Chainer is an open-source deep learning framework for Python, developed by the Japanese company Preferred Networks. It popularised the 'define-by-run' approach, in which the neural network is built while the computation runs, making it easier to experiment with flexible model structures. Since December 2019 Chainer has only been maintained, not further developed; Preferred Networks moved to PyTorch, which follows the same principle.
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.
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.
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.
Apache MXNet was an open-source deep learning framework that could train models efficiently across multiple GPUs and machines, with interfaces for Python, C++, R, Scala and other languages. Amazon Web Services backed it as its preferred framework for some years. The Apache Software Foundation retired the project in September 2023 and moved it to the Apache Attic by February 2024; it is no longer developed. Existing models are usually migrated to other frameworks.
