Difference between revisions of "PyTorch"
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* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | ||
* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]] | * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]] | ||
| − | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] | + | * [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]] |
* [http://pytorch.org/ PyTorch] ...[http://pytorch.org/resources resources] ...[http://github.com/pytorch GitHub] | * [http://pytorch.org/ PyTorch] ...[http://pytorch.org/resources resources] ...[http://github.com/pytorch GitHub] | ||
* [http://www.infoq.com/news/2019/11/State-Machine-Learning-fw-2019/ PyTorch and TensorFlow: Which ML Framework is More Popular in Academia and Industry | Alex Giamas - InfoQ] ...[http://github.com/Chillee/pytorch-vs-tensorflow code used to generate the datasets and also interactive charts from the article |] [http://twitter.com/cHHillee Horace He] | * [http://www.infoq.com/news/2019/11/State-Machine-Learning-fw-2019/ PyTorch and TensorFlow: Which ML Framework is More Popular in Academia and Industry | Alex Giamas - InfoQ] ...[http://github.com/Chillee/pytorch-vs-tensorflow code used to generate the datasets and also interactive charts from the article |] [http://twitter.com/cHHillee Horace He] | ||
Latest revision as of 11:42, 6 November 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- Python ... GenAI w/ Python ... JavaScript ... GenAI w/ JavaScript ... TensorFlow ... PyTorch
- Libraries & Frameworks Overview ... Libraries & Frameworks ... Git - GitHub and GitLab ... Other Coding options
- Train Large Language Model (LLM) From Scratch
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Development ... Notebooks ... AI Pair Programming ... Codeless ... Hugging Face ... AIOps/MLOps ... AIaaS/MLaaS
- Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Games - Metaverse ... Quantum ... Game Theory ... Design
- PyTorch ...resources ...GitHub
- PyTorch and TensorFlow: Which ML Framework is More Popular in Academia and Industry | Alex Giamas - InfoQ ...code used to generate the datasets and also interactive charts from the article | Horace He
- Microsoft AI Open-Sources ‘PyTorch-DirectML’: A Package To Train Machine Learning Models On GPUs | Asif Razzaq - Marketechpost
PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It is free and open-source software released under the modified BSD license. PyTorch provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration and Deep Neural Networks built on a tape-based autograd system. It is written in Python and is relatively easy for most machine learning developers to learn and use.
In PyTorch, the tape-based autograd system is a technique used to compute gradients efficiently and it happens to be used by backpropagation. Autograd is the core torch package for automatic differentiation1. A simple explanation of reverse-mode automatic differentiation can be found in this PyTorch forum post. PyTorch’s Autograd feature is part of what makes PyTorch flexible and fast for building machine learning projects.
