12.7K ·GitHub
What is this?
A curated list of tutorials, papers, projects, libraries, videos, books and communities related to PyTorch, covering everything from basic deep learning to LLMs, agentic AI, and reinforcement learning.
A curated list of tutorials, papers, projects, libraries, videos, books and communities related to PyTorch, covering everything from basic deep learning to LLMs, agentic AI, and reinforcement learning.Is this a library or just a list of links?It is purely a curated index of links organized into a table of contents by topic (LLMs, agentic AI, computer vision, NLP, etc.), not a library or package you install.
It is purely a curated index of links organized into a table of contents by topic (LLMs, agentic AI, computer vision, NLP, etc.), not a library or package you install.How is the list organized and where should I start?Entries are grouped under a large Table of Contents by subject area, starting with general PyTorch tutorials before moving into LLMs, agentic AI, guardrails, tabular data, vision, NLP and more. Beginners can start with the Official PyTorch Tutorials and 'Dive Into Deep Learning with PyTorch' at the top of the Tutorials section.
Entries are grouped under a large Table of Contents by subject area, starting with general PyTorch tutorials before moving into LLMs, agentic AI, guardrails, tabular data, vision, NLP and more. Beginners can start with the Official PyTorch Tutorials and 'Dive Into Deep Learning with PyTorch' at the top of the Tutorials section.Do I need anything installed to browse this list?No, it's just a README; you only need the dependencies required by whichever linked tutorial, library or paper you decide to try.
No, it's just a README; you only need the dependencies required by whichever linked tutorial, library or paper you decide to try.Are the entries vetted or kept up to date?The repo relies on community pull requests to add and maintain entries, so freshness and vetting depend on ongoing contributions rather than a formal review process.
The repo relies on community pull requests to add and maintain entries, so freshness and vetting depend on ongoing contributions rather than a formal review process.Who is this list for?PyTorch users and deep learning practitioners ranging from beginners looking for tutorials to researchers hunting for papers on niche topics like quantum ML, spiking neural networks or financial ML.
PyTorch users and deep learning practitioners ranging from beginners looking for tutorials to researchers hunting for papers on niche topics like quantum ML, spiking neural networks or financial ML.Does it cover large language models and AI agents too?Yes, there are dedicated sections for LLMs (training, finetuning, quantization, RAG) and Agentic AI (multi-agent systems, autonomous agents, planning/reasoning frameworks like LangGraph, AutoGen, CrewAI and AutoGPT).
Yes, there are dedicated sections for LLMs (training, finetuning, quantization, RAG) and Agentic AI (multi-agent systems, autonomous agents, planning/reasoning frameworks like LangGraph, AutoGen, CrewAI and AutoGPT).Is there anything on AI safety or guardrails?Yes, a 'Guardrails and AI Safety' section lists tools for content moderation, prompt injection defense, bias detection, privacy-preserving ML (Opacus, PySyft, CrypTen) and model interpretability (SHAP, Captum, LIME).
Yes, a 'Guardrails and AI Safety' section lists tools for content moderation, prompt injection defense, bias detection, privacy-preserving ML (Opacus, PySyft, CrypTen) and model interpretability (SHAP, Captum, LIME).Is there a separate resource just for AI agents?Yes, the README links out to a dedicated companion list called 'The Incredible AI Agents' for deeper coverage of that topic.
Yes, the README links out to a dedicated companion list called 'The Incredible AI Agents' for deeper coverage of that topic.How many topics does the list span?Dozens, from GANs, transformers, segmentation and reinforcement learning to time series, financial ML, quantum ML and spiking neural networks.
Dozens, from GANs, transformers, segmentation and reinforcement learning to time series, financial ML, quantum ML and spiking neural networks.What are similar projects?Machine-Learning-Tutorials, DeepLearning, Deep-Learning-Papers-Reading-Roadmap, awesome-deep-learning-papers, autumn-recruitment, ai-engineering-interview-questions-company-wise, llm_interview_note, LLM101n
Machine-Learning-Tutorials, DeepLearning, Deep-Learning-Papers-Reading-Roadmap, awesome-deep-learning-papers, autumn-recruitment, ai-engineering-interview-questions-company-wise, llm_interview_note, LLM101nWhich topics is it in?deep-learning-tutorial, deep-neural-networks, reinforcement-learning, quantization, fine-tuning, transformer, nlp, rag, computer-vision, multi-agent, learning-resources, pytorch
deep-learning-tutorial, deep-neural-networks, reinforcement-learning, quantization, fine-tuning, transformer, nlp, rag, computer-vision, multi-agent, learning-resources, pytorch