There are few things I love more than cuddling up with an exciting new book. There are always more things I want to learn than time I have in the day, and I think books are such a fun, long-form way of engaging (one where I won’t be tempted to check Twitter partway through).
This book roundup is a selection from the last few years of TWIML guests, counting only the ones related to ML/AI published in the past 10 years. We hope that some of their insights are useful to you! If you liked their book or want to hear more about them before taking the leap into longform writing, check out the accompanying podcast episode (linked on the guest’s name).
(Note: These links are affiliate links, which means that ordering through them helps support our show!)
Adversarial ML
Generative Adversarial Learning: Architectures and Applications (2022), Jürgen Schmidhuber
AI Ethics
Sex, Race, and Robots: How to Be Human in the Age of AI (2019), Ayanna Howard
Ethics and Data Science (2018), Hilary Mason
AI Sci-Fi
AI 2041: Ten Visions for Our Future (2021), Kai-Fu Lee
AI Analysis
AI Superpowers: China, Silicon Valley, And The New World Order (2018), Kai-Fu Lee
Rebooting AI: Building Artificial Intelligence We Can Trust (2019), Gary Marcus
Artificial Unintelligence: How Computers Misunderstand the World (The MIT Press) (2019), Meredith Broussard
Complexity: A Guided Tour (2011), Melanie Mitchell
Artificial Intelligence: A Guide for Thinking Humans (2019), Melanie Mitchell
Career Insights
My Journey into AI (2018), Kai-Fu Lee
Build a Career in Data Science (2020), Jacqueline Nolis
Computational Neuroscience
The Computational Brain (2016), Terrence Sejnowski
Computer Vision
Large-Scale Visual Geo-Localization (Advances in Computer Vision and Pattern Recognition) (2016), Amir Zamir
Image Understanding using Sparse Representations (2014), Pavan Turaga
Visual Attributes (Advances in Computer Vision and Pattern Recognition) (2017), Devi Parikh
Crowdsourcing in Computer Vision (Foundations and Trends(r) in Computer Graphics and Vision) (2016), Adriana Kovashka
Riemannian Computing in Computer Vision (2015), Pavan Turaga
Databases
Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases (2021), Xin Luna Dong
Big Data Integration (Synthesis Lectures on Data Management) (2015), Xin Luna Dong
Deep Learning
The Deep Learning Revolution (2016), Terrence Sejnowski
Dive into Deep Learning (2021), Zachary Lipton
Introduction to Machine Learning
A Course in Machine Learning (2020), Hal Daume III
Approaching (Almost) Any Machine Learning Problem (2020), Abhishek Thakur
Building Machine Learning Powered Applications: Going from Idea to Product (2020), Emmanuel Ameisen
ML Organization
Data Driven (2015), Hilary Mason
The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI (2019), David Carmona
MLOps
Effective Data Science Infrastructure: How to make data scientists productive (2022), Ville Tuulos
Model Specifics
An Introduction to Variational Autoencoders (Foundations and Trends(r) in Machine Learning) (2019), Max Welling
NLP
Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics (2013), Emily M. Bender
Robotics
What to Expect When You’re Expecting Robots (2021), Julie Shah
The New Breed: What Our History with Animals Reveals about Our Future with Robots (2021), Kate Darling
Software How To
Kernel-based Approximation Methods Using Matlab (2015), Michael McCourt