AI and ML Reading List: My Favorites
Before the New Year's Resolutions Enthusiasm Burns Out
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Reach out if you have one to add
These are my favorites.
General Intro:
Artificial Intelligence: A Guide for Thinking Humans, Melanie Mitchell
Machine Learning for Absolute Beginners: A Plain English Introduction, O. Theobald
The Creativity Code: Art and Innovation in the Age of AI, Marcus du Sautoy
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Pedro Domingos
Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World, Cade Metz
The Textbooks:
Artificial Intelligence: A Modern Approach, Global Edition, Peter Norvig, Stuart Russell
Deep Learning- Illustrated Edition, Ian Goodfellow, Yoshua Bengio
An Introduction to Statistical Learning: with Applications in R - 2nd ed. 2021 Edition, Gareth James, Daniela Witten, & 2 more
The Hundred-Page Machine Learning Book, Andriy Burkov
Practical Coding Books for AI/ML, Data Science:
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started, Drew Conway, John White
Programming Collective Intelligence: Building Smart Web 2.0 Applications First Edition, Toby Segaran
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd Edition, Aurélien Géron
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python, Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD 1st Edition, Jeremy Howard, Sylvain Gugger
Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 3rd Edition, Wes McKinney
The Kaggle Book: Data Analysis and Machine Learning for Competitive Data Science, Konrad Banachewicz, Luca Massaron, et al.
ML Systems:
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications 1st Edition, Chip Huyen
NLP:
Natural Language Processing with Transformers, Revised Edition 1st Edition, Lewis Tunstall, Leandro von Werra, Thomas Wolf
Q and A:
Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI, Sebastian Raschka
What Can Go Wrong/Limitations/PaperclipMaximizer:
Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What To Do About Them, Ram Shankar Siva Kumar, Hyrum Anderson
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neil
Superintelligence: Paths, Dangers, Strategies, Nick Bostrom