Difference between revisions of "PyTorch"
m |
m |
||
| Line 22: | Line 22: | ||
* [[Libraries & Frameworks Overview]] ... [[Libraries & Frameworks]] ... [[Git - GitHub and GitLab]] ... [[Other Coding options]] | * [[Libraries & Frameworks Overview]] ... [[Libraries & Frameworks]] ... [[Git - GitHub and GitLab]] ... [[Other Coding options]] | ||
* [[Python]] ... [[Generative AI with Python]] ... [[Javascript]] ... [[Generative AI with Javascript]] | * [[Python]] ... [[Generative AI with Python]] ... [[Javascript]] ... [[Generative AI with Javascript]] | ||
| − | * [[Analytics]] ... [[ | + | * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Loop]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Bayes]] ... [[Network Pattern]] |
* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[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, Generators, Drag n' Drop]] ... [[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|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|Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] | ||
Revision as of 18:55, 5 July 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Libraries & Frameworks Overview ... Libraries & Frameworks ... Git - GitHub and GitLab ... Other Coding options
- Python ... Generative AI with Python ... Javascript ... Generative AI with Javascript
- Analytics ... Visualization ... Graphical Tools ... Loop ... Diagrams & Business Analysis ... Requirements ... Bayes ... Network Pattern
- Development ... Notebooks ... AI Pair Programming ... Codeless, Generators, Drag n' Drop ... AIOps/MLOps ... AIaaS/MLaaS
- Gaming ... Game-Based Learning (GBL) ... Security ... Generative AI ... Metaverse ... Quantum ... Game Theory
- 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.
