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.
βI failed my way to successβ β Thomas Edition.
Emoji notations: πΒ (Github), πΒ (Book / Files), π’Β (Iβve noted/learned), πΊ (Youtube/Videos), βΒ (My favorite), πΒ (MOOC / Courses)
- βΒ 3Blue1Brown πΊ
- β Andrej Karpathy πΊ (Founding member of OpenAI)
- Hugging Face - Learn π
- https://github.com/faridrashidi/kaggle-solutionskaggle-solutions β Collection of Kaggle Solutions and Ideas π
- Understanding AI Models - IBM Technology πΊ
- A Mind for Numbers - Dr. Barbara Oakley π
- How to Read a Book - Mortimer Adler π
- Learning How To Learn - Dr. Barbara Oakley π
- Learning How to Learn: Powerful mental tools to help you master tough subjects β taught by Dr. Barbara Oakley (the author of books βLearning How To Learnβ and βA Mind for Numbersβ) π
- Make It Stick by Brown, Roediger & McDaniel π
- Pragmatic Thinking and Learning: Refactor Your Wetware β Andy Hunt π
- Understanding How We Learn: A Visual Guide β Yana Weinstein, Megan Sumeracki, Oliver Caviglioli π
- βΒ Andrej Karpathy (his wonderful youtube channel πΊ)
- serrano.academy πΊ (Youtube channel of Luis Serrano, the author of Grokking Machine Learning)
- sentdex πΊ
- vcubingx πΊΒ (A Youtube channel like 3Blue1Brown, talk about Math and CS).
- Yannic Kilcher πΊ
- https://github.com/faridrashidi/kaggle-solutionskaggle-solutions β Collection of Kaggle Solutions and Ideas π
- β LeetCode
- β CS50x 2025 - CS Course from Harvard University, taught by David J. Malan πΊ
- awesome-algorithms πΒ
- PrincetonAlgorithms π
- Self-taught DSA materials by YK Sugi (worked at Google, CS Dojo), sorted from beginning to advanced
- Algorithms Specialization - Stanford π
- Algorithms, Fourth Edition by Robert Sedgewick and. Kevin Wayne π
- Introduction to data structures - myucodeschool πΊ
- Learn Data Structures and Alghorithms - Udacity π
- MIT 6.006 Introduction to Algorithms, Fall 2011 π
- The Algorithm Design Manual book by Steven S. Skiena π
- Designing Data-Intensive Applications by Martin Kleppmann π
- python-patterns π
- Convolutions in Image Processing | Week 1, lecture 6 | MIT 18.S191 Fall 2020 (taught by the author of 3Blue1Brown) πΊΒ π
Articles
- [PDF] A guide to convolution arithmetic for deep learning by Vincent Dumoulin and Francesco Visin (animations) π
- [Jupyter Notebook] data-science-ipython-notebooks π
- Entropy (for data science) Clearly Explained!!! πΊΒ π’Β Noted in Goodnotes
- CS 152: Neural Networks/Deep LearningβSpring, 2021 | Video series taught by Neil Rhodes (his video about ResNet is really good) π
- Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville π
- Deep Learning Course (NYU, Spring 2020), taught by Yann LeCun πΊΒ π
- EfficientML.ai Lecture, Fall 2023, MIT 6.5940 πΊ (Efficient Deep Learning Computing)
- Grokking Deep Learning - Andrew W. Trask
- Math and Architectures of Deep Learning by Krishnendu Chaudhury (notebooks) π
- β Neural networks | 3Blue1Brown πΊ
- Neural Networks: Zero To Hero by Andrej Karpathy, and these are the videos. πΊ
- Neural networks and deep learning by Michael Nielsen β this book is recommended by 3Blue1Brown in his video about Neural Networks. π
- Understanding Deep Learning by Simon J.D. Prince π
- The Little Book of Deep Learning by FranΓ§ois Fleuret (the author of Keras) π
- VIP cheatsheets for Stanford's CS 230 Deep Learning by Afshine Amidi & Shervine Amidi πΒ π
Articles
- Backprop Explainer (wonderful article with interactive elements)
- Deep Learning: An Introduction for Applied Mathematicians by Catherine F. Higham, Desmond J.Higham.
- 90DaysOfDevOps π
- Data Engineering - DeepLearning.AI πΒ π’ My notes
- DevOps Exercises π
- Designing Data-Intensive Applications by Martin Kleppmann π
- The Complete Hands-On Introduction to Apache Airflow | Udemy by Marc Lamberti (His Youtube Channel πΊΒ is good as well)
- AI Engineering: Building Applications with Foundation Models | Chip Huyen π
- Awesome LLM Apps π
- Build LLM Applications (from Scratch) | Manning π
- Building Generative AI Services with FastAPI π
- Foundations of Large Language Models β Tong Xiao and Jingbo Zhu π
- Generative AI for Everyone | Coursera (taught by Andrew Ng) π
- Generative AI for Beginners - Microsoft πΒ π
- Hands-On Large Language Models: Language Understanding and Generation by Jay Alammar, Maarten Grootendorst πΒ β Github repository. π
- β Large Language Models explained briefly - 3Blue1Brown πΊ
- LLM courses πΒ πΒ β everything to learn about LLM.
- LLMs-from-scratch π (Book "Build a Large Language Model (From Scratch)") π
- βΒ Neural networks | 3Blue1Brown πΊ (Chap 5 & 6 talk about GPT)
- 100-Days-Of-ML-Code π
- Groking Machine Learning - Luis G. Serrano
- βΒ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition π’ My notes. π
- Machine Learning - A Probabilistic Perspective - Kevin P. Murphy π
- Machine Learning Specialization [3 courses] (Stanford) | Coursera π’ My notes for the old version of this course (using Matlab instead of Python). π
- Pattern Recognition and Machine Learning - Christopher M. Bishop π
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). π
- Mathematics for Machine Learning and Data Science Specialization | DeepLearning.AI β taught by Luis Serrano. π
- How not to be wrong by Jordan Ellenberg πΒ π’
- Practical Statistics for Data Scientists by Andrew Bruce, Peter Bruce, and Peter Gedeck π
- Practical Statistics for Data Scientists by Andrew Bruce and Peter Bruce π
- β Seeing Theory π
- Statistics for Non-Statisticians by Birger Stjernholm Madsen π
- Think Bayes by Allen B. Downey π
- Think Stats by Allen B. Downey π
- βΒ Corey Schafer πΊ
- pytorch-tutorial π
Β
- Github Copilot, Amazon CodeWhispere, β Cursor, βΒ Claude Code π’Β My note
- βΒ Lightning.ai, Google Colab π’ My note.
- Dennis Babych πΊ
- Ray Fernando β 12y ex-Apple β’ I build AI apps live (bugs included) πΊ
- Tools
- spec-kit β Toolkit to help you get started with Spec-Driven Development π