r/MachineLearning · 2021
Most-mentioned books in r/MachineLearning during 2021
This page ranks books by exact tracked mentions in r/MachineLearning within the published source unit for 2021. Counts come from the reconciled subreddit-by-month matrix; a source unit may be a bounded sample.
#1 Designing Data-Intensive Applications
Martin Kleppmann
A backend engineer's field guide to the tradeoffs behind every database, queue, and distributed system you will ever touch.
#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 Code Complete
Steve McConnell
Steve McConnell's 900-page construction manual for software, ranked third on Reddit's canonical reading list and still cited 25 years after publication.
#4 Cracking the Coding Interview
Gayle Laakmann McDowell
The book that turned software hiring into a sport — written by the ex-Googler who introduced programming challenges to the process in the first place.
#5 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.
#6 The Mythical Man-Month
Frederick P. Brooks Jr.
Fred Brooks spent the 1960s building IBM's OS/360 and wrote down everything that went wrong — in 1975, and it still lands.