Explore 161 canonical books across 11 disciplines and more than 1,280 executable skills. Inspect the synthesis, copy prompts, and build a skill deck for your own engineering work.
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AI / Deep Learning
Aston Zhang, Zack C. Lipton, Mu Li, Alexander J. Smola
Build deep-learning systems from mathematical ideas and executable code.
7 skills
AI / Deep Learning
David Barber
Probabilistic modeling and inference for machine-learning problems.
8 skills
AI / Deep Learning
François Chollet
Practical deep learning with Python and Keras.
7 skills
Second edition
AI / Deep Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Accessible statistical learning through models, intuition, and laboratories.
8 skills
Second edition
AI / Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville
Foundations, methods, and mathematics of modern deep learning.
7 skills
AI / Deep Learning
Kevin P. Murphy
Advanced probabilistic methods for contemporary machine learning.
7 skills
AI / Deep Learning
Kevin P. Murphy
A unified introduction to probabilistic machine learning.
8 skills
AI / Deep Learning
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Essential mathematics behind machine-learning models and algorithms.
8 skills
AI / Deep Learning
Richard S. Sutton, Andrew G. Barto
The standard framework for reinforcement-learning ideas and algorithms.
7 skills
AI / Deep Learning
Simon J. D. Prince
Deep learning explained between theory, intuition, and implementation.
8 skills
AI / Deep Learning
Steven Bird, Ewan Klein, Edward Loper
Natural-language processing through Python, corpora, and linguistic analysis.
8 skills
AI / Deep Learning
Stuart Russell, Peter Norvig
A broad, classical survey of artificial-intelligence methods.
8 skills
Second edition
