In no particular order here my favourite Machine Learning / AI books.
There are tons of books out there on how to use Python and Pytorch and Sci-kit learn etc. Those books are also great in case you want to combine coding in Python with learning about ML. I did not cover them here in this list. Enjoy!
Generative Deep Learning, Teaching Machines to Paint, Write, Compose and Play by David Foster
- great book that gives a nice intro into this VERY current topic. It does give you the foundations. Not covered are Transformers for that you need something else. As David Ha said on the rear cover: "If you are a creative practitioner who loves to tinker with code and want to apply deep learning to your work, then this book is for you." well said.
Essentials of Metaheuristics by Sean Luke
- Evolutionary computing (aka Metaheuristics, which is a bad name) is an exciting area that has been IMHO not yet fully enjoyed its potential. Probably cos it is very compute intense and needs paralellization. Help with that you find e.g. here: www.evotorch.ai (disclosure I am working for nnaisense)
- The book is written in a unique way as it shows you all the algorithms with pseudo code after the explanations. I liked that a lot as it makes it super practical.
- on evolutionary algorithms and computing: "... they're often best thought of as last-ditch methods, used when no other known technique works."
AIQ - How AI works and how we can harness its power for a better world by Nick Polsen and James Scott
- highly recommended. great stories give you a historical and still highly entertaining intro to data, probability and how it lead to the modern age of big data and intelligent machines. If after this book you are not motivated to work in this space then it is for sure not for you.
HBR's 10 Must Reads: On AI, Analytics, and the New Machine Age by various writers
- one more great book in this HBR series. I liked most the contributions by Thomas Davenport & Rajeev Ronanki on AI for the Real World; Drones go to work by Chris Anderson; The 3-D Printing Playbook by Richard A. D'Aveni; Collaborative Intelligence: Humans and AI are joining forces by H. James Wilson and Paul R. Daugherty
Mathematics for Machine Learning by Marc Peter Deisenroth and others
- Nice and well written intro to most of the math you must know in AI/ML
Machine Learning and Applied Mathematics Introduction by Paul Wilmott
- I really loved this book. Written by a mathematician for non-mathematicians showing very nicely intuitively otherwise complex topics such as Reinforcement Learning
The hundred page machine learning book by Andriy Burkov
- if you are an engineer, computer scientist or otherwise trained in science then this is a great intro book that will explain to you what all this ML fuzz is about.
Machine Learning Engineering by Andriy Burkov
- in case you need to develop AI features into your products then this book gives you all on how a machine learning project works. Like his 100-page book highly recommended.
Artificial Intelligence a Modern Approach by Peter Norvig and Stuart Russell
- this is a bible on the subject and covers it nearly all. in case you plan to study ML and AI then you probably have to read it. I read about 1/3 but pick subjects whenever I feel like.
AI Super-Powers China, Silicon Valley, and the New World Order by Kai-Fu Lee
- full disclosure: I am NOT a fan of Kai-Fu Lee. I think he is very opportunistic and his story with Google in China did not impress me. Still this book shows you how serious AI is taken in China and how far back e.g. Europe still is. Simply also because he does not even mention Europe. :-)
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