Best Books On Deep Learning To Read This Year

I am sure, every one of us has heard the word, Artificial Intelligence. But have you ever come across the term, DEEP Learning? Whenever you talk about Artificial Intelligence, you will always find a word linked to it, i.e Deep Learning. Like AI, deep learning also requires higher skills and knowledge. And the best way to gain that knowledge is by referring to books. There are many books on deep learning available that provide in-depth knowledge about this subject.

Books are the soul of your learning process, especially if you are a beginner in some field. Deep learning is a subject that requires a complete understanding of the basics, that can be only acquired through books. 

In today’s article, I am informing you about some of the best deep learning books. But before that, it’s important to know, What is deep learning? How is it linked to Artificial Intelligence? So, let’s start with a detailed definition of Deep Learning? 

What is Deep Learning? 

Deep Learning is a subset of Artificial Intelligence and Machine Learning. It used various methods to extract the information from raw data. It’s a very crucial element of data science that imitates the way of gaining human knowledge.

It makes the use of algorithms, statistics, and predictive modeling to execute large amounts of data. Deep learning forms the basis of all the amazing inventions that we see today. Whether it be a voice assistant robot or self-driven cars, all run on the concept of deep learning algorithms.

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The deep learning field is growing at a very faster rate, especially in the last few years. In fact, according to one of the surveys, the Deep learning market will touch the mark of $55 billion by the end of 2027. Looking at such numbers, you can assume the future field in the upcoming years.

Top 7 Deep Learning Books To Invest Your Time

Knowledge is incomplete without books. So here is the list of some of the best books on Deep learning you can read in 2022.

Deep Learning from the Scratch, building with Python from First Principles: by (Seth Weidman) 

Deep Learning from the Scratch, building with Python from First Principles: by (Seth Weidman) 

As the name suggests, deep learning from the scratch. This book explains all the things from the very first principle like how we begin our journey of learning. It’s a complete package consisting of various levels from basic to advanced knowledge.

Whether you are a beginner or already a professional in this field, this book is a must-read for you. Some of the main topics covered in the book are: 

  •  Multilayer Neural Network and Convolutional Networking 
  • Conceptual understanding of mathematical Neural network
  • Implementing neural network using Pytorch
  • Mental models and working of codes

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

Deep Learning: A Practitioner's Approach by Adam Gibson and Josh Patterson

This book provides a very practical approach to Deep learning and how it helps in creating a coherent learning network. The theories used in the book are of intermediate level, so the book mainly targets the people who are already in this field.

One specialty of this book is that it provides many examples after each theory so it becomes easy for you to understand. This is the reason it holds that place in some of the top books on Deep learning available. Some of the main theories highlighted in the book are: 

  • Relationship between Deep Learning and Machine learning concepts
  • Fundamentals of Neural Network
  • Implementation of Deep Learning architecture
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Deep Learning with PyTorch: by (Eli Stevens and Thomas Viehmann)

Deep Learning with PyTorch: by (Eli Stevens and Thomas Viehmann)

This book is one of the best books on deep learning, you will come across. All the theories, concepts are explained in detail in a very simple and easy-to-understand language. Apart from this, the book is divided into three parts that make it easier to understand. 

Part 1: It deals with the concepts of core PyTorch where it provides about Deep learning and Pytorch concepts. It also talks about different models such as Pretrained models, Tensor models, etc.

Part 2: This part deals with learning from pictures in the real world. It covers many real-life examples of deep learning, machine learning, and Artificial intelligence. 

Part 3: It deals with the deployment of the Machine Learning application and how it can be used in the real life. It also explains the use of various models that form the basis of Deep Learning (also check the best machine learning books).

TensorFlow Deep Learning Cookbook: by ( Antonio Gulli and Amita Kapoor)

TensorFlow Deep Learning Cookbook: by (Antonio Gulli and Amita Kapoor)

The cookBook is a type of book that includes almost no theories and a lot of codes. Coding is an important element of Deep learning and if you are coding, this is a must-read book for you. This book explains how to use the TensorFlow library for various Deep learning aspects.

All the algorithms, techniques are explained in a very detailed manner. But this book is only for the people who like the cookbook style of teaching. This book is available on various online platforms such as Audible, Amazon, Flipkart.

Deep Learning for Vision systems: by (Mohamed Elgendy)

Deep Learning for Vision systems: by (Mohamed Elgendy)

This book explains future aspects of deep learning and how it is going to bring a revolution in the coming few years. The book promotes vision systems such as the concept of self-driving vehicles, human robots. Mohamed Elgendy in this book explains how vision systems use algebra concepts that are used in Machine learning.

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It also provides tutorials for building applications such as Facial recognition. Undoubtedly, one of the best books on deep learning is currently available in the market.

Neural Networks and Deep Learning: by ( Charu C. Aggarwal)

Neural Networks and Deep Learning: by ( Charu C. Aggarwal)

If you are looking for solutions for issues like image processing, NLP, you should read this book at least once. It covers both classical and modern aspects of Deep Learning in a very easy manner. Neural Networks are the heart of Deep learning and this book will help you make a strong foundation for that.

It also covers various theories of algorithms and how it is applied in Deep learning. You can buy this book from Amazon, Audible, Flipkart, etc.

Artificial Intelligence by Example: by ( Denis Rothman)

Artificial Intelligence by Example: by ( Denis Rothman)

Deep learning book that explains fundamental concepts of Artificial Intelligence. It explains how deep learning forms the basis for AI using different real-life examples. It includes AI applications, the Internet of things (IoT), neural networks, and chatbots.

You can use this as a textbook for teaching purposes and in professional fields also. The mixture of various theories makes this book one of the best books on deep learning, you can read in 2022.


Deep learning is a subject that requires a deep understanding of its basics. If you are aiming for AI, you need to be a master of this subject. All the books on deep learning mentioned above are worth reading and highly useful.

If you are a beginner in Deep Learning, it is also better to start with the basic books. Once your foundation is strong, you can go for the advanced books provided in the above list. Read all these books and decide what suits you the best.

I hope this article proves helpful for you.

Frequently Asked Questions (FAQs)

What do you think is the basic difference between Deep Learning and Machine Learning?

Machine Learning involves the use of computers to perform a task with less human intervention. Deep learning involves a complex structure of algorithms modeled on the human brain. Although, both of them are an important part of Artificial Intelligence.

What are the future career opportunities in Deep Learning?

If you are planning to set up your career in Deep learning, you have a bright future ahead. Some of the main carrier opportunities provided by Deep learning are:

– Data Engineer
– Research Analyst
– Neuroinformatics
– Software Developer
– Deep Learning Instructor
– Natural Language Process Engineer