Resources

These are interesting and/or helpful content I came across. This is an active list of items which I wish to keep updating. Also, I wish to segregate it in a more useful categories as it will keep growing in the future for better discovery.

Blogs and Other Material

• Machine Learning Interviews by Chip Huyen (@chipro)
• What I learned from looking at 200 machine learning tools
• Machine Learning Interviews Machine Learning Systems Design by Chip Huyen (@chipro) Interesting articles mentioned in this book.
  • Troubleshooting Deep Neural Networks by Josh Tobin
  • Debate about science at organizations like Google Brain/FAIR/DeepMind
  • Hyperparameter optimization
  • Gradient Checkpointing
• Validating A/B Test Results
• Machine Learning Crash Course with TensorFlow APIs Google’s fast-paced, practical introduction to machine learning
• Pros and cons of different machine learning techniques.
  • Machine Learning For Dummies Cheat Sheet
  • Modern Machine Learning Algorithms: Strengths and Weaknesses by Elitedatascience
  • Machine Learning Algorithms Pros and Cons by HackingNote
  • Pros and cons of various Machine Learning algorithms
• Short summaries of different ML techniques. Not for general learning purposes.
  • Machine Learning Tutorial
• Applied Machine Learning / MLOps
  • A Guide to Production Level Deep Learning
  • What I learned from looking at 200 machine learning tools
• An interesting series of blog of 365 days of NLP Learn NLP with Me
  • Day 197: Learn NLP With Me – Filling the Gaps with NLP Interview Questions
  • Day 198: Learn NLP With Me – Filling the Gaps with NLP Interview Questions II
  • Day 199: Learn NLP With Me – Filling the Gaps with NLP Interview Questions III
  • Day 200: Learn NLP With Me – Filling the Gaps with NLP Interview Questions IV

Material With Working Code

• The fastai book

Courses

• Full Stack Deep Learning Bootcamp