The most recommended deep learning books

Who picked these books? Meet our 11 experts.

11 authors created a book list connected to deep learning, and here are their favorite deep learning books.
When you buy books, we may earn a commission that helps keep our lights on (or join the rebellion as a member).

What type of deep learning book?

Loading...

Book cover of Architects of Intelligence: The truth about AI from the people building it

Paul Thagard Author Of Bots and Beasts: What Makes Machines, Animals, and People Smart?

From my list on intelligence in humans, animals, and machines.

Why am I passionate about this?

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.  

Paul's book list on intelligence in humans, animals, and machines

Paul Thagard Why did Paul love this book?

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.

By Martin Ford,

Why should I read it?

1 author picked Architects of Intelligence as one of their favorite books, and they share why you should read it.

What is this book about?

Financial Times Best Books of the Year 2018

TechRepublic Top Books Every Techie Should Read

Book Description

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…


Book cover of Advanced Methods and Deep Learning in Computer Vision

Mark S. Nixon Author Of Feature Extraction and Image Processing for Computer Vision

From my list on computer vision from a veteran professor.

Why am I passionate about this?

It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.

Mark's book list on computer vision from a veteran professor

Mark S. Nixon Why did Mark love this book?

The advances of deep learning have been awesome, and fast. It’s been hard for the textbooks to keep up, so it’s good to include one that describes the advances and state of art very well. It seems appropriate that it’s edited by two leading researchers who are Roy – who described computer vision systems implementations in a long series of excellent books – and Matt, whose work on face recognition revolutionised and transformed the progress of face recognition in the 1990s. This book gives you an image of where we are now in computer vision, and where we are going. 

By E.R. Davies (editor), Matthew Turk (editor),

Why should I read it?

1 author picked Advanced Methods and Deep Learning in Computer Vision as one of their favorite books, and they share why you should read it.

What is this book about?

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection.

This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as…


Book cover of Deep Learning with Python

Jakub Langr Author Of GANs in Action: Deep Learning with Generative Adversarial Networks

From my list on applied deep learning.

Why am I passionate about this?

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.

Jakub's book list on applied deep learning

Jakub Langr Why did Jakub love this book?

This is a fantastic book to get you started. It is written by the author of a leading deep learning framework Keras, which makes even Tensorflow very easy to use. Chollet is a true leader of the deep learning craft and the Manning team always does an excellent job of forcing authors to make the subject matter accessible. Highly recommended!

By Francois Chollet,

Why should I read it?

2 authors picked Deep Learning with Python as one of their favorite books, and they share why you should read it.

What is this book about?

"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook

The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher Francois Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning…


Book cover of How to Measure Anything: Finding the Value of Intangibles in Business

Jakub Langr Author Of GANs in Action: Deep Learning with Generative Adversarial Networks

From my list on applied deep learning.

Why am I passionate about this?

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.

Jakub's book list on applied deep learning

Jakub Langr Why did Jakub love this book?

While technically not about deep learning, this book is fantastic for those interested in pursuing applied or practical machine learning problems. While the central thesis of a topic can be reduced to “Frequently, models are valuable simply by reducing uncertainty,” it is definitely worth a read as there’s a lot of deep thinking in this book!

By Douglas W. Hubbard,

Why should I read it?

1 author picked How to Measure Anything as one of their favorite books, and they share why you should read it.

What is this book about?

Now updated with new measurement methods and new examples, How to Measure Anything shows managers how to inform themselves in order to make less risky, more profitable business decisions This insightful and eloquent book will show you how to measure those things in your own business, government agency or other organization that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI. * Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfaction * Simplifies overall content while still making the…


Book cover of Understanding Deep Learning

Ron Kneusel Author Of How AI Works: From Sorcery to Science

From my list on the background and foundation of AI.

Why am I passionate about this?

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.

Ron's book list on the background and foundation of AI

Ron Kneusel Why did Ron love this book?

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.

By Simon J.D. Prince,

Why should I read it?

1 author picked Understanding Deep Learning as one of their favorite books, and they share why you should read it.

What is this book about?

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…


Book cover of Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

Valliappa Lakshmanan Author Of Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and Mlops

From my list on to become a machine learning engineer.

Why am I passionate about this?

I have been building real-time, production machine learning models for over 20 years. My book, and my book recommendations, are informed by that experience. I have a lot of empathy for people who are new to machine learning because I’ve taught courses on the topic. I founded the Advanced Solutions Lab at Google where we helped data scientists working for Google Cloud customers (who already knew ML) become ML engineers capable of building reliable ML models. The first two are the books I’d recommend today to newcomers and the last three to folks attending the ASL. 

Valliappa's book list on to become a machine learning engineer

Valliappa Lakshmanan Why did Valliappa love this book?

This recommendation is a bit of a cheat — I’m not recommending this exact book, but one of the books in the series that this book is part of.

Once you have the first two books under your belt, you’ll know how to solve ML problems. But you will keep reinventing the wheel. What you need next is a book on common “ML tricks” — best practices and common techniques when doing ML in production.

The problem is that these tricks are specific to the type of data that you will be processing. If you are going to be processing images or time series, read the corresponding books in the same series instead.

By Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta

Why should I read it?

1 author picked Practical Natural Language Processing as one of their favorite books, and they share why you should read it.

What is this book about?

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.

Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You'll learn how to…


Book cover of Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD

Jakub Langr Author Of GANs in Action: Deep Learning with Generative Adversarial Networks

From my list on applied deep learning.

Why am I passionate about this?

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.

Jakub's book list on applied deep learning

Jakub Langr Why did Jakub love this book?

Jeremy Howard is the lead author and has always been a world-class educator. This book is based on his fast.ai course, which has managed to splice all rigor, simplicity, and cutting edge techniques into one course. It also uses its custom fast.ai framework built on PyTorch, which is the dominant language for researchers. This book is very practically oriented and gets you off the ground very quickly with your own projects!

By Jeremy Howard, Sylvain Gugger,

Why should I read it?

2 authors picked Deep Learning for Coders with Fastai and Pytorch as one of their favorite books, and they share why you should read it.

What is this book about?

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.

Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to…


Book cover of Artifictional Intelligence: Against Humanity's Surrender to Computers

Peter J. Bentley Author Of Artificial Intelligence and Robotics: Ten Short Lessons

From my list on no hype and no nonsense artificial intelligence.

Why am I passionate about this?

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?

Peter's book list on no hype and no nonsense artificial intelligence

Peter J. Bentley Why did Peter love this book?

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.

By Harry Collins,

Why should I read it?

1 author picked Artifictional Intelligence as one of their favorite books, and they share why you should read it.

What is this book about?

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…


Book cover of Resisting AI: An Anti-fascist Approach to Artificial Intelligence

Arshin Adib-Moghaddam Author Of Is Artificial Intelligence Racist? The Ethics of AI and the Future of Humanity

From my list on future technologies and the ethics of AI.

Why am I passionate about this?

Arshin Adib-Moghaddam is Professor in Global Thought and Comparative Philosophies at SOAS University of London and Fellow of Hughes Hall, University of Cambridge. Among over a dozen honorary appointments all over the world, Adib-Moghaddam is the inaugural Director of the SOAS Centre for AI Futures.

Arshin's book list on future technologies and the ethics of AI

Arshin Adib-Moghaddam Why did Arshin love this book?

A fantastic expose about the perils of Artificial Intelligence written with clear passion for a just and equitable AI future.

This book serves as an introduction into AI’s deep learning technology and its political effects. In easily digestible prose, it charters the ways that AI impacts society and how it feeds into various social predicaments, such as the rise of right-wing movements in Europe and North America. 

By Dan McQuillan,

Why should I read it?

1 author picked Resisting AI as one of their favorite books, and they share why you should read it.

What is this book about?

Artificial Intelligence (AI) is everywhere, yet it causes damage to society in ways that can't be fixed. Instead of helping to address our current crises, AI causes divisions that limit people's life chances, and even suggests fascistic solutions to social problems. This book provides an analysis of AI's deep learning technology and its political effects and traces the ways that it resonates with contemporary political and social currents, from global austerity to the rise of the far right.
Dan McQuillan calls for us to resist AI as we know it and restructure it by prioritising the common good over algorithmic…


Book cover of Grokking Deep Learning

Jakub Langr Author Of GANs in Action: Deep Learning with Generative Adversarial Networks

From my list on applied deep learning.

Why am I passionate about this?

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.

Jakub's book list on applied deep learning

Jakub Langr Why did Jakub love this book?

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.

By Andrew W. Trask,

Why should I read it?

1 author picked Grokking Deep Learning as one of their favorite books, and they share why you should read it.

What is this book about?

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…


Book cover of Architects of Intelligence: The truth about AI from the people building it
Book cover of Advanced Methods and Deep Learning in Computer Vision
Book cover of Deep Learning with Python

Share your top 3 reads of 2024!

And get a beautiful page showing off your 3 favorite reads.

1,881

readers submitted
so far, will you?