PyTorch

PyTorch is an open-source machine learning (ML) framework based on the Python programming language and the Torch library. It’s primarily used for creating deep neural networks and is one of the preferred platforms for deep learning research. The framework is designed to expedite the process from research prototyping to deployment and supports over 200 different mathematical operations[1].

PyTorch is known for its pythonic nature, meaning it follows the coding style that uses Python’s unique features to write readable code. It also uses dynamic computation graphs, which allow developers, scientists, and neural network debuggers to run and test a portion of code in real time[1].

The basic building block of PyTorch is the tensor, which is similar to an array or matrix. These tensors encode the inputs and outputs of models, as well as their parameters[5].

PyTorch was originally developed by Meta AI and is now part of the Linux Foundation umbrella. It is free and open-source software released under the modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface[3].

Several pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, Uber’s Pyro, Hugging Face’s Transformers, PyTorch Lightning, and Catalyst[3]. Other notable applications of PyTorch include its use in building natural language processing (NLP) models, such as those used by Airbnb in their customer service department to create smart replies and recommend agent actions[5].

Citations:

[1] https://www.techtarget.com/searchenterpriseai/definition/PyTorch

[2] https://www.nvidia.com/en-us/glossary/pytorch/

[3] https://en.wikipedia.org/wiki/PyTorch

[4] https://www.simplilearn.com/what-is-pytorch-article

[5] https://builtin.com/machine-learning/pytorch

[6] https://deepai.org/machine-learning-glossary-and-terms/pytorch

[7] https://pytorch.org/tutorials/recipes/recipes/defining_a_neural_network.html

[8] https://pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html