Summary
Want to find out what artificial intelligence is all about and what it means for your world? Here are the best books on AI.
Artificial Intelligence has already pervaded the modern world and is on the verge of changing things forever. This is a critical inflection point, and everyone has a reason to become and stay informed.
If you don’t know anything about artificial intelligence, reading one or two of the best AI books for beginners will get you up to speed fairly quickly.
And if you already have a basic understanding of AI, work in artificial intelligence, or are in a line of work that may be affected by it, there’s even more reason to stay on top of the field.
Here’s a list of AI book recommendations suitable for beginners, intermediates, and experts. It includes general artificial intelligence titles, computational intelligence books, and good machine learning books.
Some teach the basics while others get into highly complex theoretical subjects. Some are literary non-fiction and others are graduate-level textbooks.
Check out these AI book recommendations. You’re sure to find one that appeals to you and enlightens you.
12 AI Books You Must Read
Best AI Books for Beginners
Artificial Intelligence Basics: A Non-Technical Introduction
Artificial Intelligence Basics by Tom Taulli covers the fundamentals of Artificial Intelligence and provides an overview of its impact.
This is one of the best AI books for beginners. It covers fundamental AI concepts, such as basic principles, data use, machine learning, deep learning, robotic process automation, natural language processing, and physical robots. It then goes over societal considerations on AI ethics, implementing AI, and the future of AI.
This book includes plenty of real-life examples of how AI is being used and case studies showing how it can be implemented within any business. It’s an ideal starting point for anyone who wants an overview of what AI is, how it’s being used, and what it means for business, governments, and daily life.
The author, Tom Taulli, is a serial software entrepreneur who’s written for publications like for online publications such as BusinessWeek.com, TechWeb.com, and Bloomberg.com. He’s adept at breaking complicated subject matters down in a way the average layperson can easily understand.
Key Learning Topics:
- Core AI principles
- Implementation best practices
- Real-world AI case studies
- AI and robotics capabilities
- Utilizing AI in business
Artificial Intelligence: A Modern Approach (4th Edition)
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig is an authoritative and comprehensive textbook traversing the full breadth of artificial intelligence technologies.
It’s one of the best AI books for beginners who want a thorough understanding of artificial intelligence theories, concepts, and practical applications. Some of those concepts explain how intelligent agents solve problems, acquire knowledge, reason, plan, handle uncertainty, communicate, perceive, and act.
This book addresses artificial intelligence and machine learning, then wraps with a look at philosophical matters, including ethics and the future of AI.
Key Learning Topics:
- Machine learning
- Deep learning
- Transfer learning
- Multiagent systems
- Robotics
- Natural language processing
- Causality
- Probabilistic programming
- Safe and fair AI
Artificial Intelligence for Dummies (2nd Edition)
Artificial Intelligence for Dummies (2nd Edition) by John Paul Mueller and Luca Massaron is a layperson’s guide to average intelligence. It explains what it is, what it can do, how it works, and how it’s being used.
It’s the best AI book for beginners who want to cut through the noise and get hype-free, down-to-earth answers on what AI is all about.
It starts with a good explanation of artificial intelligence and how it works with data, algorithms, and specialized software. It then addresses how AI is being integrated into modern society and touches on some potential future use cases.
This book is excellent at showing how AI fits into everyone’s life and outlining highly technical artificial intelligence concepts in plain and simple terms.
Key Learning Topics:
- How AI is used today
- AI’s software applications
- AI’s hardware applications
- The Future of AI
A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going
A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going by Michael Wooldridge journeys through the history of artificial intelligence and its development.
It begins with Alan Turing and ends by contemplating the prospects of truly conscious machines. Along the way, it meanders through the scientific ideas and breakthroughs that have built AI.
The author, Michael Wooldridge, is a leading AI researcher and Oxford professor of computer science. But don’t let his credentials intimidate you.
A Brief History of Artificial Intelligence is one of the best AI books for beginners who would rather dive into some good storytelling than work through a point-by-point technical breakdown.
This AI book is great for anyone looking to be informed enough about AI to hold a conversation on it.
Key Learning Topics:
- History of AI
Best AI Books for Intermediates
Deep Learning (Adaptive Computation and Machine Learning Series)
Deep Learning (Adaptive Computation and Machine Learning series) by Aaron Courville, Ian Goodfellow, and Yoshua Bengio dives into deep learning – a particular branch of machine learning that teaches computers to process information like humans do. That is, by learning from experience and using hierarchical concepts to understand the world.
This book introduces readers to core deep learning concepts, theories, and techniques. It begins with the foundations of deep learning – applied mathematics and machine learning. It then moves into deep learning techniques, practices, and research areas.
This AI book recommendation is suitable for AI students and software engineers.
Key Learning Topics:
- Linear Algebra
- Probability and Information Theory
- Numerical Computation
- Deep Network Practices
- Deep Learning Models
- Sampling, Coding, and Encoding
Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples and Case Studies
Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples and Case Studies by John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy introduces readers to how machine learning is used for predictive data analytics.
Machine learning is used to extract patterns from large datasets and build predictive models that can be used for risk assessment, price prediction, document classification, customer behavior prediction, and other purposes.
This AI book recommendation is considered an introductory textbook. It is extremely focused and comprehensive, but includes plenty of nontechnical concept explanations, mathematical models, and illustrated working examples.
Key Learning Topics:
- Predictive data models
- Information-based machine learning
- Similarity-based machine learning
- Probability-based learning
- Error-based learning
Life 3.0 Being Human in the Age of Artificial Intelligence
Life 3.0 Being Human in the Age of Artificial Intelligence by Max Tegmark is a cosmologist’s look at what artificial intelligence means for humanity.
It touches on basic AI explanations, such as intelligence, memory, computation, and learning. But this book isn’t so much about what AI is. It’s about how artificial intelligence can affect and transform the world.
The author, Max Tegmark, is an AI researcher, cosmologist, MIT professor, president of the Future of Life Institute, and supporter of effective altruism. Tegmark has spent years researching and thinking about critical AI-related concerns. He articulates those in this revelatory and engaging book.
This is another great AI book recommendation for anyone who wants a guide on how artificial intelligence may impact their personal life and how to prepare for some of these changes. And it’s another one that’s appropriate for anyone who’s simply interested in holding an intelligent conversation about AI.
Key Learning Topics:
- AI versus jobs and wages
- Prosperity in a heavily automated world
- A lethal autonomous weapons arms race
- Intelligent totalitarianism
- Altered social structures
- AI’s impact on crime, the law, and the judicial system
Gödel, Escher, Bach: An Eternal Golden Braid
Gödel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter roams through foundational concepts on mathematics, symmetry, and intelligence while looking at common themes found in the lives and works of logician Kurt Gödel, artist M. C. Escher, and composer J. S. Bach.
Author Douglas Hofstadter is a scholar of comparative literature, physics, and cognitive science. His Pulitzer Prize-winning nonfiction book uses short stories, analysis, and literary illustrations to consider how human cognition may emerge from hidden neurological mechanisms, how the nature of interconnected system maps supports and generates life, and how consciousness can thereby emerge from computer systems.
This book was first published in 1979 and quickly developed a reputation for being challenging and confusing, at times. But it’s well worth the read.
Key Learning Topics:
- Strange loops
Best AI Books for Advanced Knowledge
Neural Networks and Deep Learning
Neural Networks and Deep Learning by Charu C. Aggarwal is an advanced textbook on the classical and modern theories, concepts, and models of deep learning and neural networks.
This book introduces readers to neural networks and then ranges from basic to advanced topics. However, even the introduction is more suitable for readers with a good fundamental grasp of AI.
It delves into the algorithms, theory, and explanations behind core neural network concepts and shows how neural networks relate to traditional machine learning algorithms.
This AI book recommendation is best for those interested in computational intelligence books and it’s suitable for graduate-level students, researchers, and AI practitioners.
Key Learning Topics:
- Computational layers
- Neural architecture components
- Backpropagation algorithms
- Machine learning with shallow neural networks
- Teaching and training deep learners
- Radial basis function networks
- Restricted Boltzmann machines
Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning by James V. Stone covers the mathematics that run autonomous deep learning.
It introduces the major historical and modern neural network learning algorithms, showing the mathematics behind each one along with a step-by-step pseudocode summary.
This AI book recommendation is best for people who understand calculus and even better if the readers understand programming.
With those qualifications aside, this book should be an engaging read for math and programming-savvy readers. It discusses complex concepts without getting bogged down in theoretical minutiae.
This provides a helpful overview of deep learning algorithms for those in mathematical or programming fields.
Key Learning Topics:
- Linear associative networks
- Perceptrons
- Backpropagation algorithms
- Hopfield nets
- Boltzmann machines
- Variational autoencoders
- Deep backprop networks
- Reinforcement learning
Python: Advanced Guide to Artificial Intelligence
Python: Advanced Guide to Artificial Intelligence by Giuseppe Bonaccorso, Armando Fandango, and Rajalingappaa Shanmugamani is a guide to understanding and mastering machine learning algorithms, deep learning models, and deep neural networks.
Readers learn how to use Python to create advanced machine learning systems and intelligent agents. This book starts with a fundamental machine learning briefer then gets into supervised, semi-supervised, and reinforcement algorithms.
It teaches how to develop, implement, and use machine learning algorithms and deep learning models using Python-based libraries, TensorFlow, and Keras.
These are complex subjects that require at least ground-level knowledge of Python programming and machine learning concepts. For those reasons, this recommendation is a good machine learning book for data scientists, machine learning engineers, and artificial intelligence engineers.
Key Learning Topics:
- Machine learning models
- Supervised, semi-supervised, and unsupervised learning
- TensorFlow for classical machine learning, neural networks, MLP, RNN, CNN, and autoencoders
- TensorFlow model debugging
Neural Networks from Scratch in Python
Neural Networks from Scratch in Python by Harrison Kinsley and Daniel Kukieła teaches readers how to do exactly that – build neural networks from absolute scratch, without referring to libraries. It aims to help readers get a more concrete and firm understanding of neural networks and deep learning by engaging in experiential study.
This computational intelligence book begins with instructions on how to code your first neuron. It builds on this with instructions on adding layers, activation functions, network error and loss, optimization, derivatives, regression, and other topics.
This book is intended to be both an instructional guide and a workbook. It has plenty of images, charts, and illustrated instructions to help readers work through it. The authors have also made tutorials and sample coding freely available on YouTube.
Key Learning Topics:
- Neuron coding
- Connecting neurons in layers
- Program activation functions
- Cross-entropy loss calculation
- Gradient computations
Boost Your AI Skills with the Best Courses
The best books on AI aren’t the only way to expand your knowledge. AI courses can also help you learn about this emerging technology, in a structured, guided, and supported way.
Here are three to consider:
- Generative AI Masterclass: AI solution builders Jeremy Kirshbaum and Lev Gorelov will teach you how to integrate cutting-edge, custom AI into your business and work.
- Storytelling with AI - Create your own illustrated book in a weekend: Ammaar Reshi, AI creator and publisher of a viral AI children's book, teaches how to use AI tools to brainstorm, write, illustrate, and publish a book within a weekend.
- Midjourney for Creatives: Creative Director Nick St. Pierre teaches how to use Midjourney across the entire image generation process, including for photography, illustrations, concept art, and storyboarding.
Interested in learning how to leverage AI for good instead of being technologically left behind? Maven has you covered with AI-related courses for creatives, strategists, project and product managers, and other professionals. Enroll in one today.
Related Courses
%20(1).jpg&w=1536&q=75)
AI for Business Leaders
Rated #1 AI Business Course. AI leadership essentials in 40-module curriculum for executives to build a stronger organization in the AI age.
.jpg&w=1536&q=75)
Creative AI Bootcamp
Master AI in your daily creative process: From concept to compelling visuals. Beginner to advanced and no tech skills required.
.jpg&w=1536&q=75)
Coworking with AI
Learn the Fundamentals of incorporating AI into your work; the Tips, Tricks, Tools and Optimizations that will save your company and career
How to train your AI model with Personal AI
Learn how to create an AI-powered digital twin based on your data with Personal AI.
AI for Leaders
Highest Rated AI Leadership Course. Elevate Your Leadership: Harness AI to Innovate, Make Strategic Decisions, and Advance Your Career
AI for Admins
Join the "Understanding AI for Administrative Professionals" course to learn how to leverage AI for efficiency and future-proof your career.
You might also like
9 Machine Learning Models Easily Explained
13 AI Courses to Help You Master Artificial Intelligence in 2023
8 Best AI Platforms You Must Check Today
