2021 Guest Book Roundup

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