The Best Books to Study Deep Learning


I would like to take the time to point out that we have a millennia-old tradition of books in educating people, and even today, we are deeply reliant on paper textbooks.

I was always the type of person to enjoy presentations, videos lectures, and audiobooks. They are more exciting to me. Yet, I cannot deny the need for books with examples and documentation to learn things.

The intent of this article is to present to you the books on Deep Learning that are so top notch that even elite companies such as Facebook and Google recommend them. Without further ado, here’s the list:

1. Grokking Artificial Intelligence Algorithms by Rishal Hurbans published by Manning Publications

top-7-books-for-deep-learning grokking algorithm

This text discusses the entire journey from Classic A.I. such as Searching algorithms, intelligent search, and other gems to the evolutionary parts of A.I. that contain the later 21st-century techniques of Machine learning, Deep Learning and Reinforcement Q- learning.

You will study and build the base of how and with what methods, the AI of today evolved.

I strongly suggest this text to those, who are diving into AI for the first time and have a passion to understand A.I.’s evolution, every single one of its core aspects and not simply a few popular algorithms of deep learning or machine learning.

2. Deep Learning From Scratch: Building with Python from First Principles by Seth Weidman published by O`Reilley

Deep learning from scratch

The book takes a literal approach to the concept in its name, which involves building deep learning models from the initial concepts of Python. The writer clearly says in the text that when we study any concept in computer science, let’s say for example, “searching”, then let’s explain that concept.

The most vital parts to discuss it correctly are:

  • An description of the algorithm with plain English to eliminate confusion in the glossary,
  • Usual visual presentations of a properly functioning implementation of the algorithm to allow the reader to easily visualize the idea with deeper understanding,
  • Using math to explain why the algorithms function
  • An implementation of the algorithm in pseudocode.

This book fulfills all of the above-mentioned roles for someone with little to no background in the topic and has consistency to truly learn deep learning. This book is not that thick which is really amazing.

3. Deep learning in Python/ Pytorch by Manning Publications

This series of books is so popular and astonishing that even Pytorch itself recommends the PyTorch version of the book on its official site.

3.1 Deep Learning with Python

top-7-books-for-deep-learning | deep learning with python

The book is split into two sections:

The first is one (Fundamentals of Deep learning) in which you study from a high-level and learn the essential concepts of the subject.

Section two is (Deep learning in Practise) the part in which the text discusses deep learning for computer vision, Text and sequences, Advanced deep-learning practice, generative Deep learning.

3.2 Deep Learning with PyTorch

Deep learning with pytorch

As you already know, this book series is very popular. This text is divided into three sections:

Part 1 (Core PyTorch) in which you have the intro to deep learning and learn about the library of PyTorch, Pretrained models, Tensors as well as how they apply to situations, and more.

This is followed by Part 2 (Learning from Images in the Real-world) which discusses a real-world application of deep learning to the early detection of lung cancer as well as the complete development in detail which contributes greatly to the perspective of a learner.

The third part (Deployment) discusses the last step in any first time application of ML development, which allows others utilize the model you built and lets your model become live.

4. Grokking Deep Learning by Andrew W. Trask published by Manning Publications

grokking deep learning

If you want to know which book is the best to begin your journey to understanding deep learning, then I would close my eyes and point to this text. This book is by far the best available.

This text covers the majority of the content that is essential for you to get your hands dirty with deep learning. With this book, you will begin to walk a positive exponential slope in the direction towards intuition and knowledge of deep learning.

5. Hands-on machine learning with Scikit-learn Keras and TensorFlow by Aurelion Geron published by O` Reilley

 hands on machine learning

After you finish basic statistics, Machine learning and Deep learning…now you will want to up your game to use practical implementations and build a fully-baked Deep learning models using TensorFlow.

This book is the best one to use to learn Tensorflow. The author used a method to explain concepts which is exceptionally easy to understand and made the subject intuitive. You will feel more powerful with each time that you finish a certain section.

Studying and working together with this text will take your skill level up a notch in Deep learning and Machine learning. It’s definitely worth a try.

6. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville published by MIT Press


Here are some facts…this book’s authors include Yoshua Bengio, a pioneer in the subject of deep learning, and one of three godfathers in the subject of deep learning, Ian Goodfellow popular who is famous for the creation of Generative adversarial Networks (GANs).

This book has legendary status among books about deep learning. In addition to discussing concepts of deep learning, it helps you refresh on applied math that is relevant to deep learning.

I would not recommend this text to everybody. This book is for those who are concentrating on deep learning and want to push themselves really hard through all the maths required to understand why deep learning works.

7. Deep Learning for coders with fastai and PyTorch by Jeremy Howard & Sylvain Gugger published by O`Reilley

 fast ai and pytorch

This one is among the three best on the subject. It’s beautiful not just for deep learning by itself but also the other factors related to the practice of deep learning in practice such as Model to production, Data ethics and Your deep learning journey (a map to follow). These three things are vital to becoming a deep learning engineer or something similar.

It’s supporter by the site to teach people deep learning for free with videos and labs.

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