20 Best Deep Learning Books (2022 Reviews) - Best Book Central (2023)

Computers and technology have evolved beyond imagination. Technological advances have exceeded the expectations of their inventors. Many of today's possibilities are only possible with the help of computers and their innovations.

Every day we are taking a step towards a fully automated future. Computers and robots are now able to independently make decisions based on a given situation. Machine learning opens up new ways to solve problems. Machine learning makes it easier for computer programs to learn new things on their own. This is a big step towards the future of artificial intelligence and automation.

Deep learning is another technological marvel made possible with the help of machine learning. It is a branch of machine learning. Deep learning is basically a representation of procedural learning mechanisms based on artificial neural networks. It has the ability to learn from unstructured or unlabeled data. The learning process can be supervised, semi-supervised or unsupervised.

What are the best deep learning books to read?

To learn deep learning, it is important to understand the basics of artificial intelligence and machine learning. Effectively utilizing deep learning to achieve your goals requires expertise and knowledge of programming languages ​​and artificial intelligence algorithms. Deep learning is now widely used in data science, data analysis, machine learning, artificial intelligence programming, and various other applications. If you are interested in delving deeper into deep learning, there are some books you can refer to to help you on your learning journey.

Best Deep Learning Books: Our pick of the 20 best books

Here are some of the best deep learning books you can consider expanding your knowledge on the topic:

1. Deep Learning (Adaptive Computing and Machine Learning)

Long gone are the days when computers needed commands to function. Technology has long since left behind the days of command-driven programming, and now computers can effectively adapt and make decisions based on their own experience with data and layered systems.

Deep learning is often referred to as unsupervised learning for computers. There are very few books on this extremely complex subject. However, deep learning's ability to be used in a wide range of applications makes it a viable option for most students, researchers, and software developers. Written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, this book is a masterpiece for anyone looking to start from scratch in deep learning. This book is the right read to get you fully acquainted with the deep learning experience from the ground up.

• author: Ian Goodfellow (Author), Joshua Bengio (Author), Aaron Cuvier (Author)
• author: MIT Press; Graphical Edition (November 18, 2016)
• page: 800 pages

2. Deep Learning with Python (First Edition)

Python is the most commonly used language for data science and artificial intelligence. Several innovations in machine languages ​​are due to Python. Python has transformed machine learning, data science, artificial intelligence, and even deep learning.

Keras is a powerful Python library that lets you write programs efficiently. It is most commonly used in artificial intelligence and machine learning. Written by Francois Chollet, the creator of Keras, who also worked on AI at Google, this book can be of great help to everyone. This book provides an easy-to-follow narrative that digs deep into deep learning, artificial intelligence, and how to use Python to help you accomplish complex tasks with ease.

This book covers deep learning principles from basic to advanced natural text generation and image generation.

• author: François Jollet (Author)
• author: Manning Press; First Edition (December 22, 2017)
• page: 384 pages

3. Deep learning foundation: next generation machine intelligence algorithm design

Since its first introduction in 2000, deep learning has encompassed many possibilities. Possibilities for implementation using machine learning, artificial intelligence, and deep neural networks are currently being investigated. The industry has come a long way and the improvements are evident.

Advances in deep learning owe a lot to humans and their own ability to adapt. Algorithmic improvements and deep learning's ability to debug make them a brilliant innovation. The authors of this book are Nikhil Buduma and Nicholas Locascio. It covers all deep learning improvements made by developers and the algorithms themselves. If you want to actively participate in deep learning and want to understand the basics, functions, applications and capabilities of deep learning. This is a must-read for you.

• author: Nikhil Buduma (Author), Nicholas Locascio
• author: O'Reilly Media; Edition 1 (July 4, 2017)
• page: 298 pages

4. Deep Learning Illustrated: A Visual and Interactive Guide to Artificial Intelligence (Addison - Wesley Data and Analytics Series)

Artificial intelligence and deep learning sound interesting. The work behind the code can be very dull and tedious. Millions of lines of code must be written and understood to make a single AI task possible.

With this in mind, John Krohn, Grant Beyleveld, and Aglae Bassenss wrote and edited this highly interactive book to make deep learning better and more fun. This book is equipped with a large number of illustrations to help readers better understand and remember. There are also exercises and exercises to test your knowledge of artificial intelligence and deep learning. This book is the right reading for anyone looking to leverage deep learning for natural language processing, image generation, and gaming algorithms.

• authorBy: Jon Crohn (Author), Grant Belifford (Author), Agra Parsons (Author)
• author: Addison-Wesley Professional; First Edition (September 18, 2019)
• page: 416 pages

5. Neural Networks and Deep Learning: A Handbook

Deep learning works through the artificial neural networks of artificial intelligence and machine learning. It can adapt to changes and new information. This book deals with both classical and modern information models.

To better understand it, this book covers the first fundamental concepts of deep learning and connects them to its most modern applications. This book is the right choice for those who want to understand not only how to use deep learning effectively, but also where it comes from and the basic concepts. Written by Charu C. Aggarwal, this book consists of several topics, each covering the concepts of deep learning and neural networks.

• author: Charu C. Aggarwal (Author)
• author: Jumper? Version 1, 2018 (September 13, 2018)
• page: 520 pages

6. Deep Learning for Creativity: Teaching Machines to Draw, Compose, Write, and Play

Deep learning offers more interesting applications than data analysis. When used correctly, deep learning can create immersive and fun opportunities. If you're looking for things like creating images, writing themes, or developing games, then deep learning could be your friend.

Written by David Foster, this book covers some underrated applications of deep learning. Deep learning does have some extremely important applications, with a huge impact on science and research. This book will help you discover the interesting side of deep learning. By reading this book, you'll learn how to change facial expressions in photos and how to use deep learning to compose music. The book also includes some great examples of game graphics creation and character customization techniques.

• author: David Foster (Author)
• author: O'Reilly Media; First Edition (July 16, 2019)
• page: 330 pages

7. Deep Learning: A Practitioner's Approach

As the name suggests, if you are a beginner and want to learn deep learning. This book is not for you. This book is from the perspective of an AI expert and practitioner who has already worked in machine learning.

This book provides an in-depth look at deep neural networks, how they work, and how they can effectively support your organizational structure. Written by Adam Gibson and Josh Patterson, this book is a comprehensive edition of deep learning for professionals who use artificial intelligence for machine learning and want to get into deep learning. This book contains some best practices from experts on learning deep learning algorithms, using them effectively, and using them in various applications.

• authorBy: Josh Patterson (Author), Adam Gibson (Author)
• author: O'Reilly Media; First Edition (August 22, 2017)
• page: 532 pages

8. Deep Learning and R

Keras is one of the most powerful libraries. Built with AI and Python in mind. R is one of the most commonly used Keras languages ​​for deep learning and neural networks.

This book is written by the creator of Keras. Francois Chollet and J.J. Allaire is considered a leader in machine learning, artificial intelligence and deep learning. This book explains in detail how to use Keras and its R language. This book is a collaboration between Keras creator Francois Chollet and R Studio founder J.J. Allaire. It contains a wealth of information and guidance for anyone who wants to start deep learning with Python, Keras, and R.

• author: François Jollet (Author), J.J. Allaire (Author)
• author: Manning Press; First Edition (February 9, 2018)
• page: 360 pages

9. Deep Learning (MIT Press Essentials Series)

MIT is a world-renowned school. He has earned a well-deserved reputation for his research work, innovations and solutions to technical problems. The R&D department is second to none in areas such as adaptability to new technologies.

Written by John D. Keller, part of the MIT Press Essentials series, this book is an excellent guide for those looking to enhance their deep learning experience. This book presents an accessible version of deep learning in an accessible narrative. This book is the right guide to learn deep learning for computer vision, speech recognition, artificial intelligence, and more.

• author: John D. Kelleher (Author)
• author: MIT Press; Illustrated Edition (September 10, 2019)
• page: 296 pages

10. Deep Learning Deep Learning

There are indeed books that allow you to apply deep learning to various applications. These are shortcut methods and do not cover the basics used behind the scenes. These methods get the job done. However, they're not good in the long run, and you don't know what processes are running behind the code.

If you want to build deep learning from scratch, Grokking Deep Learning is the right choice for you. Written by a fuller and more insightful narrator than Andrew Trask. Grokking Deep Learning is the go-to book for understanding the science behind deep learning neural networks inspired by the human brain. This book lets you use Python and its libraries to effectively teach your programs to read and create images, music, and more.

• author: Andrew Trask (Author)
• author: Manning Press; First Edition (January 25, 2019)
• page: 336 pages

11. Cloud, Mobile, and Edge Deep Learning in Practice: Real-World AI and Computer Vision Projects Using Python, Keras, and TensorFlow

There are some books out there on the basics of deep learning. There are also books on understanding the deep learning process and how it works. There are also books that only talk about the possibilities and innovations that can arise from this.

However, this book is completely practical. Some research projects have no short-term effects on normal people. This book provides readers with a human-readable version of deep learning that can be used by everyday technical users. This book is equally suitable for data scientists, software developers using artificial intelligence, or enthusiasts who want to use artificial intelligence to achieve something. With an easy-to-follow narrative, this book takes you through everything you need to do to use deep learning for cloud computing, use artificial intelligence to develop mobile apps, and more.

• author: Anirudh Koul (Author), Siddha Ganju (Author), Meher Kasam (Author)
• author: O'Reilly Media; Edition 1 (November 5, 2019)
• page: 620 pages

12. Deep Learning with Python: A Comprehensive Guide with Tips and Tricks for Deep Learning Using Python Theory

Python is the most commonly used language for artificial intelligence, data analysis, data science, and machine learning. The power of Python is perfectly matched with the capabilities of artificial intelligence. In this book, you can start from the basics of Python to understand the workflow and code background of deep learning.

Written by Ethan Williams, this book provides in-depth information on how to use Python for deep learning. The book is presented in a comprehensive, accessible, and actionable narrative. This is a must for anyone who is proficient in Python and wants to take their first steps in deep learning. This book contains some unique and interesting tips and tricks to make Python effectively used for deep learning theory and algorithms.

• author: Ethan Williams (Author)
• author: Released independently (January 3, 2020)
• page: 216 pages

13.Deep Learning from Scratch: Building from First Principles in Python

Deep learning is a highly complex task that requires advanced knowledge of Python, the programming language, and an understanding of artificial intelligence and machine learning. But if you are a beginner and start deep learning without learning extra stuff. This book is for you.

Deep Learning from Scratch by Seth Weidman is an appropriate book, covering only the basics of Python and programming fundamentals, effectively getting you to the level of an effective deep learning developer. This book provides beginners with a clear and accessible narrative, enabling them to learn the OOP framework and use it to write deep learning algorithms using Python. This book also includes the implementation example of practical application, making the understanding process smoother and easier.

• author: Seth Weidman (Author)
• author: O'Reilly Media; First Edition (September 24, 2019)
• page: 252 pages

14. Advanced Deep Learning with Keras: Applied Deep Learning Techniques, Autoencoders, GANs, Variational Autoencoders, Deep Reinforcement Learning, Policy Gradients, and more.

Keras is built with a focus on artificial intelligence, machine learning, and deep learning. It opens up hundreds of possibilities for machine learning capabilities. This book is the right guide to understand the power of Keras and how to leverage it to apply deep learning to hundreds of functions using Python.

This comprehensive and in-depth application guide to deep learning, written by Rowel Atienza, should be read by anyone who wants a comprehensive understanding of deep learning. This book allows you to understand the behind-the-scenes process and apply it to many applications, including autoencoders, GANs, policy gradients, and more.

• author: Lowell Atienza (Author)
• author: Packt Publishing (31 October 2018)
• page: 368 pages

15. Introduction to Deep Learning (MIT Press)

Introduction to Deep Learning is a concise and project-oriented guide to deep learning. It removes trivial parts and concepts that are difficult to use in real applications. This book focuses on practical examples needed to create algorithms capable of autonomous, unsupervised learning and decision-making.

The author of this book is Eugene Chaniak. It provides a unique, easy-to-understand, live demonstration of algorithms that can be activated for unsupervised learning. The book is divided into project-oriented chapters. Each chapter comes with a coding example and exercises so you can try out what you've learned in that chapter. The exercises in the book support your learning journey and help you correct any mistakes you may make.

• author: Eugene Charniak (Author)
• author: MIT Press; Graphical Edition (January 29, 2019)
• page: 192 pages

16. Deep Learning and Go

We have watched a lot of Go games recently. These games utilize AI and AR to bring players an immersive experience. This book is about games. If you are a game developer and want to create a bot that can win games. You should read this book.

Written by Max Pumperla and Kevin Ferguson, this book shows you how to build a robot, teach it the rules of the game, and give it the ability to learn. Using neural networks, such bots can gain expert knowledge of the game, sometimes even beating real players. Deep learning can enable robots to improve themselves. With the help of this book, you can create such a robot.

• author: Max Pempera (Author), Kevin Ferguson (Author)
• author: Manning Press; First Edition (January 25, 2019)
• page: 384 pages

17. Digging Deeper into Deep Learning: Engagement Tools

"Deep Deep Learning" is the result of a collaboration of some of the most famous data scientists. It was written by Joanne Quin, Joanne J. McEachen, Michael Fullan, Mag Gardner and Max Max Drummy. This book is so well written that it is worth reading for anyone who wants to delve into neural networks and understand the basics of how they work.

This book is full of tips, tricks, and tools for engaging users and creating artificial intelligences that improve and teach themselves. The narrative provided in this very unique and informative book is easily understood by all teachers, students, and anyone who wants to gain deep learning through practice and use it effectively in multifaceted projects.

• author: Joanne Quinn (Author), Joanne J. McEachen (Author), Michael Fullan (Author), Margot Gardner (Mag Gardner) (Author), Max Drummy (Author)
• author: Cowen? First Edition (August 20, 2019)
• page: 296 pages

18. Deep Learning: Engage the World, Change the World

Deep learning has enabled hundreds of innovations that have been very successful in engaging users. With these attractive technologies, new technologies and updates to existing systems are being introduced on a daily basis. These technological advances benefit from deep learning and artificial intelligence.

As the name suggests, Deep Learning: Engage the World, Change the World focuses on those deep learning techniques that can be applied to user engagement applications. The authors of this book are Michael Fullan, Joanne Quinn and Joanne McElchen. Here's a unique interactive approach to deep learning and how to make your algorithms engaging for users. It is believed that deep learning will create near-human intelligence and change the world and the way we see it in a short period of time.

• authorBy: Michael Fullan (Author), Joanne Quinn (Author), Joanne J. McElchen (Author)
• author: Cowen? First Edition (December 15, 2017)
• page: 208 pages

19. Deep Learning Cookbook: Practical Recipes to Get You Started Quickly

This book is perfect for those who don't have much time but want to get into the game quickly. As the title suggests, this book contains quick recipes to help you understand deep learning and start building algorithms right away.

Written by Douwe Osinga, the book contains only one recipe per chapter. These chapters are project-based and focus on the project from inception to completion. This book is coded in Python so that it can be easily understood by those already using Python, machine learning, and artificial intelligence.

• author: Dewey Ozinga (Author)
• author: O'Reilly Media; First Edition (June 26, 2018)
• page: 252 pages

20. Deep Learning for NLP and Speech Recognition

Deep learning and artificial neural networks have opened the door to numerous possibilities in the world of artificial intelligence. NLP and speech recognition are two technological marvels that allow computers to understand not only natural language, but also the feelings and emotions associated with it.

Written by Uday Kamath, John Liu, and James Whitaker, this book is the right guide to effectively develop deep learning algorithms and enable them to learn natural language and NLP speech recognition. The system grows and learns independently over time. However, the book provides instructions for everything from developing such an algorithm to monitoring the learning process.

• author: Uday Kamas (Author), John Liu (Author), James Whittaker (Author)
• author: Jumper? First Edition 2019 (June 24, 2019)
• page: 649 pages

Choosing the Best Deep Learning Books

The scope of deep learning is immeasurable. For all those who like to stay informed and keep an eye on the future. Deep learning is a gold mine. As the world moves rapidly toward automation and artificial intelligence, the importance and applications of artificial intelligence, machine learning, and deep learning are beyond doubt.

Deep learning, the most advanced branch of artificial intelligence, may seem complicated to those who want to look at it from a distance and start learning. We have conducted a rigorous review of these books and created this guide to help you decide which book best suits your study needs and get the most out of your book learning process.