DL4J (Eclipse Deeplearning4j) is an open-source library for deep learning on the Java Virtual Machine (JVM). It allows neural networks to be trained and run in Java or Scala, so it fits into existing Java-based enterprise systems. The project also includes tools for importing models from other frameworks. It is maintained as a project of the Eclipse Foundation.
Deep learning is a branch of machine learning that uses artificial neural networks with many layers. Such networks learn from large amounts of data which features matter – from edges in images to relationships of meaning in texts. Deep learning underpins today's language models, image and speech recognition and machine translation, and usually requires considerable computing power and training data.
TensorFlow is an open-source machine learning framework developed by Google and released in 2015. It is used to build, train and deploy neural networks in applications, on servers or on mobile devices. Keras provides an accessible high-level interface; typical uses include image classification, forecasting and recommendation systems.
Keras is an open-source Python library for building and training neural networks, originally developed by François Chollet. It offers an accessible, high-level interface for assembling models from layers. Since version 3, Keras can run on JAX, TensorFlow or PyTorch as its computational backend. Example: a team builds a prototype image classification model in just a few lines of code.
PyTorch is an open-source framework for machine learning and deep learning in the Python programming language. It was originally developed at Meta (then Facebook) and has been stewarded since 2022 by the PyTorch Foundation under the umbrella of the Linux Foundation. PyTorch is widely used in research and industry because it lets developers build models flexibly, train them on GPUs and test them step by step; many well-known language and image models are built with it.
