Difference between revisions of "PyTorch"
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| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
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| − | [ | + | [https://www.youtube.com/results?search_query=PyTorch YouTube] |
| − | [ | + | [https://www.quora.com/search?q=PyTorch ... Quora] |
| + | [https://www.google.com/search?q=PyTorch ...Google search] | ||
| + | [https://news.google.com/search?q=PyTorch ...Google News] | ||
| + | [https://www.bing.com/news/search?q=PyTorch&qft=interval%3d%228%22 ...Bing News] | ||
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| + | * [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with 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 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]] | ||
| + | * [[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://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.marktechpost.com/2021/10/24/microsoft-ai-open-sources-pytorch-directml-a-package-to-train-machine-learning-models-on-gpus/ 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 [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU|GPU]] acceleration and Deep [[Neural Network]]s 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. | |
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Latest revision as of 12:42, 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.
