Lazy loaded imageAwesome-AI-Tutorials

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This page will be consistently updated with best resources,

Algorithms

  1. Algorithms
      2. GitHub - landerrosette/algs4: Algorithms, 4th edition textbook code in C++
      Algorithms, 4th edition textbook code in C++. Contribute to landerrosette/algs4 development by creating an account on GitHub.
      GitHub - landerrosette/algs4: Algorithms, 4th edition textbook code in C++
      https://github.com/landerrosette/algs4
      GitHub - landerrosette/algs4: Algorithms, 4th edition textbook code in C++

Machine/Deep Learning

  1. Annotated Deep Learning Papers
      2. GitHub - labmlai/annotated_deep_learning_paper_implementations: 🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
      🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga...
      GitHub - labmlai/annotated_deep_learning_paper_implementations: 🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
      https://github.com/labmlai/annotated_deep_learning_paper_implementations
      GitHub - labmlai/annotated_deep_learning_paper_implementations: 🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
  1. Machine Learning Playground
      2. GitHub - lujiazho/MachineLearningPlayground: Implementation of basic mathematical pattern recognition/machine learning techniques for fun
      Implementation of basic mathematical pattern recognition/machine learning techniques for fun - lujiazho/MachineLearningPlayground
      GitHub - lujiazho/MachineLearningPlayground: Implementation of basic mathematical pattern recognition/machine learning techniques for fun
      https://github.com/lujiazho/MachineLearningPlayground
      GitHub - lujiazho/MachineLearningPlayground: Implementation of basic mathematical pattern recognition/machine learning techniques for fun
  1. Microsoft AI Education Community
      2. GitHub - microsoft/ai-edu: AI education materials for Chinese students, teachers and IT professionals.
      AI education materials for Chinese students, teachers and IT professionals. - microsoft/ai-edu
      GitHub - microsoft/ai-edu: AI education materials for Chinese students, teachers and IT professionals.
      https://github.com/microsoft/ai-edu
      GitHub - microsoft/ai-edu: AI education materials for Chinese students, teachers and IT professionals.

Diffusion Models/Flow/Score/SB

  1. The Principles of Diffusion Models: From Origins to Advances
      2. The Principles of Diffusion Models
      This monograph presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematical ideas....
      The Principles of Diffusion Models
      https://arxiv.org/abs/2510.21890
      The Principles of Diffusion Models

Reinforcement Learning

  1. Mathematical-Foundation-of-Reinforcement-Learning
      2. GitHub - MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning: This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
      This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning." - MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning
      GitHub - MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning: This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
      https://github.com/MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning
      GitHub - MathFoundationRL/Book-Mathematical-Foundation-of-Reinforcement-Learning: This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
  1. Reinforcement Learning: An Overview
      2. Reinforcement Learning: An Overview
      This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based methods, policy-based methods,...
      Reinforcement Learning: An Overview
      https://arxiv.org/abs/2412.05265
      Reinforcement Learning: An Overview

(Agentic) Large Language Models

  1. Foundations of Large Language Models
      2. Foundations of Large Language Models
      This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book...
      Foundations of Large Language Models
      https://arxiv.org/abs/2501.09223
      Foundations of Large Language Models
  1. LLMForEverybody
    Github
    LLMForEverybody
    Owner
    luhengshiwo
    Updated
    Dec 14, 2025
      2. GitHub - luhengshiwo/LLMForEverybody: 每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈
      每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈. Contribute to luhengshiwo/LLMForEverybody development by creating an account on GitHub.
      GitHub - luhengshiwo/LLMForEverybody: 每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈
      https://github.com/luhengshiwo/LLMForEverybody
      GitHub - luhengshiwo/LLMForEverybody: 每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈
  1. dive-into-llms
    Github
    dive-into-llms
    Owner
    Lordog
    Updated
    Dec 14, 2025
      2. GitHub - Lordog/dive-into-llms: 《动手学大模型Dive into LLMs》系列编程实践教程
      《动手学大模型Dive into LLMs》系列编程实践教程. Contribute to Lordog/dive-into-llms development by creating an account on GitHub.
      GitHub - Lordog/dive-into-llms: 《动手学大模型Dive into LLMs》系列编程实践教程
      https://github.com/Lordog/dive-into-llms
      GitHub - Lordog/dive-into-llms: 《动手学大模型Dive into LLMs》系列编程实践教程
  1. llm-action
    Github
    llm-action
    Owner
    liguodongiot
    Updated
    Dec 14, 2025
      2. GitHub - liguodongiot/llm-action: 本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
      本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）. Contribute to liguodongiot/llm-action development by creating an account on GitHub.
      GitHub - liguodongiot/llm-action: 本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
      https://github.com/liguodongiot/llm-action
      GitHub - liguodongiot/llm-action: 本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
  1. Agentic_Design_Patterns
      2. GitHub - sarwarbeing-ai/Agentic_Design_Patterns: Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems by Antonio Gulli
      Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems by Antonio Gulli - sarwarbeing-ai/Agentic_Design_Patterns
      GitHub - sarwarbeing-ai/Agentic_Design_Patterns: Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems by Antonio Gulli
      https://github.com/sarwarbeing-ai/Agentic_Design_Patterns
      GitHub - sarwarbeing-ai/Agentic_Design_Patterns: Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems by Antonio Gulli

AI Usage Skills

  1. Awesome Claude Skills
      2. GitHub - BehiSecc/awesome-claude-skills: A curated list of Claude Skills.
      A curated list of Claude Skills. Contribute to BehiSecc/awesome-claude-skills development by creating an account on GitHub.
      GitHub - BehiSecc/awesome-claude-skills: A curated list of Claude Skills.
      https://github.com/BehiSecc/awesome-claude-skills/
      GitHub - BehiSecc/awesome-claude-skills: A curated list of Claude Skills.

YouTube AI Channels

  1. Andrej Karpathy
      2. Andrej Karpathy
      Share your videos with friends, family, and the world
      Andrej Karpathy
      https://www.youtube.com/andrejkarpathy
      Andrej Karpathy
  1. sentdex
      2. sentdex
      Python Programming tutorials, going further than just the basics. Learn about machine learning, finance, data analysis, robotics, web development, game development and more. I have ~1000 videos, all of which do not fit on the front page. Try searching on the channel page, or via https://pythonprogramming.net. If you cannot find something, just ask! Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6 Paypal donations: [email protected]
      sentdex
      https://youtube.com/@sentdex
      sentdex
  1. Sebastian Raschka
      2. Sebastian Raschka
      ML/AI researcher & former stats professor turned LLM research engineer. Author of "Build a Large Language Model From Scratch" (amzn.to/4fqvn0D) & reasoning (mng.bz/Nwr7).
      Sebastian Raschka
      https://www.youtube.com/@SebastianRaschka
      Sebastian Raschka
  1. Jeremy Howard
      2. Jeremy Howard
      Deep learning is transforming the world. We are making deep learning easier to use and getting more people from all backgrounds involved through our free courses for coders, software library, cutting-edge research, and community. The world needs everyone involved with AI, no matter how unlikely your background. We want to make deep learning as accessible as possible.
      Jeremy Howard
      https://www.youtube.com/@howardjeremyp
      Jeremy Howard
  1. MIT OpenCourseWare
      2. MIT OpenCourseWare
      A free and open online publication of educational material from thousands of MIT courses, covering the entire MIT curriculum, ranging from introductory to the most advanced graduate courses. On the OCW website, each course includes a syllabus, instructional material like notes and reading lists, and learning activities like assignments and solutions. Some courses also have videos, online textbooks, and faculty insights on teaching. Knowledge is your reward. There's no signup or enrollment, and no start or end dates. OCW is self-paced learning at its best. Whether you’re a student, a teacher, or simply a curious person that wants to learn, MIT OpenCourseWare (OCW) offers a wealth of insight, inspiration, videos, and a whole lot more! Get the full picture on the OCW website at https://ocw.mit.edu. Accessibility: https://accessibility.mit.edu/ User comments policy: https://ocw.mit.edu/comments/ (Channel banner photo by Nietnagel on Flickr: https://flic.kr/p/8WXxfK.)
      MIT OpenCourseWare
      https://www.youtube.com/@mitocw
      MIT OpenCourseWare
  1. Stanford Online
      2. Stanford Online
      You can gain access to a world of education through Stanford Online, the Stanford School of Engineering’s portal for academic and professional education offered by schools and units throughout Stanford University. https://online.stanford.edu/ Our robust catalog of degree programs, credit-bearing education, professional certificate programs, and free and open content is developed by Stanford faculty, enabling you to expand your knowledge, advance your career, and enhance your life. Stanford Online is operated and managed by the Stanford Engineering Center for Global & Online Education (CGOE). CGOE expands access to Stanford teaching and research, working in collaboration with faculty in the School of Engineering and throughout Stanford University to design and deliver extensive global, online, and enterprise education to a global audience.
      Stanford Online
      https://www.youtube.com/@stanfordonline
      Stanford Online
  1. StatQuest with Josh Starmer
      2. StatQuest with Josh Starmer
      Statistics, Machine Learning, Data Science, and AI seem like very scary topics, but since each technique is really just a combination of small and simple steps, they are actually quite simple. My goal with StatQuest is to break down the major methodologies into easy-to-understand pieces. That said, I don't dumb down the material. Instead, I build up your understanding so that you are smarter. Contact, Video Index, Etc: https://statquest.org
      StatQuest with Josh Starmer
      https://www.youtube.com/@statquest
      StatQuest with Josh Starmer
  1. 3Blue1Brown
      2. 3Blue1Brown
      My name is Grant Sanderson. Videos here cover a variety of topics in math, or adjacent fields like physics and CS, all with an emphasis on visualizing the core ideas. The goal is to use animation to help elucidate and motivate otherwise tricky topics, and for difficult problems to be made simple with changes in perspective. For more information, other projects, FAQs, and inquiries see the website: https://www.3blue1brown.com
      3Blue1Brown
      https://youtube.com/@3blue1brown
      3Blue1Brown
  1. Krish Naik
      2. Krish Naik
      I am the Founder of KrishAI Technologies Private Limited and my experience is pioneering in machine learning, deep learning, and computer vision,an educator, and a mentor, with over 10 years' experience in the industry. This is my YouTube channel where I explain various topics on machine learning, deep learning, and AI with many real-world problem scenarios. I have delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums. My main aim is to make everyone familiar of ML and AI.Please subscribe and support the channel. As i love new technology, all these videos are free and I promise to make more interesting content as we go ahead. For any collaboration drop me a mail at [email protected] Please free to drop a mail for Product unboxing, GPU's unboxing and any other collaboration
      Krish Naik
      https://www.youtube.com/@krishnaik06
      Krish Naik
  1. CampusX
      2. CampusX
      Hello, I'm Nitish Singh, the founder of CampusX, your online gateway into the world of data science! At CampusX, we believe that quality education is a universal right, not a privilege. We aim to provide TOP TIER data education to everyone with a passion to learn. On our YouTube channel, we provide high quality free content covering everything related to data. Apart from this, we also provide paid mentorship programs for students where enrolled students get the benefit of personalized guidance through live and recorded video lectures, daily skill-building activities, hands-on projects, assignments, and interactions with industry experts. Leveraging my 10 years of teaching experience in the data industry, primarily in Machine/Deep Learning, I bring real-life industry experiences directly to your learning journey. Explore our video courses at https://learnwith.campusx.in/ for in-depth technology lessons. For all business inquiries, please reach out to [email protected]
      CampusX
      https://www.youtube.com/@campusx-official
      CampusX
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Article List
LLM Learning Roadmap
✨ Awesome-Anything
Lazy loaded image Awesome-AI-Tutorials
Lazy loaded image Awesome-AI-Researchs
Lazy loaded image Awesome-GitHub-Repos
Lazy loaded image Awesome-AI-Examples
🖼️ Digital Image Processing
Lazy loaded image DIP I Image Demosaicing and Histogram Manipulation
Lazy loaded image DIP II Image Denoising
Lazy loaded image DIP III Edge Detection
Lazy loaded image DIP IV Digital Half-toning
Lazy loaded image DIP V Geometric Image Modification
🍃 LLM Components
Lazy loaded image 1.1 Tokenization
Lazy loaded image 1.2 Embedding
Lazy loaded image 1.3 Attention
Lazy loaded image 1.4 FFN, Residual Addition, and LN
Lazy loaded image 1.5 Positional Encoding
Lazy loaded image 1.6 Architecture & Decoding Policy
🌱 LLM Pre-training
Lazy loaded image 2.1 Pre-training Definition
Lazy loaded image 2.2 Pre-training Data
Lazy loaded image 2.3 Pre-training Workflow
Lazy loaded image 2.4 Continued Pre-training (CPT)
Lazy loaded image 2.5 Pre-training Evaluation
☘️ LLM Post-Training
Lazy loaded image 3.1 Supervised Fine-Tuning (SFT)
Lazy loaded image 3.2 Reinforcement Learning
Lazy loaded image 3.3 RLHF: Reinforcement Learning from Human Feedback
Lazy loaded image 3.4 Optimizing PPO-Based Algorithms
Lazy loaded image 3.5 Direct Preference Optimization (DPO)
🍀 LLM Popular Models
Lazy loaded image 4.1 BERT & Variants
Lazy loaded image 4.2 PaLM Series
Lazy loaded image 4.3 GPT Series
Lazy loaded image 4.4 LLaMA Series
Lazy loaded image 4.5 GLM Series
Lazy loaded image 4.6 Qwen Series
Lazy loaded image 4.7 DeepSeek Series
Lazy loaded image 4.8 MOE Series
Lazy loaded image 4.9 Other
🪴 LLM Applications
Lazy loaded image 5.1 Prompt Engineering
Lazy loaded image 5.2 LLM-based Agents
Lazy loaded image 5.3 Retrieval-Augmented Generation (RAG)
Lazy loaded image 5.4 Enterprise-level RAG Practice (I)
Lazy loaded image 5.5 Enterprise-level RAG Practice (II)
🌿 LLM Optimization
Lazy loaded image 6.1 GPU Usage Analysis
Lazy loaded image 6.2 Latency Analysis for Inference
Lazy loaded image 6.3 FlashAttention
Lazy loaded image 6.4 PagedAttention
Lazy loaded image 6.5 Packing for Efficient Training
Lazy loaded image 6.6 PEFT
🌾 LLM Compression
Lazy loaded image 7.1 Quantization
Lazy loaded image 7.2 Pruning
Lazy loaded image 7.3 Knowledge Distillation
🌵 LLM Hands-on Practice
Lazy loaded image 8.1 nanoGPT: Training a GPT from Scratch
Lazy loaded image 8.2 Instruction Fine-Tuning Qwen3-0.6B: A Minimal Working Example
Lazy loaded image 8.3 OpenRLHF: SFT Qwen3-0.6B and Code Analysis
Lazy loaded image 8.4 OpenRLHF: Reward Model Training and Code Analysis
Lazy loaded image 8.5 OpenRLHF: PPO Tutorial and Code Analysis
🌴 LLM Must-read Papers
Lazy loaded image 9.1 Technical Reports Explained
Lazy loaded image 9.2 MedLLM
Lazy loaded image 9.3 o1 Techinical Paper
Lazy loaded image 9.4 Prompting
Lazy loaded image 9.5 LLM-as-Judge
🌳 LLM Q&A
Lazy loaded image 10.1 Why GRPO training loss starts at 0
Lazy loaded image 10.2 VLM-R1 Reproduction Results
Lazy loaded image 10.3 KL(P||Q) or KL(Q||P)?
Lazy loaded image 10.4 Left Padding or Right Padding?
Lazy loaded image 10.5 What is QK-Norm?
Lazy loaded image 10.6 Why LLM training rarely adopts Dropout?
Lazy loaded image 10.7 Dropout and DropPath
🐝 VLM Image Encoders
Lazy loaded image 11.0 Introduction
Lazy loaded image 11.1 Vision Transformer
Lazy loaded image 11.2 CLIP
Lazy loaded image 11.3 BLIP
Lazy loaded image 11.4 EVA
Lazy loaded image 11.6 BLIP-2
Lazy loaded image 11.7 SigLIP
📝 MISC.
🤔 Large Language Models Pass the Turing Test.
Lazy loaded image OpenAI ChatGPT Progression
⏳ Claude Sonnet 4 Test with Sandtris
🔍 What DeepSeek-R1's Surge Teaches Us About SEO
💰 CivitAI’s Payment Issue
🎓 arXivSub, an Academia Assistant