Here are 100 books that Understanding Deep Learning fans have personally recommended if you like
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As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Deep learning burst on the scene in 2012 with the success of the AlexNet model in the ImageNet competition. The first comprehensive deep learning text was this one, released in 2016.
It’s almost a necessity for deep learning practitioners, but it is not for beginners. Think of it as a graduate-level text. After eight years, some portions read as slightly dated, but the essentials have not changed.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Woolridge presents the history of artificial intelligence from the point of view of an insider. This book is one of the few accounts of AI history presenting a measured perspective, one that has weathered more than one boom and bust cycle.
The book is nicely complemented by his recent series of lectures, which can be easily found on YouTube. I read Woolridge as saying, “Yes, something new has happened with the advent of large language models, but much work remains.”
From Oxford's leading AI researcher comes a fun and accessible tour through the history and future of one of the most cutting edge and misunderstood field in science: Artificial Intelligence
The somewhat ill-defined long-term aim of AI is to build machines that are conscious, self-aware, and sentient; machines capable of the kind of intelligent autonomous action that currently only people are capable of. As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Artificial intelligence is, of necessity, an academic pursuit, at least initially. McCorduck’s book is her account of the history and development of AI. She was not a historian coming to events after the fact but a living witness. Her circle of friends included all the key figures, the people those of us who fell into AI later didn’t have the opportunity to know.
This book, personal and human, not technical and heavy, reveals the humanness of the process. Yes, artificial intelligence was the goal, but human intelligence (and frailty) were central to its emergence.
In the autumn of 1960, twenty-year-old humanities student Pamela McCorduck encountered both the fringe science of early artificial intelligence, and C. P. Snow's Two Cultures lecture on the chasm between the sciences and the humanities. Each encounter shaped her life. Decades later her lifelong intuition was realized: AI and the humanities are profoundly connected. During that time, she wrote the first modern history of artificial intelligence, Machines Who Think, and spent much time pulling on the sleeves of public intellectuals, trying in futility to suggest that artificial intelligence could be important. Memoir, social history, group biography of the founding fathers…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Alan Turing’s 1936 paper “On Computable Numbers, with an Application to the Entscheidungsproblem” was foundational to the development of computer science. To this day, Turing machines, the theoretical computational devices imagined in Turing’s paper, are a research cornerstone as they embody the concept of “computable.” If a programming language can implement a Turing machine, then the language is deemed Turing complete and is, therefore, general-purpose enough to implement any algorithm.
Turing’s paper is readable, but Petzold’s book breaks it down in minute detail to explain the nomenclature and meaning behind Turing’s words. I believe all computer science students should study this paper, and you’ll be hard-pressed to find a more thorough review than the one presented in this book.
Programming Legend Charles Petzold unlocks the secrets of the extraordinary and prescient 1936 paper by Alan M. Turing
Mathematician Alan Turing invented an imaginary computer known as the Turing Machine; in an age before computers, he explored the concept of what it meant to be computable, creating the field of computability theory in the process, a foundation of present-day computer programming.
The book expands Turing's original 36-page paper with additional background chapters and extensive annotations; the author elaborates on and clarifies many of Turing's statements, making the original difficult-to-read document accessible to present day programmers, computer science majors, math geeks,…
I started my career in neuroscience. I wanted to understand brains. That is still proving difficult, and somewhere along the way, I realized my real motivation was to build things, and I wound up working in AI. I love the elegance of mathematical models of the world. Even the simplest machine learning model has complex implications, and exploring them is a joy.
Of course, this is not the obvious book to recommend for reinforcement learning, but if you are a beginner, then it’s a quick and easy place to start. It’s compact and gets straight into the main algorithms.
It has a good balance between theory and code and will get you up and running quickly.
The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice
Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games-such as Go, Atari games, and DotA 2-to robotics.
Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM…
I became fascinated by the highest achievements of human intelligence while a graduate student in philosophy working on the discovery and justification of scientific theories. Shortly after I got my PhD, I started working with cognitive psychologists who gave me an appreciation for empirical studies of intelligent thinking. Psychology led me to computational modeling of intelligence and I learned to build my own models. Much later a graduate student got me interested in questions about intelligence in non-human animals. After teaching a course on intelligence in machines, humans, and other animals, I decided to write a book that provides a systematic comparison: Bots and Beasts.
This book provides a good introduction to the current state of machine intelligence through interviews with many leading practitioners including Geoffrey Hinton, Yann LeCun, Stuart Russell, and Demis Hassabis (DeepMind). You will get a sense of both of AI’s recent accomplishments and how far it falls short of full human intelligence.
How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances?
Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community.
Martin has wide-ranging conversations with twenty-three…
I was stimulated by Norbert Wiener’s “Cybernetics” to study circuits in the brain that control behavior. For my graduate studies, I chose the olfactory bulb for its experimental advantages, which led to constructing the first computer models of brain neurons and microcircuits. Then I got interested in how the smell patterns are activated when we eat food, which led to a new field called Neurogastronomy, which is the neuroscience of the circuits that create the perception of food flavor. Finally, because all animals use their brains to find and eat food, the olfactory system has provided new insights into the evolution of the mammalian brain and the basic organization of the cerebral cortex.
The other books in this series are mostly about the real brain. But artificial intelligence promises us a new enhanced brain. What does the future hold? Terrence Sejnowski is a neuroscientist who was one of the first to realize the potential of AI. Since he has been there from the start, in this book he gives the reader an exciting inside story on the people and the advances that are reshaping our lives.
Early attempts at AI were limited, but once computational power took off big computers running multilayer neural nets began proving that they could defeat humans at the most demanding games, enhance human capabilities such as pattern recognition, text recognition, language translation, and driverless vehicles, and work to obtain rewards, just like a human. While these advances are dramatic, it is well to remember that the networks are built not from representations of real neurons, but rather from…
How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy.
The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.
I’ve been a geeky kid all my life. (I don’t think I’ve quite grown up yet.) Born in the 1970s, my childhood was a wonderful playground of building robots and software. I was awarded one of the early degrees in AI, and a PhD in genetic algorithms. I’ve since spent 25 years exploring how to make computers think, build, invent, compose… and I’ve also spent 20 years writing popular science books. I’m lucky enough to be a Professor in one of the world’s best universities for Computer Science and Machine Learning: UCL, and I guess I’ve written two or three hundred scientific papers over the years. I still think I know nothing at all about real or artificial intelligence, but then does anyone?
I’ve not met Harry, but he seems to have a logical and sensible head on his shoulders. His writing is considered and grounded, which is exactly what you need when discussing the hype that forever seems to surround AI. This book is another look at this topic and finds yet more ways to explain to readers the difference between human intelligence and our algorithmic attempts at intelligence – which are frequently pretty stupid.
Recent startling successes in machine intelligence using a technique called 'deep learning' seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the 'Surrender'.
By dissecting the intricacies of language use and meaning, Collins shows how far…
Coming from two very different backgrounds gives Dean and I a unique ‘view’ of a topic that we are both hugely passionate about: artificial intelligence. Our work together has gifted us a broader perspective in terms of understanding the development of and the philosophy beneath what is coined as artificial intelligence today and where we truly stand in terms of its potential for good – and evil. Our book list is intended to provide a great starting point from where you can jump into this incredibly absorbing topic and draw your own conclusions about where the future might take us.
Don't be fooled by the lack of a breezy narrative. This read is a dense exploration of deep learning's impact and is certainly not an ‘easy read’ by any measure, but its rewards are substantial.
Buckner delves deep into the philosophical debates surrounding AI, particularly the clash between empiricism and rationalism. Through this lens, he develops a "moderate empiricism" that sheds light on the true potential and limitations of AI. While the book demands focus, we found the payoff to be significant.
This book provides a framework for thinking about foundational philosophical questions surrounding the use of deep artificial neural networks ("deep learning") to achieve artificial intelligence. Specifically, it links recent breakthroughs to classic works in empiricist philosophy of mind. In recent assessments of deep learning's potential, scientists have cited historical figures from the philosophical debate between nativism and empiricism, which concerns the origins of abstract knowledge. These empiricists were faculty psychologists; that is, they argued that the extraction of abstract knowledge from experience involves the active engagement of psychological faculties such as perception, memory, imagination, attention, and empathy. This book explains…
I started my career in neuroscience. I wanted to understand brains. That is still proving difficult, and somewhere along the way, I realized my real motivation was to build things, and I wound up working in AI. I love the elegance of mathematical models of the world. Even the simplest machine learning model has complex implications, and exploring them is a joy.
This is a popular science book, so a little different from the others on this list. It is a beautifully written book that is accessible to people who don’t know much about AI but is simultaneously thought-provoking for experts.
It contains probably the best discussion of "intelligence" that I've read, interesting insights into how Google and other tech giants came to develop their machine learning strategy, and a fascinating chapter that views recommendation engines and their users as parts of a single intelligent organism. It's concise and easy to read.
I've read many popular AI books, which are highly variable in quality, and this criminally underappreciated work is the best by miles.
- The author is one of the most influential AI reseachers of recent decades.
- Written in an accessible language, the book provides a probing account of AI today and proposes a new narrative to connect and make sense of events that happened in the recent tumultuous past and enable us to think soberly about the road ahead.
- The book is divided into ten carefully crafted and easily-digestible chapters, each grapples with an important question for AI, ranging from the scientific concepts that underpin the technology to wider implications for society, using real examples wherever possible.
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