- TensorFlow: An open-source library for machine learning, developed by Google, that allows for the deployment of computations to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
- Keras: An open-source library for building neural networks in Python, which runs on top of TensorFlow, Theano, and CNTK.
- PyTorch: An open-source machine learning library for Python, developed by Facebook, that provides a wide range of tools for building and training neural networks.
- Caffe: A deep learning framework developed by Berkeley AI Research and by community contributors, it is written in C++, with a Python interface.
- Theano: An open-source numerical computation library for Python, that allows the optimization of mathematical expressions involving multi-dimensional arrays.
- MXNet: An open-source deep learning framework for efficient and flexible research, it allows to run models on a variety of devices, from cloud to edge.
- Chainer: An open-source deep learning framework for Python, it allows the creation of complex neural networks with a high-level, intuitive interface.
- Deeplearning4j: A library for deep learning in Java and Scala, it supports various neural network architectures and tools for distributed training.
- Torch: A scientific computing framework for Lua, it provides a wide range of algorithms for deep learning, including neural networks, and support for GPU acceleration.
- DLib: A library for machine learning and computer vision in Python, it includes support for deep learning, including a deep neural network implementation and tools for training and deploying deep learning models.
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