7 Best Deep Learning Books to Read in 2024

Top 7 Deep Learning Books

Here is a list of the best Deep Learning Books that you must read in 2024.

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. 

Choose the Right Program

Unlock the potential of AI and ML with Simplilearn's comprehensive programs. Choose the right AI ML program to master cutting-edge technologies and propel your career forward.

Program Name	AI Engineer	Post Graduate Program In Artificial Intelligence and Machine Learning	Post Graduate Program In Artificial Intelligence and Machine Learning
Geo	All Geos	All Geos	IN/ROW
University	Simplilearn	Purdue	Caltech
Course Duration	11 Months	11 Months	11 Months
Coding Experience Required	Basic	Basic	No
Skills You Will Learn	10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more.	16+ skills including chatbots, NLP, Python, Keras and more.	8+ skills including Supervised & Unsupervised Learning Deep Learning Data Visualization, and more.
Additional Benefits	- Get access to exclusive Hackathons, Masterclasses and Ask-Me-Anything sessions by IBM - Applied learning via 3 Capstone and 12 Industry-relevant Projects	Purdue Alumni Association Membership Free IIMJobs Pro-Membership of 6 months Resume Building Assistance	Upto 14 CEU Credits Caltech CTME Circle Membership
Cost	$$	$$$$	$$$$

Take Your Deep Learning Expertise Even Deeper: Get Certified  

Self-motivated students and professionals are always reading and staying on top of the latest developments in their field. In addition to perusing the latest deep learning books in 2024, why not take your mastery of deep learning to the next level? Simplilearn’s comprehensive, industry aligned Caltech Post Graduate Program in AI and Machine Learning can get you there. If you want a more comprehensive learning experience, check out the AI and ML Course, offered in partnership with Purdue University. Don’t wait for the future to pass you by; be a part of it today!