- How Smart Machines Think (The MIT Press)
- Many-Core Computing: Hardware and Software edited by Bashir M. Al-Hashimi and Geoff V. Merrett
- PhD: An uncommon guide to research, writing & PhD life by James Hayton
- 3 years for a PhD?: Here’s how to do it right by David Yoong
- Deep Learning for Computer Vision with Python (Volumes 1, 2 and 3) by Adrian Rosebrock
- Deep Learning Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow by Magnus Ekman
- Machine Learning Yearning by Andrew Ng
- Constraining Designs for Synthesis and Timing Analysis-A Practical Guide to Synopsys Design Constraints (SDC) by S. Gangadharan and S. Churiwala
- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
- Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
- A Mind for Numbers: How to Excel at Math and Science (Even If You Flunked Algebra) by Barbara Oakley
- How Not to Be Wrong: The Power of Mathematical Thinking by Jordan Ellenberg
- Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan
- How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil
- Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths
- Thinking, Fast and Slow by Daniel Kahneman
- Thinking Statistically by Uri Bram, Hannah Vazquez
- Machine Learning: The New AI (The MIT Press Essential Knowledge series) by Ethem Alpaydin
- Your Brain at Work: Strategies for Overcoming Distraction, Regaining Focus, and Working Smarter All Day Long by David Rock
- Top Brain, Bottom Brain: Surprising Insights into How You Think by Stephen Kosslyn, G. Wayne Miller
- Brain Rules (Updated and Expanded): 12 Principles for Surviving and Thriving at Work, Home, and School by John Medina
- Dissertations Made Manageable: How to Research and Write by a Prolific Author by Conrad Jones
- A Novice Guide to How to Write a Thesis: Quick tips on how to finish your thesis or dissertation by Sharaf Mutahar Alkibsi
- How to Write a Thesis by Umberto Eco
- The Hundred-Page Machine Learning Book by Andriy Burkov
- Artificial Intelligence, Machine Learning and Deep Learning: Essential Concepts to Know about Machine Learning, Deep Learning and Artificial Intelligence (Three Book Bundle) by Mark Howard
- Machines that Think: Everything you need to know about the coming age of artificial intelligence (New Scientist Instant Expert) by New Scientist
- The AI Delusion by Gary Smith
- The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski
- The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb
- Artificial Unintelligence: How Computers Misunderstand the World (The MIT Press) by Meredith Broussard
- Alan Turing: The Enigma by Andrew Hodges
- Machine Learning For Beginners Guide Algorithms: Supervised & Unsupervised Learning. Decision Tree & Random Forest Introduction (Artificial Intelligence Book 1) by William Sullivan
- Machine Learning: For Beginners – Your Starter Guide For Data Management, Model Training, Neural Networks, Machine Learning Algorithms (Machine Learning Book 1) by Ken Richards
- Machine Learning: For Beginners – Your Definitive Guide For Machine Learning Framework, Machine Learning Model, Bayes Theorem, Decision Trees (Machine Learning Book 2) by Ken Richards
- They Say, I Say: The Moves That Matter in Academic Writing by Gerald Graff, Cathy Birkenstein
- How to Lie with Statistics by Darrell Huff
- Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists: Untangling Numbers from the Media, Politicians and Activists by Joel Best
- Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
- TED Talks: The official TED guide to public speaking: Tips and tricks for giving unforgettable speeches and presentations by Chris Anderson
- Machine Learning: The New AI (The MIT Press Essential Knowledge Series) by Ethem Alpaydi
- Thinking Statistically by Uri Bram
- Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths
- Building Great Sentences: Exploring the Writer’s Craft -(The Great Courses) by Brooks Landon
- How to Publish Your Book by Jane Friedman
- Follow the Geeks: 10 Digital Innovators and the Future of Work by Lyndsey Gilpin and Jason Hiner
- How Not to Be Wrong: The Power of Mathematical Thinking by Jordan Ellenberg
- Start with Why: How Great Leaders Inspire Everyone to Take Action by Simon Sinek
- A Mind For Numbers: How to Excel at Math and Science (Even If You Flunked Algebra) by Barbara Oakley
- Cracking The Machine Learning Interview by Nitin Suri
- An Introduction to Linear Algebra (Dover Books on Mathematics) by L. Mirsky
- Linear Algebra (Dover Books on Mathematics) by Georgi E. Shilov
- Starting Your PhD: What You Need To Know (PhD Knowledge Book 1) by Helen Kara
- Writing for Computer Science by Justin Zobel
- How to Deliver a TED Talk: Secrets of the World’s Most Inspiring Presentations, revised and expanded new edition, with a foreword by Richard St. John and an afterword by Simon Sinek by Jeremey Donovan
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
- How Smart Machines Think (The MIT Press) by Sean Gerrish (Author), Kevin Scott (Foreword)
- Surviving AI by Calum Chace
- What to Do When Machines Do Everything: How to Get Ahead in a World of AI, Algorithms, Bots, and Big Data by Malcolm Frank, Paul Roehrig, Ben Pring
- Deep Thinking: Where Artificial Intelligence Ends and Human Creativity Begins by Garry Kasparov
- Rebooting AI: Building Artificial Intelligence We Can Trust by Gary Marcus and Ernest Davis
- How Smart Machines Think by Sean Gerrish
- Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
- Architects of Intelligence: The truth about AI from the people building it by Martin Ford