and sub-arrays of INDArrays using NDArrayIndex
- [Operations](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/operations.md): Scalar, element-wise, transform, reduction, broadcast, comparison, and linear algebra operations on INDArrays
- [Matrix Manipulation](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/matrix-manipulation.md): Reshaping, transposing, permuting, concatenating, sorting, and other shape manipulation operations on INDArrays
- [Data Types](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/data-types.md): The DataType enum, per-array typing, type casting, mixed precision, and migration from global data type
- [Activations](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/activations.md): Activation functions in ND4J — the Activation enum, IActivation interface, mathematical definitions, and usage in layers
- [Updaters](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/updaters.md): Optimization algorithms in ND4J — Adam, SGD, AdaGrad, learning rate schedules, and per-layer updater configuration
- [Loss Functions](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/loss-functions.md): All loss functions in ND4J — ILossFunction implementations, usage in output layers, weighted loss, and custom loss functions
- [Weight Initialization](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/weight-initialization.md): Weight initialization strategies in ND4J — WeightInit enum, IWeightInit interface, and choosing the right initializer
- [Serialization](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/serialization.md): Saving and loading INDArrays in binary, text, NumPy, and ByteBuffer formats
- [Workspaces](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/workspaces.md): MemoryWorkspace API — configuration, policies, nested workspaces, scope panic, and lifecycle management
- [Random Number Generation](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/random.md): Generating random INDArrays, probability distributions, seeding, and reproducibility in ND4J
- [New Operations Reference](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/new-operations.md): Complete reference for ~130 new native operations — fused attention, KV cache, PEFT linear layers, normalization, positional encoding, quantization, SSM, MoE, and audio/signal processing
- [Backends](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-1.md): How ND4J's backend system works — SPI mechanism, backend selection, and the relationship between nd4j-native and nd4j-cuda
- [CPU (nd4j-native)](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-1/cpu.md): Setting up the nd4j-native CPU backend — Maven dependencies, AVX2/AVX512 optimizations, OpenBLAS, MKL, and multi-threading
- [CUDA (nd4j-cuda)](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-1/cuda.md): Setting up the nd4j-cuda GPU backend — CUDA versions, cuDNN integration, multi-GPU, and GPU memory management
- [Hardware Backends (1.0.0-rewrite)](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-1/hardware-backends.md): GPU, TPU, DSP, and CPU acceleration backends — CUDA, TPU (PJRT), Hexagon (QNN), ZLUDA, ARM ACL, Apple Accelerate, cuDNN, MPS, MLIR, and multi-backend dispatch
- [SameDiff](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-2.md): Automatic differentiation framework in ND4J — define-and-run computation graphs, comparison with MultiLayerNetwork and ComputationGraph
- [Variables](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-2/variables.md): SDVariable types — VARIABLE, CONSTANT, PLACEHOLDER, ARRAY — data types, naming, and type conversion
- [Operations](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-2/operations.md): Op namespaces — sd.math, sd.nn, sd.cnn, sd.rnn, sd.loss, sd.random — and SDVariable methods
- [Training](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-2/training.md): Training SameDiff models — TrainingConfig, fit(), listeners, loss curves, and evaluation
- [Execution and Inference](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-2/execution.md): Running SameDiff graphs — exec(), output(), batchOutput(), placeholders, and InferenceSession
- [Serialization](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-2/serialization.md): Saving and loading SameDiff graphs in FlatBuffers format
- [DSP Framework Overview](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-2/dsp-framework.md): End-to-end overview of the whole Dynamic Shape Plan (DSP) framework — architecture from SameDiff graph to native replay, plan caching, state management, execution backends, LLM decode integration, mul
- [DSP Execution Engine](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/overview-2/dsp.md): Complete guide to the Dynamic Shape Plan execution engine — compiled graph runtime with CUDA graph capture/replay, Triton/NVRTC/PTX JIT, 26-pass optimizer, and multi-backend dispatch
- [Quickstart](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/quickstart.md): End-to-end quickstart guide — from Maven setup to training an MNIST classifier in Deeplearning4j
- [Core Concepts](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/concepts.md): Architecture overview of Deeplearning4j — MultiLayerNetwork, ComputationGraph, the training pipeline, and DL4J's relationship to ND4J
- [Beginners Guide](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/beginners.md): Getting started with deep learning and Deeplearning4j — prerequisites, learning path, and recommended resources
- [Cheat Sheet](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/cheat-sheet.md): Quick reference cheat sheet for Deeplearning4j — common configurations, layer types, and API patterns
- [Examples Tour](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/examples-tour.md): Tour of the dl4j-examples repository — classification, regression, CNN, RNN, and advanced examples
- [Neural Networks](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork.md): The MultiLayerNetwork API — building, configuring, training, evaluating, and using sequential neural networks
- [ComputationGraph](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/computationgraph.md): The ComputationGraph API — multi-input, multi-output, skip connections, and arbitrary DAG topologies
- [Layers Reference](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/layers.md): Complete reference for all layer types in Deeplearning4j — Dense, Activation, Dropout, Embedding, BatchNormalization, and more
- [Convolutional Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/convolutional.md): CNN layers in Deeplearning4j — Conv1D/2D/3D, pooling, deconvolution, depthwise, separable, and upsampling layers
- [Recurrent Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/recurrent.md): RNN layers in Deeplearning4j — LSTM, GRU, Bidirectional wrapper, masking, TBPTT, and sequence data handling
- [Autoencoders](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/autoencoders.md): Autoencoder and Variational Autoencoder layers in Deeplearning4j — architecture, configuration, and training
- [Graph Vertices](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/vertices.md): Vertex types for ComputationGraph — Merge, ElementWise, Subset, Stack, Reshape, and custom vertices
- [Data Iterators](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/iterators.md): DataSetIterator implementations — built-in iterators, custom iterators, async loading, and data splitting
- [Listeners](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/listeners.md): Training listeners — ScoreIterationListener, PerformanceListener, EvaluativeListener, CheckpointListener, and custom listeners
- [Evaluation](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/evaluation.md): Detailed evaluation guide — classification metrics, regression metrics, ROC curves, calibration, and multi-output evaluation
- [Model Persistence](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/model-persistence.md): Saving and loading neural networks — ModelSerializer, saving normalizers, and model format details
- [Transfer Learning](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/transfer-learning.md): Transfer learning in Deeplearning4j — TransferLearning.Builder, FineTuneConfiguration, freezing layers, and modifying pretrained networks
- [Early Stopping](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/early-stopping.md): Early stopping in Deeplearning4j — EarlyStoppingConfiguration, termination conditions, score calculators, and model savers
- [Custom Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/custom-layer.md): Writing custom layers in Deeplearning4j — extending Layer, SameDiff-backed custom layers, and custom graph vertices
- [Visualization](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/multilayernetwork/visualization.md): Visualizing training progress — VertxUIServer, StatsListener, the training dashboard, and t-SNE visualization
- [NLP](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview.md): Natural language processing in Deeplearning4j — Word2Vec, Doc2Vec, tokenization, and text processing pipeline
- [Word2Vec](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview/word2vec.md): Word2Vec in Deeplearning4j — Skip-Gram, CBOW, training word vectors, and using pretrained embeddings
- [Doc2Vec](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview/doc2vec.md): Document vectors with ParagraphVectors (Doc2Vec) — training document embeddings and document similarity
- [Sentence Iterators](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview/sentence-iterator.md): Text input for NLP — SentenceIterator, BasicLineIterator, FileSentenceIterator, and custom iterators
- [Tokenization](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview/tokenization.md): Tokenizer factories in Deeplearning4j — DefaultTokenizerFactory, custom tokenizers, and preprocessors
- [Vocabulary Cache](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview/vocabulary-cache.md): VocabCache in Deeplearning4j — how vocabulary is stored, InMemoryLookupCache, and vocabulary management
- [Distributed Training](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-1.md): When and how to use distributed training — architecture overview, Spark integration, and ParallelWrapper
- [Spark How-To](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-1/spark-howto.md): Step-by-step guide to distributed training with Apache Spark — setup, data loading, training, and evaluation
- [Spark Data Pipelines](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-1/spark-data-howto.md): Loading and preprocessing data on Apache Spark for distributed DL4J training
- [Spark API Reference](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-1/spark-api-reference.md): API reference for SparkDl4jMultiLayer, SparkComputationGraph, and TrainingMaster
- [Parameter Server](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-1/parameter-server.md): Gradient sharing via the Aeron-based parameter server for distributed training
- [Technical Reference](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-1/technical-reference.md): Technical details of distributed training — Strom ASGD algorithm, mesh networking, and fault tolerance
- [ParallelWrapper (Multi-GPU)](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-1/parallel-wrapper.md): Multi-GPU training with ParallelWrapper and high-throughput inference with ParallelInference in DL4J 1.0.0-rewrite.
- [Model Zoo](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-2.md): Pretrained models in Deeplearning4j — available architectures, using pretrained weights, and transfer learning from zoo models
- [Available Models](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-2/models.md): Complete list of pretrained models — VGG, ResNet, AlexNet, LeNet, YOLO, UNet, and other architectures
- [Training UI](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-3.md): Web-based training visualization — VertxUIServer, StatsListener, training dashboard, and t-SNE visualization
- [Troubleshooting](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/troubleshooting.md): Common training problems and solutions — NaN loss, slow convergence, overfitting, memory errors, and debugging tips
- [Benchmarking](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/benchmarking.md): How to benchmark DL4J and ND4J — OpProfiler, performance metrics, and comparing configurations
- [Building from Source](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/build-from-source.md): How to build Deeplearning4j from source — prerequisites, Maven build, libnd4j C++ compilation, CUDA and CPU builds, and common issues
- [Contributing](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/contributing.md): How to contribute to Deeplearning4j — Eclipse CLA, build process, project architecture, adding ops, writing examples, and pull request workflow
- [LLM & VLM Stack](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-4.md): Source-verified overview of the 1.0.0-rewrite SameDiff generation, tokenizer, evaluation, VLM, audio, TorchScript, and web modules.
- [LLM Inference and Serving](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-4/inference-and-serving.md): Production guide to GenerationPipeline, fixed-buffer DSP reuse, constrained decoding, cross-request KV prefix caching, and cache observability.
- [LLM Fine-Tuning Overview](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-4/fine-tuning.md): Practical guide to the SameDiff LLM fine-tuning paths: supervised instruction tuning, LoRA and QLoRA adapters, distillation, preference alignment, and quantized training.
- [Instruction SFT, LoRA, and QLoRA](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-4/instruction-sft-lora.md): How to fine-tune LLMs with SameDiff using response-masked supervised fine-tuning, LoRA adapters, QLoRA, and the SFTTrainingPipeline API.
- [Knowledge Distillation](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-4/knowledge-distillation.md): How to train smaller SameDiff language models from larger teacher models using cached teacher logits, KL loss, hard-label loss, feature loss, and attention loss.
- [Preference and RL Alignment](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-4/preference-rl-alignment.md): How to align SameDiff language models with preference pairs, rewards, DPO, GRPO, PPO, ORPO, KTO, reward models, and RLAlignmentPipeline.
- [Quantized and Memory-Efficient Training](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/overview-4/quantized-training.md): How to reduce LLM fine-tuning memory with GraphOptimizer, QLoRA, mixed precision, FP8, Adam8bit, gradient accumulation, and gradient checkpointing.
- [PEFT & RL Alignment Training](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/deeplearning4j/peft-and-rl.md): Parameter-efficient fine-tuning (12 methods), reinforcement learning alignment (9 trainers), mixed-precision training, knowledge distillation, and dataset curation
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/overview.md): DataVec ETL framework — purpose, architecture, and the data pipeline from raw data to DataSet
- [Schema](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/schema.md): Defining data schemas — Schema, ColumnType, SequenceSchema, and schema inference
- [Record Readers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/readers.md): RecordReader implementations — CSV, JSON, image, regex, line, and custom readers
- [Transforms](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/transforms.md): TransformProcess — data transformations, column operations, type conversions, and sequences
- [Conditions](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/conditions.md): Conditional operations in DataVec — filtering and transforming data based on conditions
- [Filters](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/filters.md): Data filtering in DataVec — removing records based on conditions
- [Normalization](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/normalization.md): Data normalization in DataVec — normalizer implementations and serialization
- [Operations](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/operations.md): DataVec operations — calculators, reducers, and data analysis tools
- [Reductions](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/reductions.md): DataVec reduction operations — aggregating, grouping, and summarizing records and sequences
- [Analysis](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/analysis.md): DataVec data analysis tools — profiling datasets, detecting quality issues, and computing statistics locally and on Spark
- [Serialization](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/serialization.md): Data serialization in DataVec — saving and loading schemas, transform processes, and normalized data
- [Executors](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/executors.md): Running DataVec transform processes — LocalTransformExecutor and SparkTransformExecutor
- [Records and Writables](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/records.md): DataVec record format — Writable types, Record, and the data representation layer
- [Image Data](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/image.md): Loading and preprocessing image data — ImageRecordReader, NativeImageLoader, and image transforms
- [Apache Arrow](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/datavec/arrow.md): Apache Arrow integration in DataVec — ArrowRecordReader and zero-copy data exchange
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview.md): All model import paths in DL4J — Keras to DL4J, TF/ONNX to SameDiff, and direct inference runtimes
- [Keras Import](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1.md): Importing Keras models into Deeplearning4j — supported features, limitations, and getting started
- [Getting Started](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/getting-started.md): Step-by-step guide to importing Keras models — saving in Python, loading in Java, and running inference
- [Functional Model](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/functional-model.md): Importing Keras Functional API models as ComputationGraph
- [Sequential Model](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/sequential-model.md): Importing Keras Sequential models as MultiLayerNetwork
- [API Reference](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/model-import-api.md): KerasModelImport API — all import methods and configuration options
- [Supported Features](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/supported-features.md): Full support matrix for Keras model import — layers, activations, losses, and optimizers
- [Core Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-core.md): DL4J equivalents and API reference for Keras core layers — Dense, Flatten, Dropout, Reshape, Merge, Permute, and more.
- [Convolutional Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-convolutional.md): DL4J equivalents and API reference for Keras convolutional layers — Conv1D, Conv2D, Conv3D, SeparableConv2D, transposed convolutions, cropping, upsampling, and zero-padding.
- [Recurrent Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-recurrent.md): DL4J equivalents and API reference for Keras recurrent layers — SimpleRNN, LSTM, and associated utilities.
- [Pooling Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-pooling.md): DL4J equivalents and API reference for Keras pooling layers — MaxPooling, AveragePooling, and GlobalPooling in 1D, 2D, and 3D variants.
- [Normalization Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-normalization.md): DL4J equivalents and API reference for Keras normalization layers — BatchNormalization.
- [Embedding Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-embeddings.md): DL4J equivalents and API reference for Keras embedding layers — Embedding mapped to EmbeddingSequenceLayer.
- [Advanced Activations](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-advanced-activations.md): DL4J equivalents and API reference for Keras advanced activation layers — LeakyReLU, PReLU, ELU, and ThresholdedReLU.
- [Wrapper Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-wrappers.md): DL4J equivalents and API reference for Keras wrapper layers — Bidirectional and TimeDistributed.
- [Noise Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-noise.md): DL4J equivalents and API reference for Keras noise layers — GaussianNoise, GaussianDropout, and AlphaDropout.
- [Local Layers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-local.md): DL4J equivalents and API reference for Keras locally connected layers — LocallyConnected1D and LocallyConnected2D.
- [Activations](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/activations.md): Keras to DL4J activation function mapping for model import
- [Optimizers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/optimizers.md): Keras to DL4J optimizer mapping for model import
- [Losses](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/losses.md): Keras to DL4J loss function mapping for model import
- [Initializers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/initializers.md): Mapping of Keras weight initializers to DL4J WeightInit implementations for model import in Eclipse Deeplearning4j 1.0.0-rewrite.
- [Constraints](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/constraints.md): Mapping of Keras weight constraints to DL4J LayerConstraint implementations for model import in Eclipse Deeplearning4j 1.0.0-rewrite.
- [Regularizers](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/regularizers.md): Mapping of Keras regularizers to DL4J regularization for model import in Eclipse Deeplearning4j 1.0.0-rewrite.
- [Backend](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/backend.md): DL4J Keras model import is backend-agnostic — models trained with TensorFlow, Theano, or CNTK backends can all be imported into Eclipse Deeplearning4j 1.0.0-rewrite.
- [SameDiff Import](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-2.md): Importing TensorFlow and ONNX models into SameDiff — architecture, supported ops, and usage
- [TensorFlow Import](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-2/tensorflow.md): Importing TensorFlow frozen graphs and SavedModels into SameDiff
- [ONNX Import](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-2/onnx.md): Importing ONNX models into SameDiff
- [ONNX Import & Export (Expanded)](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-2/onnx-expanded.md): ~120 new ONNX op implementations including Microsoft LLM contrib ops, ONNX ML domain classifiers, quantized inference ops, and bidirectional SameDiff-to-ONNX export
- [GGML/GGUF Import](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-3.md): Import quantized LLMs from GGUF files — architecture handlers, quantization codecs, adaptive quantization, round-trip export, and pipeline modules
- [ONNX Runtime](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-4.md): Direct ONNX model inference via ONNX Runtime 1.10 — no conversion to SameDiff required
- [TensorFlow](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-5.md): Running TensorFlow frozen graphs directly via JavaCPP TF bindings
- [TensorFlow Lite](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-5/tensorflow-lite.md): TensorFlow Lite 2.8 inference for mobile and edge deployment
- [Apache TVM](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-6.md): Apache TVM 0.8 integration for optimized model inference
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/python4j/overview.md): Embedding CPython in JVM applications — what Python4J is, use cases, and architecture
- [Getting Started](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/python4j/getting-started.md): Setting up Python4J — Maven dependencies, executing Python code, and variable I/O
- [NumPy Bridge](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/python4j/numpy-bridge.md): Zero-copy data exchange between INDArray and numpy.ndarray via python4j-numpy
- [Advanced Usage](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/python4j/advanced.md): Advanced Python4J features — GIL management, context managers, garbage collection, and subprocess mode
- [Overview](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/omnihub/overview.md): OmniHub model registry — downloading and using pretrained models from the DL4J ecosystem
- [Usage](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/omnihub/usage.md): Downloading and loading models from OmniHub — OmniHubUtils API
- [Available Models](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/omnihub/available-models.md): Pretrained models available through OmniHub — DL4J and SameDiff model catalog
- [Maven Setup](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/maven.md): Maven dependencies for Deeplearning4j — BOM, backend selection, platform classifiers, and version management
- [Build Tools (Gradle, SBT)](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/build-tools.md): Configuring Deeplearning4j with Gradle, SBT, and other build tools
- [GPU and CPU Setup](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/gpu-cpu.md): Configuring GPU and CPU backends — CUDA setup, multi-GPU, CPU optimizations, and backend switching
- [cuDNN](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/cudnn.md): cuDNN integration — installation, configuration, and performance benefits
- [Memory Configuration](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/memory.md): JVM memory flags, off-heap configuration, and memory management for ND4J and DL4J
- [Workspaces](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/workspaces.md): WorkspaceMode configuration for training and inference memory management
- [Performance Debugging](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/performance-debugging.md): Diagnosing and resolving performance issues in DL4J and ND4J — profiling, OpProfiler, ETL bottlenecks, GC overhead, and backend verification.
- [GraalVM Native Image](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/graalvm.md): Build ND4J and DL4J applications as GraalVM native executables for millisecond startup — serverless, edge, and single-binary deployment
- [Snapshots and Daily Builds](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/configuration/snapshots.md): Using nightly snapshot builds of Eclipse Deeplearning4j 1.0.0-rewrite — repository configuration, version identifiers, Maven and Gradle setup.
- [1.0.0-rewrite](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-rewrite.md): Release notes for the Deeplearning4j 1.0.0-rewrite: what changed from M2.1, the DSP execution engine, SameDiff as the core runtime, multi-GPU execution, model import expansion, hardware backends, and
- [1.0.0-M2.1](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-m2.1.md)
- [1.0.0-M2](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-m2.md)
- [1.0.0-M1.1](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-m1.1.md)
- [1.0.0-M1](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-m1.md)
- [1.0.0-beta7](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-beta7.md)
- [1.0.0-beta6](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-beta6.md)
- [1.0.0-beta5](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-beta5.md)
- [1.0.0-beta4](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-beta4.md)
- [1.0.0-beta3](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-beta3.md)
- [1.0.0-beta2](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-beta2.md)
- [1.0.0-beta](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-beta.md)
- [1.0.0-alpha](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/release-notes/1.0.0-alpha.md)