Best Deep Learning Books to Read

There is a lot to discover when it comes to deep learning. The relatively new field, which is part of the larger family of machine learning, explores how artificial neural networks can power everything from image and speech recognition to material inspection to social network filtering. So how do these neural networks actually work? They essentially allow computers to learn from past processes so they can make future decisions without human assistance.

Deep learning is a fascinating concept that is being implemented in various fields, so it is no surprise that there is a growing number of books and training around the topic. And while there is no best deep learning book because each one explores a different element or theory surrounding deep learning, there are quite a few that cover the basics and others that delve into the many practical ways deep learning methods can be applied in everyday processes. Read on for seven books that explore the fundamentals of deep learning and beyond.

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Deep Learning
Ian Goodfellow

In this college-level academic textbook on deep learning, Ian Goodfellow and fellow authors Aaron Courville and Yoshua Bengio cover the fundamentals of deep learning to help those who are completely new to the field. For Goodfellow, Courville, and Bengio deep learning involves more than just coding. It requires knowledge of math concepts like linear algebra and probability to better understand how deep learning works and implement its various techniques. The comprehensive free book, which also has an online version, covers both current research and a look at the potential impact of deep learning in the near future.

Neural Networks and Deep Learning
Michael Nielsen

In this neural network book, Nielsen teaches the programming paradigm of neural networks inspired by the human brain and helps connect the dots between these networks and deep learning. Considered one of the best books on neural networks, it takes a theoretical approach to deep learning to illustrate how it may help solve common issues surrounding speech and image recognition, as well as natural language processing. For Michael Nielsen, deep learning is a passion, and it shows in this easy-to-read book.

Deep Learning with Python
Francois Chollet

Chollet wrote this technical deep learning book as a way for programmers with Python experience to implement practical concepts within their work. Readers can also discover how deep learning is used with TensorFlow, Keras, and machine learning concepts in this deep learning with Python book. The book covers both the theory and the coding behind deep learning with Python, and Chollet creates strong mental imagery for each of the examples he highlights. He also happens to be an artificial intelligence (AI) researcher with Google, so he has extensive knowledge in the field.

TensorFlow 1.x Deep Learning Cookbook
Antonio Gulli, Amita Kapoor

This TensorFlow book is written in a cookbook style, offering experienced programmers a more hands-on approach for using coding with deep learning. Rather than teach the concept, though, this deep learning book covers the implementation and operation of the vast TensorFlow library within deep learning contexts using more than 90 coding recipes to solve AI-driven problems. It is a great book for developers who want to visualize how deep learning methods are used in practical settings.

Deep Learning: A Practitioner’s Approach
Adam Gibson, Josh Patterson

Written by two of the co-creators of Deeplearning4j, the standard Java programming library for deep learning, this book covers both machine learning and deep learning fundamentals. However, most of its pages include examples of Java-based code for deep learning so developers can better recognize how it is used in companies across the globe.

Grokking Deep Learning
Andrew W. Trask

Trask, who developed the OpenMined open-source AI community to collectively build a safer AI experience, offers a behind-the-scenes look at deep learning. In the book, he teaches developers how to actually build their own deep learning neural networks from the ground up while uncovering the actual science behind each concept. It is a great starter book for mastering deep learning frameworks.

Machine Learning Yearning
Andrew Ng

This free downloadable ebook, which offers technical strategies for AI engineers, is based on Ng's extensive practical experience leading Google Brain and Baidu deep learning teams. A must-read for professionals in the field, Ng offers a roadmap for developing projects and navigating experiments. It also looks at the many ways AI and machine learning are transforming industries around the world. 

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Explore Deep Learning Concepts

If you have a passion for deep learning or just want to explore how it is used in a practical setting, check out Simplilearn’s Deep Learning With TensorFlow Training Course. By enrolling in the online course, you can gain a greater understanding of deep learning concepts, as well as the TensorFlow open-source framework, to be able to implement deep learning algorithms and build artificial neural networks. Take your deep learning career to the next level by enrolling today.

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