Based on the list first drawn up for my talk “Straight outta Swampland” at the ITF KU Leuven in January 2020. If you get the joke and are interested, then feel free to poke me for the slides.
Specific topics are covered in their own pages [NLP], [ComputerVision], [TimeSeries] and [AGI]
Introduction to Statistical Learning, Witten et al http://faculty.marshall.usc.edu/gareth-james/ISL/
The Elements of Statistical Learning, Hastie et al https://web.stanford.edu/~hastie/ElemStatLearn/
Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, http://aima.cs.berkeley.edu/
Pattern Recognition and Machine Learning, Bishop https://cds.cern.ch › record › files › 9780387310732_TOCPattern Recognition and Machine Learning
Deep Learning, Goodfellow et al https://www.deeplearningbook.org
Dive into Deep Learning https://arxiv.org/abs/2106.11342
Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy. https://probml.github.io/pml-book/book1.html
Neural Networks and Deep Learning by Michael Nielsen. nhttp://neuralnetworksanddeeplearning.com/
https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
https://research.facebook.com/blog/2021/12/introducing-bean-machine-a-probabilistic-programming-platform-built-on-pytorch/
Pen and Paper Exercises in Machine Learning https://arxiv.org/abs/2206.13446
https://marcotcr.github.io/lime/tutorials/Lime%20-%20basic%20usage%2C%20two%20class%20case.html good original LIME example
https://classic.d2l.ai/index.html - Smola et al, Dive into Deep Learning
https://www.coursera.org/learn/machine-learning - classical ml.
https://www.deeplearning.ai/ - deep.
https://www.fast.ai/ - even deeper man.
https://arxiv.org/abs/1803.08823 - spot my bias.
https://developers.google.com/machine-learning/guides/rules-of-ml
Interpretability: https://twitter.com/hima_lakkaraju/status/1390754121322467330?s=11
TWIMLAI
Datacamp
In no particular order
colab.research.google.com
Background: https://mml-book.com/
Awesome lists: https://github.com/sindresorhus/awesome
https://minimum-viable-data-scientist.readthedocs.io/
https://www.textbook.ds100.org/intro
https://developers.google.com/machine-learning/crash-course/
https://project-awesome.org/hangtwenty/ dive-into-machine-learning
https://paperswithcode.com/ - It’s paper with code.
https://callingbullshit.org
** https://fleuret.org/ee559/ **
https://colah.github.io
http://www.arxiv-sanity.com - The ML ArXiV is otherwise impossible to follow.
https://www.jeremyjordan.me/evaluating-a-machine-learning-model/
https://explained.ai/
DEEP LEARNING INTERVIEWS https://arxiv.org/abs/2201.00650
Distill.pub
You can attend or help out at Meetups and Hackathons: We learn machine learning, Data Science meetups, Tensorflow meetup, NLP meetups.