Digital Minimalism: Choosing a Focused Life in a Noisy World
Cal Newport
斯坦福算法博弈论二十讲
Tim Roughgarden
Bayesian Data Analysis
Andrew Gelman
Writing Well and Being Well for Your PhD and Beyond: How to ...
Katherine Firth
Computer Vision: Models, Learning, and Inference
Simon J. D. Prince
Hyperparameter Optimization in Machine Learning
Luca Franceschi
Machine Learning, Second Edition: A Probabilistic Perspective
Kevin P. Murphy
Matrix Calculus (for Machine Learning and Beyond)
Alan Edelman, Steven G. Johnson
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
Stefano V. Albrecht & Filippos Christianos & Lukas Schäfer
Non-Convex Optimization for Machine Learning
Prateek Jain & Purushottam Kar
Pattern Recognition and Machine Learning
Christopher M. Bishop
Pattern Recognition and Machine Learning: Solutions to Exercises ...
Markus Svensén & Christopher M. Bishop
Pen and Paper Exercises in Machine Learning
Michael U. Gutmann
Physics-based Deep Learning
N. Thuerey, B. Holzschuh, P. Holl, G. Kohl, M. Lino, Q. Liu, ...
Probabilistic Graphical Models: Principles and Techniques
Daphne Koller & Nir Friedman
Statistical Rethinking: A Bayesian Course With Examples in R ...
Richard McElreath