With the explosive growth of Artificial Intelligence in recent times. AI has found its way into our everyday lives, transforming the way we live and do things. If you’ve been wanting to know more about this awesome technology – of course, that’s what this blog is all about – I have taken the time to curate this list of 14 books on artificial intelligence to help you get started on your learning journey.
Here are some of the best books out there on Artificial Intelligence. Happy reading!
1. Deep Learning: Foundations and Concepts
This book provides a comprehensive introduction to the fundamental concepts of deep learning, catering to both beginners in machine learning and experienced practitioners. It covers key ideas related to contemporary architectures and techniques, emphasizing practical applications rather than abstract theory.
Organized into bite-sized chapters with a linear progression, the book is suitable for two-semester undergraduate or postgraduate machine learning courses and is relevant to active researchers or self-study. The authors, Chris Bishop and Hugh Bishop, offer multiple perspectives, including textual descriptions, diagrams, mathematical formulas, and pseudo-code, to explain complex concepts.
While a foundational understanding of machine learning requires some mathematical background, the focus is on conveying clear ideas with real-world practical value. The book also includes a self-contained introduction to probability theory. Endorsed by experts like Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, it is praised for bridging the gap in modern deep learning literature and staying focused on enduring ideas in the rapidly evolving field.
2. Superintelligence: Paths, Dangers, Strategies
“Superintelligence” by Nick Bostrom explores the implications of machines surpassing humans in general intelligence, raising questions about whether artificial agents will save or potentially destroy humanity.
Bostrom emphasizes the unique capabilities of the human brain and discusses the potential consequences if machine superintelligence were to exceed human control. The book highlights the critical role of humans in shaping the future, suggesting the possibility of creating a seed Artificial Intelligence to guide the development of superintelligence in a controlled and survivable manner.
Bostrom’s work is described as profoundly ambitious, offering a reconceptualization of the essential task facing humanity in navigating the future of intelligent life.
3. Deep Learning (Adaptive Computation and Machine Learning Series)
“Deep Learning” is a comprehensive book introducing a wide range of topics in deep learning, a form of machine learning that enables computers to learn from experience and understand the world hierarchically.
Authored by three experts, the book covers mathematical and conceptual backgrounds, including linear algebra, probability theory, information theory, numerical computation, and machine learning. It explores deep learning techniques used in industry, such as deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology.
The book also delves into applications like natural language processing, speech recognition, computer vision, recommendation systems, bioinformatics, and videogames. Additionally, it provides research perspectives on theoretical topics like linear factor models, autoencoders, representation learning, probabilistic models, Monte Carlo methods, and deep generative models.
Suitable for undergraduate or graduate students, as well as software engineers, the book is endorsed by Elon Musk and is considered the only comprehensive guide on the subject. Supplementary material is available on a dedicated website for both readers and instructors.
4. The Hundred-Page Machine Learning Book
“The Hundred-Page Machine Learning Book” by Andriy Burkov is praised by industry experts and practitioners for its concise yet comprehensive coverage of machine learning. Peter Norvig commends Burkov for successfully condensing the vast field of machine learning into 100 pages, providing a solid introduction for practitioners.
Aurélien Géron applauds the book’s breadth of topics, including mathematical equations often omitted in shorter works. Karolis Urbonas hails it as a great introduction from a world-class practitioner, while Chao Han wishes such a resource existed during his statistics graduate studies.
Sujeet Varakhedi appreciates the book’s efficiency in cutting through complexity, and Deepak Agarwal finds it wonderful for engineers looking to incorporate machine learning without extensive time commitments. Vincent Pollet describes it as an excellent read to start with machine learning, and Gareth James predicts it will become a go-to resource for academics and practitioners alike.
Despite its brevity, the book covers major machine learning approaches, making it accessible to a wide audience without assuming advanced mathematical or programming knowledge. It’s recommended for beginners and experienced practitioners alike, serving as both a learning tool and a handy reference. In essence, the book is described as everything one needs to know about machine learning in a hundred pages.
5. Reinforcement Learning
The second edition of “Reinforcement Learning” by Richard Sutton and Andrew Barto is an updated and expanded text on one of the most active research areas in artificial intelligence. Reinforcement learning is a computational approach where an agent aims to maximize its cumulative reward while navigating a complex and uncertain environment.
The authors provide a clear and straightforward account of the key ideas and algorithms in the field. The new edition includes additional topics and updates, with a focus on core online learning algorithms. Part I covers reinforcement learning in the tabular case, introducing new algorithms like UCB, Expected Sarsa, and Double Learning.
Part II extends these concepts to function approximation, incorporating artificial neural networks and the Fourier basis, along with expanded discussions on off-policy learning and policy-gradient methods. Part III introduces new chapters on reinforcement learning’s connections to psychology and neuroscience, along with updated case studies featuring AlphaGo, AlphaGo Zero, Atari game playing, and IBM Watson’s wagering strategy. The final chapter explores the potential societal impacts of reinforcement learning in the future.
6. Artificial Intelligence: A Modern Approach, Global Edition
The fourth edition of “Artificial Intelligence: A Modern Approach” is a long-anticipated revision that comprehensively explores the field of artificial intelligence (AI). This edition updates readers on the latest technologies, presents concepts in a more unified manner, and includes new or expanded coverage of various topics, such as machine learning, deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
The revision aims to provide a thorough and contemporary overview of AI, addressing the latest developments and advancements in the field.
7. Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
This advanced textbook is designed for researchers and graduate students in machine learning and statistics, providing in-depth coverage of cutting-edge topics such as deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. Serving as an advanced counterpart to “Probabilistic Machine Learning: An Introduction,” the book places deep learning within a broader statistical context, bridging approaches based on deep learning with those rooted in probabilistic modeling and inference.
Authored by leading scientists and domain experts from institutions like Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, the book addresses essential issues in machine learning. Topics covered include the generation of high-dimensional outputs (images, text, graphs), insights from data based on latent variable models, training and testing under different distributions, using probabilistic models and inference for causal inference and decision making, and includes online Python code accompaniment.
8. The Artificial Intelligence and Generative AI Bible
“The Artificial Intelligence and Generative AI Bible” is a comprehensive guide designed to help individuals understand and navigate the complexities of Artificial Intelligence (AI) and Generative AI. The book covers a wide range of topics, from basic AI principles to advanced subjects like Generative Adversarial Networks (GANs), Natural Language Processing (NLP), deep learning, ethics, and the future of AI. The guide is divided into five books, covering AI basics, AI and deep learning, NLP in AI, Generative AI and Ethics, and prompts, creative AI, and future applications.
Key highlights include a thorough understanding of AI and its subfields, insights into the principles and techniques of Generative AI, practical applications of AI across various industries, the latest advancements in NLP and speech recognition, training and deploying Deep Learning models, ethical considerations in AI, and exploration of GANs and their applications. The book also discusses the role of prompts and human-AI collaboration in creative AI, real-world case studies showcasing AI solving complex problems, and futuristic insights into the potential impact and future of AI.
“The Artificial Intelligence and Generative AI Bible” aims to empower readers with practical examples, expert insights, and a future-oriented approach, helping them leverage AI to drive innovation and create groundbreaking solutions. The guide encourages readers to become AI experts and stay at the forefront of technological innovation.
9. Artificial Intelligence and Its Transformative Impact on Healthcare
This book offers a comprehensive overview of the transformative impact of artificial intelligence (AI) on the healthcare industry. Covering various aspects such as medical imaging, drug discovery, clinical decision-making, patient monitoring, and more, the book explores the evolution of AI in healthcare, its advantages and disadvantages, applications in patient care and revenue cycle management, security and privacy considerations, as well as challenges and limitations.
Aimed at healthcare professionals, researchers, policymakers, and those interested in the intersection of AI and healthcare, the book provides valuable insights into the potential benefits of AI in healthcare and addresses the real-world impact through case studies and practical applications. It serves as an essential resource for understanding the current landscape and future possibilities of AI in the healthcare industry.
10. Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
This textbook provides a comprehensive introduction to machine learning using a unified, probabilistic approach. It addresses the challenges posed by today’s abundance of electronic data and focuses on automated methods of data analysis. The content covers a wide range of topics, including probability, optimization, and linear algebra, alongside recent developments like conditional random fields, L1 regularization, and deep learning.
The book adopts an informal and accessible style, incorporating pseudo-code for key algorithms and utilizing color images and worked examples from diverse application domains such as biology, text processing, computer vision, and robotics. Emphasizing a principled model-based approach, the book often employs graphical models to express concepts concisely.
The included MATLAB software package, PMTK (probabilistic modeling toolkit), implements many of the models discussed. Suitable for upper-level undergraduates with a basic math background and introductory-level graduate students, the book offers a solid foundation in machine learning principles.
11. Active Learning with AI: A Practical Guide
In “Artificial Intelligence and Education,” Stephen M. Kosslyn addresses the potential of Artificial Intelligence (AI) to enhance teaching and learning. Recognizing the challenges and concerns educators may face in navigating this field, Kosslyn aims to demystify AI and its relationship to education.
The book provides step-by-step illustrations and examples, offering educators and course designers the tools to understand AI concepts and integrate them into the creation of active learning exercises and lesson plans. Whether in traditional classrooms, online settings, or using a hybrid approach, the book informs and empowers educators to develop more engaging, personalized, and scalable learning experiences for their students.
12. From Deep Learning to Rational Machines: What the History of Philosophy Can Teach Us about the Future of Artificial Intelligence
“This book offers a framework for contemplating foundational philosophical questions related to the use of deep artificial neural networks (deep learning) for achieving artificial intelligence. It establishes connections between recent breakthroughs in deep learning and classic works in empiricist philosophy of mind.
The discussion revolves around the historical debate between nativism and empiricism, particularly concerning the origins of abstract knowledge. Empiricists, who were faculty psychologists, argued that extracting abstract knowledge from experience involves the active engagement of psychological faculties like perception, memory, imagination, attention, and empathy.
The book demonstrates how recent breakthroughs in deep learning have realized ambitious ideas about these faculties, drawing on the works of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke, David Hume, William James, and Sophie de Grouchy. It highlights the interdisciplinary connection’s utility, illustrating how it benefits both philosophy and computer science.
Computer scientists can glean ideas and aspirational goals from the history of philosophy, while philosophers can observe how some of the historical empiricists’ speculative ideas can now be realized in specific computational systems.”
13. 80 Ways to Use ChatGPT in the Classroom: Using AI to Enhance Teaching and Learning
In “80 Ways to Use ChatGPT in the Classroom: Using AI to Enhance Teaching and Learning,” Dr. Stan Skrabut, an instructional technologist and designer, explores the impact of ChatGPT on the classroom setting. He delves into questions about its implications, whether educators should be concerned, and whether they should choose to use it or not.
The book provides various ways to integrate ChatGPT into the classroom environment, covering aspects such as class preparation, instructional assistance, assessment creation, study aids, and even incorporating it into computer programming education. Additionally, the book addresses how students can leverage ChatGPT for writing assistance, research, and personalized learning.
Dr. Skrabut offers practical examples and suggestions to help educators maximize ChatGPT’s capabilities while addressing potential concerns and criticisms. The overarching goal of the book is to demonstrate how AI, specifically ChatGPT, can enhance the teaching and learning experience. It encourages educators to embrace this technology in the classroom, presenting a diverse array of potential applications.
14. AI For Dummies (For Dummies (Computer/Tech))
“Artificial Intelligence For Dummies” offers a clear introduction to the field of artificial intelligence (AI), addressing common misconceptions and providing an overview of its applications. The book explores the contributions of AI to society, its uses in computer applications, and its limitations.
It delves into various applications, from self-driving cars and drones to its impact on the medical field. The guide aims to make the world of AI more accessible, providing readers with a comprehensive and hands-on understanding of this evolving technology.
Hope you found the list useful. Are there other nice books on artificial intelligence you’ll like to share with us? please leave us your comments.
Software Engineer | AI Tech Enthusiast | Content Writer