Deep learning software

TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training and inference of neural networks. It is one of the most popular deep learning frameworks, alongside others such as PyTorch. It is free and open-source software released under the Apache License 2.0.

PyTorch is an open-source deep learning library, originally developed by Meta Platforms and currently developed with support from the Linux Foundation. The successor to Torch, PyTorch provides a high-level API that builds upon optimised, low-level implementations of deep learning algorithms and architectures, such as the Transformer, or SGD. Notably, this API simplifies model training and inference to a few lines of code. PyTorch allows for automatic parallelization of training and, internally, implements CUDA bindings that speed training further by leveraging GPU resources.

Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later added support for more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers, models, or metrics that can be used in native workflows in JAX, TensorFlow, or PyTorch — with one codebase." Keras 3 will be the default Keras version for TensorFlow 2.16 onwards, but Keras 2 can still be used.

thumb|362x362px|AlexNet architecture and a possible modification. At the top is half of the original AlexNet, which is divided into two halves, one for each GPU. At the bottom is the same architecture, but the final "projection" layer is replaced by another that projects to fewer outputs. If one freezes the remaining model and only fine-tunes the last layer, one can obtain another vision model at a significantly lower cost than training one from scratch. thumb|245x245px|LeNet (left) and AlexNet (right) block diagram AlexNet is a convolutional neural network architecture developed for image cla

Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.

free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain

deep learning framework

numerical computation library for Python

Professional application for data mining

deep learning software

multi-language machine learning library

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Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia.

cloud machine-learning platform