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{{Machine learning}}
'''PyTorch''' is a [[machine learning]] [[Library (computing)|library]] based on the [[Torch (machine learning)|Torch]] library,<ref>{{cite news|url=https://www.infoworld.com/article/3159120/artificial-intelligence/facebook-brings-gpu-powered-machine-learning-to-python.html|title=Facebook brings GPU-powered machine learning to Python|last=Yegulalp|first=Serdar|date=19 January 2017|work=InfoWorld|access-date=11 December 2017}}</ref><ref>{{cite web|url=https://www.oreilly.com/ideas/why-ai-and-machine-learning-researchers-are-beginning-to-embrace-pytorch|title=Why AI and machine learning researchers are beginning to embrace PyTorch|last=Lorica|first=Ben|date=3 August 2017|publisher=O'Reilly Media|access-date=11 December 2017}}</ref><ref>{{Cite book|title=Deep Learning with Python|last=Ketkar|first=Nikhil|date=2017|publisher=Apress, Berkeley, CA|isbn=9781484227657|pages=195–208|language=en|doi=10.1007/978-1-4842-2766-4_12|chapter=Introduction to PyTorch}}</ref> used for applications such as [[computer vision]] and [[natural language processing]],<ref>{{Cite web|url=https://www.datacamp.com/tutorial/nlp-with-pytorch-a-comprehensive-guide|title=NLP with PyTorch: A Comprehensive Guide|author=Moez Ali|date=Jun 2023|website=datacamp.com|language=en|access-date=2024-04-01}}</ref> originally developed by [[Meta AI]] and now part of the [[Linux Foundation]] umbrella.<ref>{{Cite news|url=https://www.oreilly.com/ideas/when-two-trends-fuse-pytorch-and-recommender-systems|title=When two trends fuse: PyTorch and recommender systems|last=Patel|first=Mo|date=2017-12-07|work=O'Reilly Media|access-date=2017-12-18|language=en}}</ref><ref>{{Cite news|url=https://techcrunch.com/2017/09/07/facebook-and-microsoft-collaborate-to-simplify-conversions-from-pytorch-to-caffe2/|title=Facebook and Microsoft collaborate to simplify conversions from PyTorch to Caffe2|last=Mannes|first=John|work=[[TechCrunch]]|access-date=2017-12-18|language=en|quote=FAIR is accustomed to working with PyTorch – a deep learning framework optimized for achieving state of the art results in research, regardless of resource constraints. Unfortunately in the real world, most of us are limited by the computational capabilities of our smartphones and computers.}}</ref><ref>{{Cite web|url=https://venturebeat.com/2017/11/29/tech-giants-are-using-open-source-frameworks-to-dominate-the-ai-community/|title=Tech giants are using open source frameworks to dominate the AI community|last=Arakelyan|first=Sophia|date=2017-11-29|website=[[VentureBeat]]|language=en-US|access-date=2017-12-18}}</ref><ref>{{Cite web |title=PyTorch strengthens its governance by joining the Linux Foundation |url=https://pytorch.org/blog/PyTorchfoundation/ |access-date=2022-09-13 |website=pytorch.org |language=en}}</ref> It is one of the most popular [[deep learning]] frameworks, alongside others such as [[TensorFlow]] and PaddlePaddle<ref>{{Cite web|url=https://github.com/cncf/velocity|title=Top 30 Open Source Projects.|website=Open Source Project Velocity by CNCF|access-date=2023-10-12}}</ref><ref>{{Cite web|url=https://github.com/PaddlePaddle/Paddle|title=Welcome to the PaddlePaddle GitHub.|website=PaddlePaddle Official Github Repo|access-date=2024-10-28}}</ref>, offering [[free and open-source software]] released under the [[modified BSD license]]. Although the [[Python (programming language)|Python]] interface is more polished and the primary focus of development, PyTorch also has a [[C++]] interface.<ref>{{Cite web|url=https://pytorch.org/cppdocs/frontend.html|title=The C++ Frontend|website=PyTorch Master Documentation|access-date=2019-07-29}}</ref>.You can find a step-by-step tutorial for learning PyTorch on
https://pytorch.org/tutorials/beginner/basics/intro.html, which guides you through the basics to advanced concepts of deep learning using PyTorch. Additionally, you can watch this 26 hours helpful video: [https://www.freecodecamp.org/news/learn-pytorch-for-deep-learning-in-day/<nowiki>] to further enhance your understanding.</nowiki>
A number of pieces of [[deep learning]] software are built on top of PyTorch, including [[Tesla Autopilot]],<ref>{{Cite web|last=Karpathy|first=Andrej|title=PyTorch at Tesla - Andrej Karpathy, Tesla|website=[[YouTube]] |date=6 November 2019 |url=https://www.youtube.com/watch?v=oBklltKXtDE}}</ref> [[Uber]]'s Pyro,<ref>{{Cite news|url=https://eng.uber.com/pyro/|title=Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language|date=2017-11-03|work=Uber Engineering Blog|access-date=2017-12-18|language=en-US}}</ref> [[Hugging Face]]'s Transformers,<ref>{{Citation|title=PYTORCH-TRANSFORMERS: PyTorch implementations of popular NLP Transformers|date=2019-12-01|url=https://pytorch.org/hub/huggingface_pytorch-transformers/|publisher=PyTorch Hub|access-date=2019-12-01}}</ref> [[PyTorch Lightning]],<ref>{{Citation|title=PYTORCH-Lightning: The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate|date=2020-06-18|url=https://github.com/PyTorchLightning/pytorch-lightning/|publisher=Lightning-Team|access-date=2020-06-18}}</ref><ref>{{Cite web|url=https://pytorch.org/ecosystem/|title=Ecosystem Tools|website=pytorch.org|language=en|access-date=2020-06-18}}</ref> and Catalyst.<ref>{{Citation|title=GitHub - catalyst-team/catalyst: Accelerated DL & RL|date=2019-12-05|url=https://github.com/catalyst-team/catalyst|publisher=Catalyst-Team|access-date=2019-12-05}}</ref><ref>{{Cite web|url=https://pytorch.org/ecosystem/|title= Ecosystem Tools|website=pytorch.org|language=en|access-date=2020-04-04}}</ref>
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