Eclipse Deeplearning4j
Comprehensive documentation for Eclipse Deeplearning4j and its ecosystem — ND4J, SameDiff, DataVec, Keras Import, Python4J, and more
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Comprehensive documentation for Eclipse Deeplearning4j and its ecosystem — ND4J, SameDiff, DataVec, Keras Import, Python4J, and more
Eclipse Deeplearning4j is an open-source, distributed deep learning framework for the JVM. It provides a complete ecosystem for building, training, and deploying neural networks in Java and Scala, with native GPU acceleration via CUDA and high-performance CPU computation via OpenBLAS and oneDNN.
New to Deeplearning4j? Start here:
Ecosystem Overview — understand how all the libraries fit together
Quickstart — build and train your first neural network in 10 minutes
Maven Setup — add DL4J to your project
Beginners Guide — learning resources and roadmap
Before diving into specific libraries, review the foundational concepts:
Tensors and NDArrays — the fundamental data structure
Data Pipelines — loading and transforming data for training
Neural Network Fundamentals — layers, activations, loss functions
The Training Loop — how training works end-to-end
Evaluation — measuring model performance
Memory and Workspaces — off-heap memory management
Configure Maven/Gradle dependencies
Set up GPU acceleration
Train a CNN for image classification
Train an RNN/LSTM for sequences
Import a Keras model
Import a TensorFlow model
Import an ONNX model
Run distributed training on Spark
Use pretrained models
Debug memory issues
Call Python from Java
This documentation covers Deeplearning4j 1.0.0-M2.1 (current). Use the version selector in the sidebar or the links below to access documentation for other releases.
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