The increasingly sophisticated field of artificial intelligence (AI) has grown and spawned several disciplines that deserve their own focused consideration, namely machine learning (ML) and the ML subset “deep learning.” As it sounds, deep learning is the process of leveraging data analytics and the latest gains in computing power to enable computers to observe, learn, and respond to relatively complex situations faster than humans can.
Given this rapid evolution in AI and its offshoots, there are now several good deep learning books available for those aspiring to master the technology. Although there may be concerns about AI taking peoples’ jobs (Skynet, anyone?), the truth is that advances in AI—and by extension, deep learning—have generated a huge demand for talent. Whenever there is demand, job security and good wages tend to follow.
Simplilearn’s free, 9-hour tutorial on deep learning will whet your appetite, but you may want to dig a little deeper in 2023 by checking out the following deep learning books. When you’re serious about advancing your AI career, consider Simplilearn’s comprehensive, online Deep Learning (with Keras and Tensorflow) certification course.
Top 7 Deep Learning Books
Here is a list of the best Deep Learning Books that you must read in 2023.
1. Grokking Deep Reinforcement Learning, by Miguel Morales
Within deep learning, there is the sub-subset of deep reinforcement learning (DRL). This discipline within a discipline is based on user experience and trial and error to determine the most efficient and effective pathways for a given process or problem. Some of the most advanced DRL applications can predict movements in the stock market and even beat chess grandmasters.
Morales’ book uses a hands-on approach to learning DRL by providing exercises to help learners build their own deep learning systems using the Python programming language. It also includes ample use of examples and illustrations to drive home the fundamental principles and practical applications of DRL. Grokking Deep Reinforcement Learning also includes a companion ebook so you’ll always have access on your desktop or mobile device.
2. Deep Learning for Vision Systems, by Mohamed Elgendy
While it still seems impossibly futuristic, self-driving vehicles are just around the corner and will soon become the norm. Even now, many new cars have systems that detect objects in your path when you’re backing out of the driveway. The primary technology driving this development is computer vision, enabled through deep learning, which gives computers “eyes” that allow them to interact with the real world.
AI expert Mohamed Elgendy, VP of Engineering at Rakuten, has written a deep learning book that is both chock full of wisdom and infinitely approachable. Deep Learning for Vision Systems uses basic algebra to explain the concepts that enable computers to “see” and learn how the physical world works, with tutorials to help you build applications for facial recognition and image generation.
3. Deep Learning in Computer Vision: Principles and Applications, edited by Mahmoud Hassaballah and Ali Ismail Awad
Another deep learning book delving into the computer vision niche, Deep Learning in Computer Vision: Principles and Applications introduces the deep learning principles behind a specific topic in each chapter. Topics include object detection with convolutional neural networks, multi-biometric facial recognition systems, deep semantic segmentation in autonomous driving, and many more. Intended for experienced AI professionals and advanced learners, this book focuses on the areas of computer vision, pattern recognition, and image processing.
4. Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
This college-level, academic textbook covers the fundamentals of deep learning and is intended to help those who are completely new to the field. The authors, who are leaders in their field, stress the importance of attaining knowledge of math concepts like linear algebra and probability to better understand how deep learning works, going beyond just coding. This Deep Learning book also explains the deep learning concepts that are widely used in the industry, sequence modeling and speech recognition.
While a physical copy of the book may be purchased from most online booksellers, this comprehensive book also has a free, online version. From the link (in the title, above) you can also access lectures, exercises, and links to supplementary sites. This is a great place to start if you’re completely new to the field.
5. Artificial Intelligence by Example (2nd Edition), by Denis Rothman
This deep learning book explains the fundamentals of AI, including a sizable portion devoted to deep learning concepts, and will help you develop your own AI functions with hands-on exercises and tutorials. Real-life examples of deep learning applications detailed in the book include AI applications in blockchain and the Internet of Things (IoT). You’ll also learn how to develop chatbot functions using recurrent neural networks (RNNs) and convolutional neural networks (CNNs).
The completely revised and updated Artificial Intelligence by Example Second Edition includes examples for combining reinforcement learning and deep learning, as well as other new developments in AI and machine learning.
6. Neural Networks and Deep Learning, by Michael Nielsen
This free, online book covers 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, Neural Networks and Deep Learning takes a theoretical approach to the subject to illustrate how it may help solve common problems surrounding speech and image recognition, as well as natural language processing.
While the book presents the advanced mathematics behind these deep learning concepts, Nielsen also provides a guide to help you understand it and summaries of each chapter that omit the math. Given that it’s free and highly regarded, this is a great resource for newcomers to the field of deep learning.
7. Deep Learning: A Practitioner's Approach, by Adam Gibson and Josh Patterson
Adam Gibson and Josh Patterson are the co-creators of Deeplearning4j (DL4J), which has become the standard Java programming library for deep learning. Deep Learning: A Practitioner’s Approach covers the fundamentals of both machine learning and deep learning, taking a more practical approach to the technology than others. This book explains how deep networks evolved from neural networks, the basics of RNNs and CNNs, how to map specific deep networks to the right problem, how to use the DL4J programming library natively on Spark and Hadoop, and much more.
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