Here are 100 books that The Deep Learning Revolution fans have personally recommended if you like
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Since childhood I've been fascinated with the beauty of organic molecules. I pursued this passion in graduate school at Brown University and through a postdoctoral position at Stanford University. My professional career began at a startup pharmaceutical company in California, which evolved into research positions in agriculture and food ingredients. After 30 years I retired as a vice-president of research and development for a food ingredients company. I developed a passion for food and cooking and subsequently acquired a position as the science editor for America’s Test Kitchen, which I held for over 12 years. Today at the age of 80 I still write and publish scientific papers and books about food, cooking, and nutrition.
This book was the inspiration for my book and was written by a professor of Biological Anthropology at Harvard University. It sets out a convincing argument that cooking may have been started by the earliest humans about 2 million years ago, which is far earlier than most anthropologists believe. Much of Wrangham’s arguments are based on his own research that illustrates how cooking provided better nutrition resulting in the expansion of the human brain by 60% over thousands of years giving humans a head-start over all other living species.
In this stunningly original book, Richard Wrangham argues that it was cooking that caused the extraordinary transformation of our ancestors from apelike beings to Homo erectus. At the heart of Catching Fire lies an explosive new idea: The habit of eating cooked rather than raw food permitted the digestive tract to shrink and the human brain to grow, helped structure human society, and created the male-female division of labour. As our ancestors adapted to using fire, humans emerged as "the cooking apes".
Covering everything from food-labelling and overweight pets to raw-food faddists, Catching Fire offers a startlingly original argument about…
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.
If flavorful food has been a critical element in the evolution of our large brains, how did large brains give rise to our high intelligence? This is to be found in the circuits of our cerebral cortex and the regions to which it is connected. Daeyeol Lee is one of the leaders in research on how the cerebral cortex generates behavior in monkeys, for its insights into how this occurs in humans. This is providing new ways to define the neural basis of intelligence based on the application of new single-cell recording techniques in primates and brain scanning techniques in humans.
With his approach based on a deep understanding of how primates gave rise to humans, Lee asks the critical questions: What is intelligence? How did it evolve from monkeys to humans? Can computers and artificial intelligence ever equal human biological intelligence in all its complexity? Based on Lee’s research…
What is intelligence? How did it begin and evolve to human intelligence? Does a high level of biological intelligence require a complex brain? Can man-made machines be truly intelligent? Is AI fundamentally different from human intelligence? In Birth of Intelligence, distinguished neuroscientist Daeyeol Lee tackles these pressing fundamental issues. To better prepare for future society and its technology, including how the use of AI will impact our lives, it is essential to understand the biological root and limits of human intelligence. After systematically reviewing biological and computational underpinnings of decision making and intelligent behaviors, Birth of Intelligence proposes that true…
I’m a historian of science fascinated by how scientists cope with uncertainty. I’m drawn to books that identify and try to explain the gaps in scientific knowledge and describe ways of knowing that might not be called scientific. I love to read stories about how ordinary people discover extraordinary things about their environments. I’m always curious about what happens when savvy locals are visited by scientific experts. Will they join forces? Admit what they don’t know? Or is a struggle brewing?
I was captivated by an insight that came to Firestein while he was teaching college biology. Science courses typically teach what scientists know about their disciplines, but what’s most exciting to scientists is what they don’t know.
So Firestein had the brilliant idea to design a course where scientists would share their “ignorance”—the questions that keep them up at night and propel new research. It helps that the author used to work as a stand-up comic!
Knowledge is a big subject, says Stuart Firestein, but ignorance is a bigger one. And it is ignorance-not knowledge-that is the true engine of science.
Most of us have a false impression of science as a surefire, deliberate, step-by-step method for finding things out and getting things done. In fact, says Firestein, more often than not, science is like looking for a black cat in a dark room, and there may not be a cat in the room. The process is more hit-or-miss than you might imagine, with much stumbling and groping after phantoms. But it is exactly this "not…
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.
Many years ago a young neuroscientist asked to visit me; he had just come to the U.S. from Brazil and was seeking advice on a lab he could join to train in the function of nerve cells in the cerebral cortex. He soon became spectacularly successful, showing that the brain forms different perceptions and controls different movements by overlapping distributions of cortical neurons that constitute an internal reality of the external world.
Building on this knowledge, Nicolellis has led the way in constructing brain-machine interfaces to enable a patient, for example, to learn to walk after suffering a stroke. In doing so, he has come to realize that everything humans experience in our lives is due to the reality constructed by the brain to represent the reality of the external world. As he expresses it, brain reality is the true creator of everything. Some may find this new view disturbing,…
A radically new cosmological view from a groundbreaking neuroscientist who places the human brain at the center of humanity's universe
Renowned neuroscientist Miguel Nicolelis introduces a revolutionary new theory of how the human brain evolved to become an organic computer without rival in the known universe. He undertakes the first attempt to explain the entirety of human history, culture, and civilization based on a series of recently uncovered key principles of brain function. This new cosmology is centered around three fundamental properties of the human brain: its insurmountable malleability to adapt and learn; its exquisite ability to allow multiple individuals…
I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.
This book is a fantastic intro to someone who really wants to intuitively understand deep learning. It can help you clear up things where you are stuck or simply if you’re having trouble explaining parts of your algorithm to your business stakeholders. It is also a really good preparation if you want a really solid, practical basis to come up with new tweaks or types of models.
Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the "brain" behind the world's smartest Artificial Intelligence systems out there.
Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the "black box" API of some library or framework, readers will actually understand how to build these algorithms completely from scratch.
Key Features: Build neural networks that can see and understand images Build an A.I. that will learn to defeat you in a classic Atari game Hands-on Learning
Written for readers with high school-level math and…
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…
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.
Goodfellow’s Deep Learning is a must in the field because it was the first. Prince’s new book is an essential follow-up to be up-to-date with the latest model types, including diffusion models (think Stable Diffusion or DALL-E), transformers (the heart of large language models), graph networks (reasoning over relationships), and reinforcement learning.
The math level is similar to what you’ll find in Goodfellow’s book.
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced…
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.
