ML

Machine Learning

• Lectures

  • Stanford CS229: Machine Learning (web, youtube)
  • Andrew Ng’s ML course material (link)

• Cheetsheets

  • Amidi’s: Shervin or Afshin or Github

• Resources

  • An Introduction to Statistical Learning (book), (video), (youtube), (solutions)
  • The Elements of Statistical Learning (book)
  • Tom Mitchel’s book: Machine Learning
  • Andriy Burkov. The Hundred Page Machine Learning (web)
  • Mehryar Mohri, Foundations of ML (web)
  • Understanding Machine Learning: From Theory to Algorithms (ebook)
  • Patterns, Predictions and Actions (ebook)
  • “Awsome” Machine Learning resouces (github)

Deep Learning

• Lectures

  • Ng/Katanforoosh (youtube), (web)
  • Ng/Deep Learning Specialization (coursera), (github)
  • Lex Fridman at MIT (web), (youtube)
  • Pieter Abbeel Deep Unsupervised Learning (Bekrley CS-294)

• Cheetsheets

  • Ng/Deep Learning Specialization (Tess Ferrandez cheatsheets)

• Resources

  • Ian Goodfellow et al.: Deep Learning
  • Dive into Deep Learning
  • Michael Nielsen: NN & DL (html-book)
  • Deep Learning with PyTorch, (web)

Computer Vision

• Lectures
  • Basic computer vision: The Ancient Secrets of Computer Vision (youtube)
  • Stanford Convolutional Neural Networks for Visual Recognition (youtube), (web)
  • Advanced Deep Learning for Computer Vision - Technical University Munich (youtube)

Natural Language Processing

• Lectures

  • Stanford CS224N: Natural Language Processing with Deep Learning (web), (youtube)

• Transformers

  • Transformers: An introduction and catalog (blog post)
  • How Do Vision Transformers Work?
  • Multi-Headed Attention (MHA), intro and pytorch code

• Resources

  • “Awsome” NLP Books etc. (github)

Time Series

• Resources
  • Forecasting: Principles and Practice (book)
  • Classical Time Series Forecasting Methods in Python (blogPost)
  • Time series in python (blogPost)
  • Matrix Profile (link)
  • Introduction to Econometrics with R
  • Principles of Econometrics with R
  • New Introduction to Multiple Time Series Analysis

Interview

• “Awesome” Data Science Inteview Resources (github)
• Machine Learning Interviews Book, Chip Huyen (github)