šŸ“š What are you top general Machine Learning books ?

I would say:
šŸ“˜ Deep Learning - Goodfellow, Bengio and Courville
šŸ“— Artificial Intelligence: A Modern Approach - Russel & Norvig
šŸ“™ Pattern Recognition and Machine Learning - Bishop

#AI #ML #MachineLearning #book #books #recommendations #academia #question #discussion #media

@solalnathan i have this blog post as my fav books. am ashamed to say i still have not read "a modern approach" :/ :/ :/ https://matpalm.com/blog/cool_machine_learning_books/
brain of mat kelcey...

@mat_kelcey Very cool way to list your favorite book. I should probably do something similar šŸ˜€

(I clearly haven't read AIMA from front to cover, far from it, its nothing to be ashamed of)

@solalnathan

Understanding ML (https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/) by Shai Shalev-Shwartz and Shai Ben-David.

Bayesian Data Analysis (http://www.stat.columbia.edu/~gelman/book/) by Andrew Gelman, John Carlin, Hal Stern, David Dunson, @avehtari and Donald Rubin.

Mathematics for Machine Learning (https://mml-book.github.io) by @deisenroth, Aldo Faisal and @chengsoonong.

I haven't read it yet, but it looks great:

Probabilistic Numerics (https://www.probabilistic-numerics.org/textbooks/) by @PhilippHennig, @maosbot and Hans Kersting.

@solalnathan Clearly also the book series in Probabilistic Machine Learning by @sirbayes - see https://probml.github.io/pml-book/
ā€œProbabilistic machine learningā€: a book series by Kevin Murphy

ā€œProbabilistic Machine Learningā€ - a book series by Kevin Murphy

pml-book
@kadeng @solalnathan I didn’t know about the newer books! I love the first one, feels really complementary to Bishop’s, given the different perspective. I used both for my advanced ML class