Let Learn Dunia take you through some of the best deep learning books available on the market. You are free to pick the best option that matches your needs.
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It’s no secret that artificial intelligence is growing at an exponential rate. Guess what? This growth rate can translate into a lucrative career. If you want to jump on the artificial intelligence bandwagon for an exciting career, it’s imperative to know about Deep Learning.
What is Deep Learning?
Deep learning is a subset of machine learning. It is essentially a multilayer neural network that tries to simulate the human brain by providing large amounts of data and learning from it. Deep learning drives many artificial intelligence services that help in automation and performing tasks without human interference.
How to Learn Deep Learning?
There are online as well as offline options available for you to learn deep learning.
If you are looking to learn deep learning, a good deep learning book could help. You can use it in conjunction with your video courses to fast forward your learning process. Here, we have compiled a list of books to choose from.
Best Deep Learning Books
1. Deep Learning with Python
Description: If you have some background knowledge of Python, this book can be instrumental in your learning journey. It explains the basics of deep learning, which makes it ideal for beginners.
According to the author, you need a Linux system to do the examples. But only the GPU-based examples would require that while the rest of the examples can be quickly done on Windows 10/Anaconda 3.
Deep Learning with Python is the best deep learning book to get you started on Keras, Deep Learning, and Neural Networks. It nicely prepares you to learn the more advanced topics on the subject.
The book doesn’t give all the formulas and algorithms that can be learned from other books on the subject. It also doesn’t bother the reader with the mathematics behind everything, which makes it great for someone not interested in mathematics.
- Originally Published- 2017
- Author- François Chollet
You can buy this book here.
2. Deep Learning (Adaptive Computation and Machine Learning series)
Description: This is an excellent theoretical book that covers a wide range of topics. Herein, the author explains the underlying concepts clearly through illustrative examples. The math part of the book is also not something that would bother the readers much.
Some of the top deep learning techniques practitioners employ like regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology, are explained in detail. The chapters after Chapter 9 are experimental topics that provide good exposure to a wide variety of ideas.
The book is an excellent resource for anyone planning a career in the industry or looking to research deep learning.
- Originally Published- 2016
- Authors- Ian Goodfellow, Yoshua Bengio, Aaron Courville
You can buy this book here.
3. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series) (1st Edition)
Description: Deep Learning Illustrated could be your go-to book if you want to go through deep learning without the rigorous mathematical part. It is an excellent introduction to what deep learning is all about.
The authors have done a commendable job of explaining both the context and modern application of deep learning. The book is divided into two parts.
- The first part of the book contains the history of deep learning. This is great for someone looking to understand the hows and whys behind the beginning of deep learning.
- The second part of the book contains the code. The authors have tried to make the second part as easy as possible without overdoing the math.
- Originally Published- 2019
- Authors- Jon Krohn, Grant Beyleveld, Aglaé Bassens
You can buy this book here.
4. Advanced Deep Learning with TensorFlow 2 and Keras (2nd edition)
Description- This is the second edition of the book, which came out in February 2020. Think of it as the best deep learning book for advanced practitioners of artificial intelligence.
The book starts with an introduction to Keras, which is essential for even a beginner. Clear and concise definitions of standard terms are also provided in the first chapter. It sets you up nicely for the more advanced concepts.
The author provides examples that help revise concepts like common neural network architectures, such as the multilayer perceptron, recurrent neural network, and convolutional neural network. It certainly helps in case you haven’t been in touch with the concepts recently.
This is a well-organized book that can be used for learning and reference while working on projects.
- Originally Published- 2020
- Author- Rowel Atienza
You can buy this book here.
5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition
Description: Looking for something with a hands-on approach to learning by doing? Don’t go any further than this deep learning book. This is a code-focused book, so you can work on real problems. As the name suggests, this is the second part, which also includes the latest concepts like TensorFlow.
The book begins with traditional machine learning, which gives a great deal of context before moving on to many newer and more advanced concepts. Even the advanced concepts are explained clearly and precisely. In doing so, the book doesn’t skip over explaining any challenging parts and breaks all of them into small, consumable chunks.
It’s a great deep learning textbook.
- Originally Published- 2019
- Author- Aurélien Géron
You can buy this book here.
6. Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
Description: As the name says, the book aims to teach deep learning without being too heavy on mathematics. The authors have consciously omitted some topics to elaborate only on the ones necessary for beginners.
The authors have categorically mentioned that this is an excellent introduction to deep learning for anyone with over a year of coding experience in Python. But even if you are a complete beginner, you can use this book along with your video course.
The diagrams in the book are of excellent quality. The book makes heavy use of the Fastai library. Therefore, some coders may find it complicated.
- Originally Published- 2020
- Authors- Jeremy Howard, Sylvain Gugger
You can buy this book here.
7. Deep Learning from Scratch: Building with Python from First Principles (1st Edition)
Description: As the name indicates, this book will teach you Deep Learning in the Python language. It is a well-organized book that details all the relevant info. This book is different from the others on the list as it teaches the theory and guides you on how to build different kinds of neural networks.
The book explains in-depth the concepts you need to essentially know like coding layers, backpropagation, optimization, the trainer for the neural net, and more. It teaches how to code from scratch rather than just using a toolkit like Theano, TensorFlow, or PyTorch, which restricts the understanding of bureau networks’ work.
If you’re not a massive fan of black and white images, the book’s printed version might put you off. The colourless pictures don’t do justice to the brilliant content of the book. However, we still rate it among the best deep learning books out there.
- Originally Published- 2019
- Author- Seith Weidman
You can buy this book here.
8. Neural Networks and Deep Learning: A textbook by Charu C. Aggarwal
Description: This is an excellent theoretical book covering both the classical and modern models. If you are a student looking to learn Deep Learning from the exam perspective, this is probably the best deep learning book for you.
Make no mistake, it is not an implementation book. The objective of the book is to explain the concepts clearly. However, you need some background knowledge of calculus and linear algebra to understand the mathematical parts clearly.
The author has done a commendable job of lucidly explaining complex terms. It also provides excellent references for further reading and research. If math is not a concern for you, don’t think twice before picking up this book if you want to understand the underlying concepts of deep learning.
Originally Published- 2018
Author- Charu C. Aggarwal
You can buy this book here.
9. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Description: This is an excellent book for anyone looking to learn deep learning in-depth. You can rely on the book for gaining a practical approach to every aspect of Multilayer Perceptrons (MLP).
The book walks you through the theory and research done in the last ten years regarding MLP. It covers a wide variety of topics on Neural networks. The best part is that the book quickly explains pretty complex topics in an easy-to-understand manner.
The 30 code examples given in the book are well explained. Mind you, a thorough understanding of the code examples will help you gain deeper insights into deep learning. After reading it, you can start using the examples in real-world problems.
The book is written for Python programmers who want to learn deep learning and machine learning. Therefore, you’ll need a reasonable Python proficiency to extract the most out of this book.
- Originally Published- 1999
- Authors- Russell Reed, Robert J MarksII
You can buy this book here.
10. Neural Networks for Pattern Recognition
Description: This book is an excellent introduction to neural networks. It only covers feed-forward networks, excluding recurrent networks.
The book begins by providing the necessary foundation of neural networks and explains why they are so powerful and their practical aspects. This allows the reader to explore the modern treatment of neural treatments like deep learning. All through, the book sticks to its core aim of investigating statistical approaches to pattern recognition.
The exercises in the book are chosen carefully to ensure the understanding of the results. It is well-organized and proceeds at a steady pace. Because the book does not provide in-depth explanations, it may be more appropriate for someone who is already familiar with neural networks and their mathematical aspects.
- Originally Published- 1996
- Author- Christopher M. Bishop
You can buy this book here.
Conclusion
So, that was our list of the 10 best deep learning books out there. When it comes to the right option for your learning needs, take your pick, considering your learning requirements. If you are excited by the opportunities in artificial intelligence, deep learning is a must for you.
Deep learning is here to stay, and if you are eyeing a career in the field, you better start learning it as soon as you can. Let us know in the comments section if we missed any of your favorite books.
Happy Learning!
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