Resources

Here is a list of data science resources, personal blogs and technical writings that I was finding myself more or less going back to from time to time. Eventually I’ve decided that a list of them was needed.

• Personal websites & blogs
  • Eugene Yan
  • Chan`s Jupyter
  • Vicki Boykis
  • Chip Huyen’s Blog
  • Sebastian Raschka, PhD
  • a how-to blog | Susan Shu Chang
  • swyx’s site | Shawn Wang
  • Machine Learning • Daniel Bourke
• Papers
  • ApplyingML
  • Papers with Code
  • Large Language Model (LLM) Reading List – GitHub
  • Anti-hype LLM reading list – veekaybee/normcore-llm.md — GitHub Gist
  • The Practical Guides for Large Language Models – GitHub
• Notes
  • Chris Albon’s Study Notes
  • Oh Shit, Git!?!
• Interview preparation
  • Introduction to Machine Learning Interviews Book • MLIB
  • Data Science Interview Questions & Answers – GitHub
  • Data Science Interview Questions – GitHub
• Learning resources
  • The Full Stack Website
  • Practical Deep Learning for Coders – fast.ai
  • Deep Learning Fundamentals • Lightning AI
  • Alexey Grigorev’s Machine Learning Bootcamp – GitHub
  • Alexey Grigorev’s MLOps Bootcamp – GitHub
  • Zero to Mastery TensorFlow for Deep Learning • Daniel Bourke
  • Neural Networks and Deep Learning
  • CS231n: Deep Learning for Computer Vision – Stanford Online
  • CS224N: Natural Language Processing with Deep Learning – Stanford Online
• Podcasts
  • Machine Learning Street Talk – YouTube
  • DataTalks.Club – YouTube
  • The Real Python Podcast – YouTube
• Others
  • Trending repositories on GitHub today – GitHub
  • Compact Awesome CV – GitHub