72 books like Deep Learning with Python

By Francois Chollet,

Here are 72 books that Deep Learning with Python fans have personally recommended if you like Deep Learning with Python. Shepherd is a community of 11,000+ authors and super readers sharing their favorite books with the world.

Shepherd is reader supported. When you buy books, we may earn an affiliate commission.

Book cover of Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent 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?

There are three types of machine learning books — books written for people who want to become machine learning engineers, books written for people who want to become machine learning researchers, and books written for business executives. Reading a book written for researchers or executives can be a frustrating experience if you are a software engineer, social scientist, or mechanical engineer who wants to learn machine learning and get an ML job in the industry.

If you are a coder who wants to become an ML engineer, you have got to learn machine learning concepts, but you want to learn them in a practical way. You need a book that leads with intuition and shows you implementations with code. It has to do this without getting sidetracked into ML theory, getting mired in statistical concepts, or being so superficial that you don’t understand why the code works.…

By Géron Aurélien,

Why should I read it?

1 author picked Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow as one of their favorite books, and they share why you should read it.

What is this book about?

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help…


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 The Hundred-Page Machine Learning Book

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?

Even if you have the practical knowledge, it's sometimes necessary to understand the mathematical and theoretical concepts that underlie the machine learning approaches you are using. This book is a great introduction to the world of ML theory.

By Andriy Burkov,

Why should I read it?

1 author picked The Hundred-Page Machine Learning Book as one of their favorite books, and they share why you should read it.

What is this book about?

WARNING: will not work on e-ink Kindle devices!
Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."

Aurélien Géron, Senior AI Engineer, author of the…


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 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?

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.

By Ian Goodfellow, Yoshua Bengio, Aaron Courville

Why should I read it?

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

What is this book about?

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…


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 Foundations of Deep Reinforcement Learning: Theory and Practice in Python

Simon J.D. Prince Author Of Understanding Deep Learning

From my list on machine learning and deep neural networks.

Why am I passionate about this?

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.

Simon's book list on machine learning and deep neural networks

Simon J.D. Prince Why did Simon love this book?

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.

By Laura Graesser, Wah Loon Keng,

Why should I read it?

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

What is this book about?

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…


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 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems

Tomasz Lelek Author Of Software Mistakes and Tradeoffs: How to make good programming decisions

From my list on big data processing ecosystem.

Why am I passionate about this?

I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.

Tomasz's book list on big data processing ecosystem

Tomasz Lelek Why did Tomasz love this book?

The Hands-on Machine Learning book presents an end-to-end approach to many problems that can be solved with machine learning.

Every concept and topic is backed up with a running code that you can experiment with and adapt to your real-world problems.

Thanks to this book, you will be able to understand the state of the art of today's machine learning and feel comfortable using the most up-to-date ML methods.

By Géron Aurélien,

Why should I read it?

1 author picked Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e as one of their favorite books, and they share why you should read it.

What is this book about?

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout…


5 book lists we think you will like!

Interested in machine learning, python, and deep learning?

11,000+ authors have recommended their favorite books and what they love about them. Browse their picks for the best books about machine learning, python, and deep learning.

Machine Learning Explore 50 books about machine learning
Python Explore 29 books about python
Deep Learning Explore 19 books about deep learning