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Comparison of deep learning software - Wikipedia Comparison of deep learning software The following tables compare notable software frameworks, libraries, and computer programs for deep learning applications
PyTorch - Wikipedia 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
Torch (machine learning) - Wikipedia Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua [3] It provides LuaJIT interfaces to deep learning algorithms implemented in C It was created by the Idiap Research Institute at EPFL Torch development moved in 2017 to PyTorch, a port of the library to Python [4][5][6]
Recurrent neural network - Wikipedia MXNet: an open-source deep learning framework used to train and deploy deep neural networks PyTorch: Tensors and Dynamic neural networks in Python with GPU acceleration
PyTorch Lightning - Wikipedia PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce
DeepSpeed - Wikipedia DeepSpeed is an open source deep learning optimization library for PyTorch [1]
Transformer (deep learning) - Wikipedia The transformer model has been implemented in standard deep learning frameworks such as TensorFlow and PyTorch Transformers is a library produced by Hugging Face that supplies transformer-based architectures and pretrained models
Deep reinforcement learning - Wikipedia Depiction of a basic artificial neural network Deep learning is a form of machine learning that transforms a set of inputs into a set of outputs via an artificial neural network Deep learning methods, often using supervised learning with labeled datasets, have been shown to solve tasks that involve handling complex, high-dimensional raw input data (such as images) with less manual feature
Tensor Processing Unit - Wikipedia Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale
Computer vision - Wikipedia Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos From the perspective of engineering, it seeks to automate tasks that the human visual system can do [5][6][7] "Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or