PyTorch vs TensorFlow: What are the differences?
Developers describe PyTorch as "A deep learning framework that puts Python first". PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. On the other hand, TensorFlow is detailed as "Open Source Software Library for Machine Intelligence". TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch and TensorFlow belong to "Machine Learning Tools" category of the tech stack.
"Developer Friendly" is the top reason why over 2 developers like PyTorch, while over 16 developers mention "High Performance" as the leading cause for choosing TensorFlow.
PyTorch is an open source tool with 29.6K GitHub stars and 7.18K GitHub forks. Here's a link to PyTorch's open source repository on GitHub.
Uber Technologies, 9GAG, and StyleShare Inc. are some of the popular companies that use TensorFlow, whereas PyTorch is used by Suggestic, cotobox, and Depop. TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to PyTorch, which is listed in 21 company stacks and 46 developer stacks.
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What is PyTorch?
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