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A collection of learning resources

Anh-Thi Dinh
MOOCData ScienceDeep LearningDeepLearning.AIData EngineeringNLPStatisticsMathsPythonAWSBackendAPI & Services
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The resources listed in this note have not been verified yet. They are included so that they can be checked at a later time. The order is random.
β€œI failed my way to success” β€” Thomas Edition.
☝
Emoji notations: πŸ™Β (Github), πŸ“šΒ (Book / Files), 🟒 (I’ve noted/learned), πŸ“Ί (Youtube/Videos), ⭐ (My favorite), πŸŽ“Β (MOOC / Courses)

General

  • ⭐ 3Blue1Brown πŸ“Ί
  • 500+ Artificial Intelligence Project List with Code πŸ™
  • ⭐ Andrej Karpathy πŸ“Ί (Founding member of OpenAI)
  • AWS Machine Learning Blog
  • DeepLearning.AI: Start or Advance Your Career in AI
  • Deep Learning Drizzle πŸ™
  • Deep Learning Institute (DLI) Training and Certification | NVIDIA
  • ⭐ Developer Roadmaps - roadmap.sh
  • Distill β€” Latest articles about machine learning
  • From 0 to research scientist resources guide πŸ™
  • Google AI - Understanding AI: AI tools, training, and skills
  • Google Cloud Skills Boost
  • Hugging Face - Learn πŸŽ“
  • HΖ°α»›ng DαΊ«n Tα»± Học TrΓ­ Tuệ NhΓ’n TαΊ‘o πŸ“Ί
  • https://github.com/faridrashidi/kaggle-solutionskaggle-solutions β€” Collection of Kaggle Solutions and Ideas πŸ™
  • Learn Python, Data Viz, Pandas & More | Tutorials | Kaggle
  • Training | Microsoft Learn πŸŽ“
  • Understanding AI Models - IBM Technology πŸ“Ί

Learn how to learn

  • A Mind for Numbers - Dr. Barbara Oakley πŸ“š
  • How To Learn Any Skill So Fast It Feels Illegal - YouTube πŸ“Ί
  • How to Read a Book - Mortimer Adler πŸ“š
  • I spent the past year learning how to learn. Here are the key parts I have gathered so far! : r/GetStudying
  • Learning How To Learn - Dr. Barbara Oakley πŸ“š
  • Learning How to Learn: Powerful mental tools to help you master tough subjects β€” taught by Dr. Barbara Oakley (the author of books β€œLearning How To Learn” and β€œA Mind for Numbers”) πŸŽ“
  • Make It Stick by Brown, Roediger & McDaniel πŸ“š
  • Pragmatic Thinking and Learning: Refactor Your Wetware β€” Andy Hunt πŸ“š
  • Understanding How We Learn: A Visual Guide β€” Yana Weinstein, Megan Sumeracki, Oliver Caviglioli πŸ“š

Blog / Personal websites

  • ⭐ Andrej Karpathy (his wonderful youtube channel πŸ“Ί)
  • Denny's Blog
  • Home - colah's blog
  • Chip Huyen
  • Lil'Log
  • RaΓΊl GΓ³mez blog
  • ruder.io
  • serrano.academy πŸ“Ί (Youtube channel of Luis Serrano, the author of Grokking Machine Learning)
  • sentdex πŸ“Ί
  • StatQuest with Josh Starmer πŸ“Ί
  • vcubingx πŸ“ΊΒ (A Youtube channel like 3Blue1Brown, talk about Math and CS).
  • Yannic Kilcher πŸ“Ί

Coding platforms

  • CodeChef - Learn Coding / Practical Coding Courses by Experts
  • Hackerrank
  • https://github.com/faridrashidi/kaggle-solutionskaggle-solutions β€” Collection of Kaggle Solutions and Ideas πŸ™
  • ⭐ LeetCode

Computer Science

  • ⭐ CS50x 2025 - CS Course from Harvard University, taught by David J. Malan πŸ“Ί
  • ⭐ Developer Roadmaps - roadmap.sh
  • copding-interview-university πŸ™
  • Learn to Program: Crafting Quality Code Course by University of Toronto | Coursera πŸŽ“
  • Teach Yourself Computer Science

Data Structure & Algorithms

  • Abdul Bari channel
  • Algorithms, Part I | Coursera πŸŽ“
  • Algorithms, Part II Course (Princeton) | Coursera πŸŽ“
  • awesome-algorithms πŸ™Β 
  • Data Structures and Algorithms Specialization [6 courses] (UCSD) | Coursera πŸŽ“
  • PrincetonAlgorithms πŸ™
  • Self-taught DSA materials by YK Sugi (worked at Google, CS Dojo), sorted from beginning to advanced
    • Algorithms Specialization - Stanford πŸŽ“
    • Algorithms, Fourth Edition by Robert Sedgewick and. Kevin Wayne πŸ“š
    • Introduction to data structures - myucodeschool πŸ“Ί
    • Learn Data Structures and Alghorithms - Udacity πŸŽ“
    • MIT 6.006 Introduction to Algorithms, Fall 2011 πŸŽ“
    • The Algorithm Design Manual book by Steven S. Skiena πŸ“š

Design patterns

  • Designing Data-Intensive Applications by Martin Kleppmann πŸ“š
  • Design Patterns: Elements of Reusable Object-Oriented Software πŸ“š
  • Head First Design Patterns, 2nd Edition πŸ“š
  • python-patterns πŸ™
  • Refactoring and Design Patterns
  • System Design Interview πŸ“Ί

Computer Vision

  • CS231n Deep Learning for Computer Vision (lecture videos) πŸŽ“
  • Convolutions in Image Processing | Week 1, lecture 6 | MIT 18.S191 Fall 2020 (taught by the author of 3Blue1Brown) πŸ“ΊΒ πŸŽ“
  • Introduction to Computer Vision and Image Processing Course by IBM | Coursera πŸŽ“
Articles
  • Image Kernels explained visually
  • Object Detection for Dummies | Lil'Log: part 1, part 2, part 3, part 4.
  • [PDF] A guide to convolution arithmetic for deep learning by Vincent Dumoulin and Francesco Visin (animations) πŸ“š

Data Science

  • IBM Data Science Professional Certificate | Coursera πŸŽ“Β πŸŸ’ My notes
  • [Jupyter Notebook] data-science-ipython-notebooks πŸ™
  • PythonDataScienceHandbook πŸ“š

Articles

  • Entropy (for data science) Clearly Explained!!! πŸ“ΊΒ πŸŸ’Β Noted in Goodnotes

Deep Learning

  • Awesome Deep Learning πŸ™
  • CS 152: Neural Networks/Deep Learningβ€”Spring, 2021 | Video series taught by Neil Rhodes (his video about ResNet is really good) πŸŽ“
  • CS231n Convolutional Neural Networks for Visual Recognition πŸŽ“
  • ⭐ Deep Learning Specialization [5 courses] (DeepLearning.AI) | Coursera πŸŽ“ 🟒 My notes
  • DeepMind x UCL | Deep Learning Lecture Series 2021 πŸ“Ί
  • Dive into Deep Learning (Vietnamese version) πŸ“š
  • Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville πŸ“š
  • Deep Learning Course (NYU, Spring 2020), taught by Yann LeCun πŸ“ΊΒ πŸŽ“
  • Deep Learning with Python πŸ“š
  • EfficientML.ai Lecture, Fall 2023, MIT 6.5940 πŸ“Ί (Efficient Deep Learning Computing)
  • fast.ai – fast.aiβ€”Making neural nets uncool again πŸŽ“
  • Grokking Deep Learning - Andrew W. Trask
  • Math and Architectures of Deep Learning by Krishnendu Chaudhury (notebooks) πŸ“š
  • ⭐ Neural networks | 3Blue1Brown πŸ“Ί
  • Neural Networks: Zero To Hero by Andrej Karpathy, and these are the videos. πŸ“Ί
  • Neural networks and deep learning by Michael Nielsen β€” this book is recommended by 3Blue1Brown in his video about Neural Networks. πŸ“š
  • Understanding Deep Learning by Simon J.D. Prince πŸ“š
  • The Little Book of Deep Learning by FranΓ§ois Fleuret (the author of Keras) πŸ“š
  • VIP cheatsheets for Stanford's CS 230 Deep Learning by Afshine Amidi & Shervine Amidi πŸ“šΒ πŸ™
Articles
  • An Overview of Deep Learning for Curious People | Lil'Log
  • Backprop Explainer (wonderful article with interactive elements)
  • Deep Learning: An Introduction for Applied Mathematicians by Catherine F. Higham, Desmond J.Higham.

Engineering / MLOps

  • 90DaysOfDevOps πŸ™
  • Awesome Data Engineering πŸ™
  • Data Engineering - DeepLearning.AI πŸŽ“Β πŸŸ’ My notes
  • DevOps Exercises πŸ™
  • Designing Data-Intensive Applications by Martin Kleppmann πŸ“š
  • IBM AI Engineering Professional Certificate | Coursera
  • Machine Learning in Production Specialization - DeepLearning.AI
  • The Complete Hands-On Introduction to Apache Airflow | Udemy by Marc Lamberti (His Youtube Channel πŸ“ΊΒ is good as well)
  • Meta Database Engineer Professional Certificate - Coursera

AWS Certified Data Engineer (DEA-C01)

  • AWS Certified Data Engineer - Associate Certification | AWS Certification
  • AWS Certified Data Engineer Complete Course | AWS Certified Data Engineer πŸ“ΊΒ πŸŽ“
  • Complete AWS Certified Data Engineer Associate - DEA-C01 | Udemy πŸŽ“

DevOps

  • AWS Fundamentals Specialization [3 courses] (AWS) | Coursera πŸŽ“

Generative AI / LLM

  • 5-Day Gen AI Intensive Course with Google Learn Guide | Kaggle
  • AI Engineering: Building Applications with Foundation Models | Chip Huyen πŸ“š
  • Awesome LLM Apps πŸ™
  • Build LLM Applications (from Scratch) | Manning πŸ“š
  • Building Generative AI Services with FastAPI πŸ“š
  • Building Generative AI-Powered Applications with Python | IBM Coursera
  • CS324 - Large Language Models | Stanford πŸŽ“
  • CS25: Transformers United πŸŽ“
  • ChatGPT Prompt Engineering for Developers - DeepLearning.AI πŸŽ“Β πŸŸ’ My note
  • Foundations of Large Language Models β€” Tong Xiao and Jingbo Zhu πŸ“š
  • Generative AI for Everyone | Coursera (taught by Andrew Ng) πŸŽ“
  • Generative AI for Beginners - Microsoft πŸŽ“Β πŸ™
  • Generative AI with Large Language Models | Amazon Coursera πŸŽ“
  • Hands-On Large Language Models: Language Understanding and Generation by Jay Alammar, Maarten Grootendorst πŸ“šΒ β€” Github repository. πŸ™
  • ⭐ Large Language Models explained briefly - 3Blue1Brown πŸ“Ί
  • LLM courses πŸ™Β πŸŽ“Β β€” everything to learn about LLM.
  • LLMs-from-scratch πŸ™ (Book "Build a Large Language Model (From Scratch)") πŸ“š
  • Learn the fundamentals of generative AI for real-world applications - DeepLearning.AI
  • ⭐ Stanford CS229 I Machine Learning I Building Large Language Models (LLMs) πŸ“Ί

Articles / Video

  • Attention Is All You Need πŸ“Ί
  • ChatGPT: 30 Year History | How AI Learned to Talk πŸ“Ί
  • ⭐ Neural networks | 3Blue1Brown πŸ“Ί (Chap 5 & 6 talk about GPT)
  • Prompt Engineering | Lil'Log
  • What does it mean for computers to understand language? | LM1 πŸ“Ί
  • Why Recurrent Neural Networks are cursed | LM2 πŸ“Ί

Machine Learning

  • 100-Days-Of-ML-Code πŸ™
  • A visual introduction to machine learning: Part 1, Part 2
  • Groking Machine Learning - Luis G. Serrano
  • ⭐ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition 🟒 My notes. πŸ“š
  • Machine Learning & Deep Learning Tutorials πŸ™
  • Machine Learning - A Probabilistic Perspective - Kevin P. Murphy πŸ“š
  • Machine Learning cΖ‘ bαΊ£n
  • Machine Learning From Scratch πŸ™
  • Machine Learning Specialization [3 courses] (Stanford) | Coursera 🟒 My notes for the old version of this course (using Matlab instead of Python). πŸŽ“
  • Machine-Learning-Tutorials πŸ™
  • Pattern Recognition and Machine Learning - Christopher M. Bishop πŸ“š
  • Understanding Machine Learning: From Theory to Algorithms πŸ“š

Maths / Prop / Stats

  • Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition. (use for ref, hard to read for self-study). Consider An Introduction to Statistical Learning instead (same authors). πŸ“š
  • Mathematics for Machine Learning and Data Science Specialization | DeepLearning.AI β€” taught by Luis Serrano. πŸŽ“
  • Math for Machine Learning πŸ“Ί

Statistics

  • Introduction to Statistics Course by Stanford University | Coursera πŸŽ“
  • How not to be wrong by Jordan Ellenberg πŸ“šΒ πŸŸ’
  • La statistique expliquΓ©e Γ  mon chat πŸ“Ί
  • Practical Statistics for Data Scientists by Andrew Bruce, Peter Bruce, and Peter Gedeck πŸ“š
  • Practical Statistics for Data Scientists by Andrew Bruce and Peter Bruce πŸ“š
  • ⭐ Seeing Theory πŸ“š
  • Statistics for Non-Statisticians by Birger Stjernholm Madsen πŸ“š
  • Statistics How To: Elementary Statistics for the rest of us!
  • Think Bayes by Allen B. Downey πŸ“š
  • Think Stats by Allen B. Downey πŸ“š

NLP

  • Natural Language Processing Specialization [4 courses] (DeepLearning.AI) | Coursera
  • Natural Language Processing in Action πŸ“š

Articles

  • The Illustrated Word2vec – Jay Alammar – Visualizing machine learning one concept at a time.

Python

  • Automate the Boring Stuff with Python πŸ“š
  • ⭐ Corey Schafer πŸ“Ί
  • Exercism Learning Tracks
  • Recommended Python learning resources - fast.ai Course Forums πŸŽ“
  • The Hitchhiker's Guide to Python! β€” The Hitchhiker's Guide to Python (github) πŸ™ πŸ“š

Pytorch

  • pytorch-tutorial πŸ™

Tensorflow

  • TensorFlow Developer Professional Certificate (DeepLearning.AI) | Coursera πŸŽ“ 🟒 My notes.

Tools & Services & References

Β 
  • arXiv.org e-Print archive
  • Connected Papers | Find and explore academic papers
  • Chat with Andrew Ng's avatar
  • Github Copilot, Amazon CodeWhispere, ⭐ Cursor, ⭐ Claude Code 🟒 My note
  • ⭐ Lightning.ai, Google Colab 🟒 My note.
  • OpenAI, Mistral, Claude, Gemini, Grok 🟒 My note.
  • The latest in Machine Learning | Papers With Code
  • Vercel AI SDK
  • Vertex AI 🟒 My note.

Vibe Coding / Building apps

  • Cursor Learncp
  • Dennis Babych πŸ“Ί
  • Goon Nguyen’s posts.
  • Ray Fernando β€” 12y ex-Apple β€’ I build AI apps live (bugs included) πŸ“Ί
  • Tools
    • spec-kit β€” Toolkit to help you get started with Spec-Driven Development πŸ™
β—†Generalβ—†Learn how to learnβ—†Blog / Personal websitesβ—†Coding platformsβ—†Computer Scienceβ—‹Data Structure & Algorithmsβ—‹Design patternsβ—†Computer Visionβ—†Data Scienceβ—‹Articlesβ—†Deep Learningβ—†Engineering / MLOpsβ—‹AWS Certified Data Engineer (DEA-C01)β—‹DevOpsβ—†Generative AI / LLMβ—‹Articles / Videoβ—†Machine Learningβ—†Maths / Prop / Statsβ—‹Statisticsβ—†NLPβ—‹Articlesβ—†Pythonβ—†Pytorchβ—†Tensorflowβ—†Tools & Services & Referencesβ—†Vibe Coding / Building apps
About|My sketches |Cooking |Cafe icon Support Thi
πŸ’Œ [email protected]