Deep learning

Software libraries

https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software

• Deeplearning4j — An open-source deep-learning library written for Java/C++ with LSTMs and convolutional networks. It provides parallelization with Spark on CPUs and GPUs.
• Gensim — A toolkit for natural language processing implemented in the Python programming language.
• Keras — An open-source deep learning framework for the Python programming language.
• Microsoft CNTK (Computational Network Toolkit) — Microsoft’s open-source deep-learning toolkit for Windows and Linux. It provides parallelization with CPUs and GPUs across multiple servers.
• MXNet — An open source deep learning framework that allows you to define, train, and deploy deep neural networks.
• OpenNN — An open source C++ library which implements deep neural networks and provides parallelization with CPUs.
• PaddlePaddle — An open source C++ /CUDA library with Python API for scalable deep learning platform with CPUs and GPUs, originally developed by Baidu.
• TensorFlow — Google’s open source machine learning library in C++ and Python with APIs for both. It provides parallelization with CPUs and GPUs.
• Theano — An open source machine learning library for Python supported by the University of Montreal and Yoshua Bengio’s team.
• Torch — An open source software library for machine learning based on the Lua programming language and used by Facebook.
• Caffe – Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
• DIANNE – A modular open-source deep learning framework in Java / OSGi developed at Ghent University, Belgium. It provides parallelization with CPUs and GPUs across multiple servers.