DL4J (Deeplearning4j) is an ecosystem of libraries for developing deep learning applications on the Java Virtual Machine. It provides tools for constructing neural networks, performing numerical computations and preparing data for machine learning workflows.
The ecosystem includes several interconnected projects. Deeplearning4j provides a high-level neural network API, while ND4J supplies multidimensional arrays and numerical operations. SameDiff provides automatic differentiation and graph-based computation, and DataVec handles data transformation and preprocessing.
DL4J is designed for Java developers who need to integrate machine learning into existing JVM applications. Its libraries can also be used from other JVM languages, including Kotlin, Scala and Clojure.
The project is undergoing a substantial architectural transition. Its established high-level API is in maintenance mode, while development is shifting towards model interoperability, numerical computing and more modern deep learning workflows.
This is free and open source software.
Key Features
- High-level API for constructing and training neural networks using MultiLayerNetwork and ComputationGraph.
- Supports feedforward, convolutional and recurrent neural network architectures.
- Allows developers to define custom neural network layers and loss functions.
- ND4J provides multidimensional arrays and mathematical operations for numerical computing.
- Supports linear algebra, matrix operations and tensor transformations.
- Provides CPU and CUDA GPU acceleration through native numerical computing backends.
- SameDiff offers graph-based computation and automatic differentiation.
- Supports gradient calculation for training and optimising machine learning models.
- DataVec provides data transformation, preprocessing and feature extraction facilities.
- Supports processing structured data, images, audio and other machine learning inputs.
- Provides facilities for importing compatible Keras models.
- Includes facilities for importing supported TensorFlow graph models.
- Python4J enables interaction between JVM applications and embedded Python execution.
- Provides reusable Java APIs for integrating deep learning into existing applications.
- Cross-platform support – runs on Linux, macOS, and Windows.
Website: github.com/deeplearning4j/deeplearning4j
Support:
Developer: Eclipse
License: Apache License 2.0
DL4J is written in Java. Learn Java with our recommended free books and free tutorials.
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