r/MachineLearning · 2020
Most-mentioned books in r/MachineLearning during 2020
This page ranks books by exact tracked mentions in r/MachineLearning within the published source unit for 2020. Counts come from the reconciled subreddit-by-month matrix; a source unit may be a bounded sample.
#1 Pattern Recognition and Machine Learning
Christopher M. Bishop
Bishop's 2006 Bayesian ML textbook: 26 of its 39 Reddit mentions come from r/MachineLearning, where it sits permanently on the serious-math shelf.
#2 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Aurélien Géron
Aurélien Géron's end-to-end ML textbook: build a working neural net with Scikit-Learn, Keras, and TensorFlow before you finish the first half.
#3 Clean Code
Robert C. Martin
The book 536 Reddit threads cite when arguing about naming variables — revered by beginners, argued over by seniors.
#4 Design Patterns
Erich Gamma
The 1994 Gang of Four catalog that named 23 object-oriented patterns, gave developers a shared vocabulary, and taught a generation to over-apply both.
#5 Effective Python
Brett Slatkin
Brett Slatkin's 90-item guide to writing Python the way the language actually wants to be written.
#6 Fluent Python
Luciano Ramalho
The Python book r/Python recommends when you're done with the tutorials and need to know what the language actually does.