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Computers and technology have evolved beyond anyone’s imagination. Technological advancements have exceeded the expectations of their own inventors. Today, many possibilities are only achieved with the help of Computers and their innovations.
Each day, we are taking one step further towards a fully automated future. Computers and robots today are enabled of making decisions on their own, given the circumstances. Machine learning is adopting new ways to solve problems. Machine learning makes it easier for a computer program to learn new things on its own. This is a great step towards the future of AI and automation.
Deep learning is another technological wonder, made possible with the help of machine learning. It is a branch of machine learning. Deep learning is basically a representation of a learning mechanism for a program based on an artificial neural network. It has the capability to learn from unstructured or unlabelled data. The learning process can be supervised, semi-supervised or unsupervised at all.
What are the Best Deep Learning Books to read?
To learn Deep Learning, it is important that you understand the fundamentals of AI and machine learning. It requires expertise and command over programming languages as well as algorithms of AI to use Deep Learning efficiently for your goals. Deep Learning is widely used today for Data Science, Data analysis, machine learning, AI programming and a wide range of other applications. If you are looking to get your hands on Deep Learning, you can get an idea of some books that will help you through the learning journey.
Best Books on Deep Learning: Our Top 20 Picks
Here are some of the best deep learning books that you can consider to expand your knowledge on the subject:
1. Deep Learning (Adaptive computation and machine learning)
Long gone are the days when computers needed commands to work. Technology has moved way past the era of command-specific programs and now computers can adapt and make decisions efficiently through their own experience with data and hierarchy systems.
Deep learning is the term used for unsupervised learning by computers commonly. There are seldom books written on this highly complex topic. Yet, the possibilities of Deep Learning in a wide range of applications make it the learn-worthy choice for most students, researchers, and software engineers. Written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, this book is a masterpiece for all those who want to start from scratch in the world of deep learning. The book is right to read to get you from beginning to the expertise of Deep learning comprehensively.
- Authors: Ian Goodfellow (Author), Yoshua Bengio (Author), Aaron Courville (Author)
- Publisher: The MIT Press; Illustrated Edition (November 18, 2016)
- Pages: 800 pages
2. Deep Learning with Python (1st Edition)
Python is the most commonly used language for Data Science and Artificial Intelligence. Several innovations for Machine language are to thank to Python. With the help of Python machine learning, data science, artificial intelligence, and even deep learning have changed a lot.
Keras is a powerful Python library that enables you to write programs efficiently. It is most commonly used for Artificial Intelligence and Machine Learning. Written by Keras creator Francois Chollet, who has also worked with Google for AI research, this book is a great help for all. The book has easy to understand narrative and deep insight into Deep learning, artificial intelligence, and how you can get assistance with python to get complex tasks done easily.
The book covers Deep Learning principles from basics to natural text generation and image generation at advanced levels.
- Authors: François Chollet (Author)
- Publisher: Manning Publications; 1st Edition (December 22, 2017)
- Pages: 384 pages
3. Fundamentals of Deep Learning: Designing next-generation machine intelligence algorithms
Since its first introduction in 2000, deep learning has covered a lot of ways. There is constant ongoing research for the possibilities that can come true with the help of Machine Learning, Artificial Intelligence, and deep neural networks. A lot of progress has been done in the sector and you can clearly see the improvements.
The improvements in Deep Learnings are to thank both humans and their own adaptive abilities. The algorithms have upgraded themselves and troubleshooting abilities of deep learning make them a wondrous innovation. The book is written by Nikhil Buduma and Nicholas Locascio. It covers all the improvements in Deep learning by programmers and the algorithms themselves. IF you want to actively get involved in deep learning and want to know the basics, working, applications and possibilities of Deep learning. This is a must-read book for you.
- Authors: Nikhil Buduma (Author), Nicholas Locascio
- Publisher: O’Reilly Media; 1st Edition (July 4, 2017)
- Pages: 298 pages
4. Deep Learning Illustrated: A Visual, Interactive guide to Artificial Intelligence (Addison – Wesley Data & analytical series)
As interesting as Artificial Intelligence and Deep Learning may sound. The working that is behind the code can be really dry and boring. Millions of lines of codes need to be written and understood to make a single task enabled by Artificial Intelligence.
Keeping this point in mind John Krohn, Grant Beyleveld, and Aglae Bassenss have written and compiled this highly interactive book to learn Deep Learning in a better and more fun way. The book consists of numerous illustrations that will help readers to understand better and remember for longer. There are exercises and practices as well to test your knowledge of Artificial Intelligence and deep learning. This is the right read for all those who are looking to use Deep Learning for natural language processing, image generation, and gaming algorithms.
- Authors: Jon Krohn (Author), Grant Beyleveld (Author), Aglaé Bassens (Author)
- Publisher: Addison-Wesley Professional; 1st Edition (September 18, 2019)
- Pages: 416 pages
5. Neural Networks and Deep Learning: A textbook
Deep Learning works through artificial neural networks of Artificial Intelligence and machine learning. It can adapt to changes and new information. This book is about both classic and modern models of the information.
To understand it better, the book covers initial fundamental concepts of deep learning and connects it to most modern applications of them. The book is the right choice for those who not only want to learn how to use Deep Learning effectively but also, where it comes from and what are the basic concepts of it. Written by Charu C. Aggarwal the book consists of several topics, each covering a concept of Deep Learning and Neural Networks.
- Authors: Charu C. Aggarwal (Author)
- Publisher: Springer; 1st ed. 2018 Edition (September 13, 2018)
- Pages: 520 pages
6. Generative Deep Learning: Teaching Machines to Paint, Compose, Write and Play
Deep Learning has far more interesting applications than working with Data Analysis. If applied in the right way, there are immersive and fun possibilities that can come true with the help of Deep Learning. If you are looking for something like image generation, write about a topic or game development, Deep learning can be your friend.
The book is written by David Foster, and it covers some underrated applications of deep learning. While it is true that deep learning has some greatly important applications that have a huge impact on science and research. This book will help you get the fun side of Deep learning. With reading this book, you can learn how to change facial expressions in photos, and use Deep Learning for music composition. The book also has some great examples for Image generation and character adaptive techniques for gaming.
- Authors: David Foster (Author)
- Publisher: O’Reilly Media; 1st Edition (July 16, 2019)
- Pages: 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. The book has a perspective of AI expert and practitioner who is already working with Machine Learning.
The book covers some in-depth insight into Deep Neural networks, their working process and how they can efficiently help your organizational structure. Written by Adam Gibson and Josh Patterson, the book presents a full-scale version of deep learning for the experts who are working on Machine Learning with the help of AI and want to grow towards Deep Learning. This book consists of some great practices followed by the experts to learn and work efficiently with Deep Learning algorithms and use it for a variety of applications.
- Authors: Josh Patterson (Author), Adam Gibson (Author)
- Publisher: O’Reilly Media; 1st Edition (August 22, 2017)
- Pages: 532 pages
8. Deep Learning with R
Keras is one of the most powerful libraries. It is created by keeping Artificial Intelligence and Python in mind. R is one of the languages of Keras that is most commonly used with Deep Learning and neural networking.
The book is written by the creator of Keras. Francois Chollet and J. J. Allaire are considered the top bras when it comes to the world of machine learning, artificial intelligence, and Deep Learning. In this book, the use of Keras and its R language is explained thoroughly. The book is a collaboration of Keras Creator Francois Chollet and R Studio Founder J. J. Allaire. It contains ample information and guidance for anyone who wants to get into deep learning with Python, Keras and R language.
- Authors: Francois Chollet (Author), J. J. Allaire (Author)
- Publisher: Manning Publications; 1st Edition (February 9, 2018)
- Pages: 360 pages
9. Deep Learning (The MIT Press Essential Knowledge series)
Massachusetts Institute of Technology is a world-known school. It has gained its due popularity for the research work, innovations and solutions to technological problems like no other. The research and development department is unmatched in terms of adaptivity to new technologies and much more.
Written by John D. Keller, as a part of the MIT press essential knowledge series, this book is a great guide for those who want to polish their expertise in Deep Learning. This book presents an accessible and comprehensible version of deep learning in an easy to understand narrative. The book is the right guide to learn Deep Learning for computer vision, speech recognition, artificial intelligence and more.
- Authors: John D. Kelleher (Author)
- Publisher: The MIT Press; Illustrated Edition (September 10, 2019)
- Pages: 296 pages
10. Grokking Deep Learning
While there are books that can enable you to apply Deep learning on several applications. They follow the shortcut methods that do not cover the basic principles being used under the hood. Those methods may get the job done. Yet, are not good in the long run and you have no idea what processes are going on behind your code.
Grokking Deep Learning is the right choice for you if you want to build deep learning from the very scratch. Written by a highly comprehensive and understanding narrative of Andrew Trask. Grokking Deep learning is the right book to understand the science behind neural deep learning networks inspired by human brains. The book enables you to use python and its libraries to effectively make your program learn reading and creating the images, music, and much more.
- Authors: Andrew Trask (Author)
- Publisher: Manning Publications; 1st Edition (January 25, 2019)
- Pages: 336 pages
11. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer Vision projects using Python, Keras & TensorFlow
There are books about the fundamentals of deep learning. Then there are books about understanding the processes of deep learning and how it works. Also, there are books that only talk about the possibilities and innovations made possible by it.
This book, however, is completely practical. There are projects in research that have no short-term impact on a regular person. This book presents its reader with an understandable by all versions of deep learning that can be used for everyday tech users. The book is equally great for Data scientists, Software engineers working with AI, or hobbyists who want to get something done with the help of AI. This book has a highly understandable narrative and will enable you to do all that is required to use Deep Learning for cloud computing, mobile application development with AI and much more.
- Authors: Anirudh Koul (Author), Siddha Ganju (Author), Meher Kasam (Author)
- Publisher: O’Reilly Media; 1st Edition (November 5, 2019)
- Pages: 620 pages
12. Deep Learning with Python: Comprehensive Guide of Tips and Tricks using Deep Learning with Python Theories
Python is the most commonly used language for AI, Data Analysis, Data Science, and Machine Learning. The power of Python is the right match for possibilities covered by Artificial Intelligence. This book lets you start from the basics of Python to understand the working process of Deep Learning and what goes behind the code.
Written by Ethan Williams, this book contains elaborative information on how Python can be used for Deep Learning. This book follows a comprehensive, easy to understand and apply narrative. It is a must-read book for all those who have good command over python and want to take their first step towards deep learning. There are some unique and interesting tips and tricks in the book enabling python efficiently for Deep Learning theories and algorithms.
- Authors: Ethan Williams (Author)
- Publisher: Independently published (January 3, 2020)
- Pages: 216 pages
13. Deep Learning from Scratch: Building with Python from First principles
Deep Learning is a highly complex task that requires top expertise with Python, programming language, understanding of AI and machine learning. However, if you are a beginner and start with Deep Learning without having to learn extra stuff. This is the right book for you.
Deep Learning from Scratch by Seth Weidman, is the right book that covers only necessities from Python’s first principles and programming fundamentals to effectively grow you to the level of an efficient deep learning programmer. The book has a clear and easy to understand narrative for beginners that allows them to learn OOP framework and use it with the help of Python to write Deep Learning algorithms. The book has implementation examples as well for real-life applications that make the understanding process smoother and easier.
- Authors: Seth Weidman (Author)
- Publisher: O’Reilly Media; 1st Edition (September 24, 2019)
- Pages: 252 pages
14. Advanced Deep Learning with Keras: Apply Deep Learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more.
Keras was built focused on Artificial Intelligence, Machine Learning, and Deep Learning. It has opened hundreds of ways for the possibilities of Machine Learning. This book is the right guide to understand the power of Keras and how you can use it with the help of Python to apply Deep Learning to hundreds of possibilities.
Written by Rowel Atienza, this comprehensive and elaborative guide on the applications of deep learning should be read by every person who wants to understand the complete scope of Deep Learning. The book enables you to understand the processes under the hood and be able to apply themselves for numerous applications including autoencoders, GANs, policy gradients and much more.
- Authors: Rowel Atienza (Author)
- Publisher: Packt Publishing (October 31, 2018)
- Pages: 368 pages
15. Introduction to Deep Learning (The MIT Press)
Introduction to Deep Learning is a concise and project drive guide to Deep Learning. It cuts the unimportant parts and concepts that are scarcely used in the real-world application. The book focuses on practical examples required to build algorithms that are capable of learning and taking decisions on their own, unsupervised.
The book is written by Eugene Charniak. It features a unique, easy to understand and to the point narrative towards algorithms that can be enabled to learn unsupervised. The book is divided into chapters based on projects. Each chapter has its example, and programming exercise so you can test the knowledge you have managed to gain through the specific chapter. Exercises in the book enable you to feel confident about your learning journey and to rectify any mistakes you are making as well.
- Authors: Eugene Charniak (Author)
- Publisher: The MIT Press; Illustrated Edition (January 29, 2019)
- Pages: 192 pages
16. Deep Learning and the game of Go
We have been seeing a lot f Go games recently. These games featured AI and AR to create an immersive experience for the players. The book is all about gaming. If you are a game developer and want to create a bot that can win games. You should be reading this book.
Written by Max Pumperla, and Kevin Ferguson the book teaches you how to build a bot, teach it the rules of the game and enable it of learning. Through neural networks, such bots can gain expertise in the game and sometimes even beat real players. Deep Learning can make possible a bot that is capable of self-improvement. And with the help of this book, you can create a bot like that.
- Authors: Max Pumperla (Author), Kevin Ferguson (Author)
- Publisher: Manning Publications; 1st Edition (January 25, 2019)
- Pages: 384 pages
17. Dive into Deep Learning: Tools for Engagement
Dive into deep learning is collaboration of some most renowned data scientists. It is written by Joanne Quin, Joanne J. McEachen, Michael Fullan, Mag Gardner, and Max Drummy. With such brilliance behind the words, the book is a worthy read for all those who want to let themselves dive deep into deep neural networks and understand the fundamentals of its working process.
The book is loaded with tips and tricks, and tools for engaging the users and creating an AI that is capable of self-improvement and learn things on its own. The narrative offered by this highly unique and informative book is easy to understand by all teachers, students, and all those who want to get their hands-on deep learning and be able to use it efficiently for versatile projects.
- Authors: Joanne Quinn (Author), Joanne J. McEachen (Author), Michael Fullan (Author), Mag Gardner (Author), Max Drummy (Author)
- Publisher: Corwin; First Edition (August 20, 2019)
- Pages: 296 pages
18. Deep Learning: Engage the World, Change the World
Deep Learning has made possible hundreds of innovations that are highly successful in engaging users. With these engaging techniques, new technologies and updates to existing systems are being introduced each day. These upgradations to the technology are to thanks Deep Learning and Artificial Intelligence.
As the name suggests, Deep Learning: Engage the World, Change the World focuses on these deep learning techniques that can be applied towards user engagement applications. The book is written by Michael Fullan, Joanne Quinn, and Joanne McEachen. It follows a unique and interactive approach towards Deep Learning and how you can enable your algorithm to engage users. Deep Learning is believed to create near-human intelligence and is anticipated to change the world and how we look at it in a short span.
- Authors: Michael Fullan (Author), Joanne Quinn (Author), Joanne J. McEachen (Author)
- Publisher: Corwin; First Edition (December 15, 2017)
- Pages: 208 pages
19. Deep Learning Cookbook: Practical Recipes to get started quickly
This book is right for those who do not have a lot of time at their hands and they want to get in the game real quick. As the name suggests, the book has some quick recipes to understand deep learning and start creating algorithms in no time at all.
Written by Douwe Osinga, this book contains chapters with a single recipe in each chapter. The chapters are project-based, focused on one project from scratch to finish. The book follows Python coding to make it easy to understand for those who are already working with Python, Machine Learning and AI.
- Authors: Douwe Osinga (Author)
- Publisher: O’Reilly Media; 1st Edition (June 26, 2018)
- Pages: 252 pages
20. Deep Learning for NLP and speech recognition
Deep Learning and Artificial Neural Networking have opened the doors for so many possibilities in the world of Artificial Intelligence. NLP and speech recognition are two marvels of technology that enable a computer to understand not only the natural language but the feelings and emotions connected behind that.
Written by Uday Kamath, John Liu, and James Whitaker, this book is the right guide for you to effectively develop Deep Learning algorithms and make them capable of learning speech recognition through natural languages and NLP. The system grows over time and learns on its own. However, from developing such an algorithm to overseeing the learning process, all the guidance is provided comprehensively in this book.
- Authors: Uday Kamath (Author), John Liu (Author), James Whitaker (Author)
- Publisher: Springer; 1st ed. 2019 Edition (June 24, 2019)
- Pages: 649 pages
Choosing the Best Deep Learning Books
Deep Learning has a scope beyond measure. For those who like to stay up-to-date and keep an eye on the future. Deep Learning is a gold mine. With the world moving rapidly towards automation and Artificial Intelligence, there are no second thoughts on the importance and applications of Artificial Intelligence, Machine Learning, and Deep Learning.
Deep Learning is the most advanced branch of Artificial Intelligence that may seem complex to those who are looking at it afar and want to start learning it. We have critically reviewed these books and compiled this guide for you so you can decide which book would suit your learning needs best and you can have the best advantages of the learning process through the books.