links

A place where I list links to interesting, fascinating places that I like on the Internet.

blogs

ml blogs

• Chip Huyen: https://huyenchip.com/blog/
• Lilian Weng: https://lilianweng.github.io/lil-log/
• Chris Olah: https://colah.github.io
• Gwern: https://www.gwern.net
• Andrej Karpathy: https://karpathy.github.io
• David Ha (@hardmaru): http://blog.otoro.net
• Andrey Kurenkov: http://www.andreykurenkov.com/writing/
• Sebastian Ruder: http://ruder.io/
• Rich Sutton: http://www.incompleteideas.net
• Fast.ai: https://www.fast.ai
• Zachary Lipton: http://approximatelycorrect.com
• Chris Sutton: http://www.theexclusive.org
• Stephen Merity: https://smerity.com
• Joel Grus (Data Science from scratch): https://joelgrus.com
• Francis Bach: https://francisbach.com/
• Sebastian Bubech: https://blogs.princeton.edu/imabandit/
• http://hunch.net/
• http://blog.echen.me
• https://kiranvodrahalli.github.io/links/
• https://khanna.cc/blog/

cs blogs

• Steve Yegge Amazon rants: https://sites.google.com/site/steveyegge2/blog-rants
• Steve Yegge’s Blog rants: https://steve-yegge.blogspot.com
• Dan Luu - Code Recurse: https://danluu.com
• Bret Victor: http://worrydream.com/
• Adrian Coyler - The Morning Paper: https://blog.acolyer.org/
• Google’s Engineering Practices documentation
• http://www.craftinginterpreters.com

science/general

• http://michaelnielsen.org/blog/
• https://waitbutwhy.com/

online publications

• Distill.pub
• Explained AI (Terrance Par)
• Depth First Learning
• Programmar’s perspective for Matrix Multiplication (Better Explained)

textbooks

math

• Mathematics for ML (Marc Deisenroth, UCL)
• Street fighting math (Sanjoy Mahajan)
• Seeing Theory - A visual introduction to probability and statistics (Daniel Kunin, Brown Uni)
• Immersive Math - Linear Algebra
• The Matrix Calculus You Need For Deep Learning (Jeremy Howard and Terrance Parr)
• A Beginner’s Guide to the Mathematics of Neural Networks (A.C. Coolen, King’s College)

ML/DL

• Machine Learning - A probabilistic approach (Kevin Murphy)
• The Elements of Statistical Learning (Hastie)
• Deep Learning (Ian Goodfellow)
• Dive into Deep Learning (Zachary Lipton)
• Grokking Deep Learning - From scratch with numpy (Andrew Trask)
• Mechanics of Machine Learning (Jeremy Howard and Terrance Parr)

AI

• Reinforcement Learning - An Introduction (Sutton and Barto)
• Fairness and Machine Learning (Moritz Hardt)

CS

• Architecture of Open Source Applications

online courses

math

• HN, how to learn math
• fast.ai Numerical Linear Algebra (Rachel Thomas)
• MIT OCW Mathematics for CS

ML

• Cornell CS 4780: ML for Intelligent System (Kilian Weinberger), youtube playlist
• University of Edinburgh MLPR (Iain Murray)
• fast.ai Introduction to ML for coders (Jeremy Howard)
• Machine Learning Mastery
• Rules of ML: Best practices for ML Engineering (Martin Zinkevich, Google)

DL

• fast.ai Practical DL for coders (Jeremy Howard)
• fast.ai DL from foundations (Jeremy Howard)
• Stanford CS 231n: Convolutional Neural Networks (Fei Fei Li), youtube playlist
• Full stack deep learning bootcamp (Pieter Abbeel)
• Advanced Deep Learning and RL (Thore Graepel, UCL/Deepmind), slides, youtube playlist

RL

• Spinning up in RL (openAI)
• Introduction to RL (Analytics Vidhya)
• Introduction to RL (David Silver, UCL), youtube playlist
• Implementation of RL algorithms (Denny Britz)

programming

• CS231n: Numpy tutorial (Fei Fei Li)

others

• Lectures on Digital photography (Marc Levoy), youtube playlist
• Brief history of humankind (Yuval Noah Harari)

youtube channels

science

• 3blue1brown
• Primer

anki articles

• Augmenting Long-term Memory - Micheal Nielsen
• Super memo’s rules for formulating knowledge
• Spaced Repetition - Gwern.net
• Spaced repetition - Lesswrongwiki
• Anki Tips: What I Learned Making 10,000 Flashcards - rs.io

writing (some scientific specific)

• The day you became a better writer (Scott Adams)
• Writer’s Handbook: https://writing.wisc.edu/Handbook/
• Iain Murray - Speaking resources: http://homepages.inf.ed.ac.uk/imurray2/teaching/speaking/
• Iain Murray - Writing resources: http://homepages.inf.ed.ac.uk/imurray2/teaching/writing/
• Sharon Goldwater- Writing clear: http://homepages.inf.ed.ac.uk/sgwater/writing_advice.html
• Sharon Goldwater- Presentations: http://homepages.inf.ed.ac.uk/sgwater/presentation_advice.html
• Sharon Goldwater - Dissertation: http://homepages.inf.ed.ac.uk/sgwater/writing/ug4_report.html
• Matt - LoR: http://matt.might.net/articles/how-to-recommendation-letter/
• Matt - Apply to grad school: http://matt.might.net/articles/how-to-apply-and-get-in-to-graduate-school-in-science-mathematics-engineering-or-computer-science/
• Matt - Email: http://matt.might.net/articles/how-to-email/
• Sharon Goldwater Resources: http://homepages.inf.ed.ac.uk/sgwater/resources.html
• How to Write an Abstract: http://users.ece.cmu.edu/~koopman/essays/abstract.html
• Advice for Authors - Academically Interesting: https://jsteinhardt.wordpress.com/2017/02/28/advice-for-authors/
• Heuristics for Scientific Writing (a Machine Learning Perspective) – Approximately Correct: http://approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/
• Ten simple rules for scientists - Improving your writing productivity: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006379
• Scholars Shouldn’t Fear ‘Dumbing Down’ for the Public - The Atlantic: https://www.theatlantic.com/science/archive/2018/10/scholars-shouldnt-fear-dumbing-down-public/573979/
• How to give a technical presentation (how to give a scientific talk): https://homes.cs.washington.edu/~mernst/advice/giving-talk.html