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Top 7 Must-Read Books on Deep Learning

  • Last Updated : 05 Nov, 2021

Deep Learning is a part of machine learning and artificial intelligence that uses multiple layers to extract useful information from raw data. From self-driven cars to voice assistant robots, all of these work on deep learning algorithms. As per the survey, it is observed that the global deep learning market is expected to rise by $44.3 billion by 2027. This simply means that there is a huge demand for deep learning professionals. A career in this ever-evolving domain promises insanely high salaries, growth opportunities, and global recognition. Deep learning professionals are hot assets currently as the demand is high but the supply is low. 


Big Tech giants are continuously rushing to secure these top minds. If you are someone who is willing to build their career in this lucrative domain then you are at the right place. In this blog, we will discuss the top 7 must-read books on deep learning that will surely enhance your deep learning skills. So let’s get started.

1. Deep Learning From Scratch: Building with Python from First Principles by Seth Weidman 

This is an amazing book to build your foundation of deep learning. The book is authored by Seth, a data scientist who lives in San Francisco. The book progresses in an extremely understandable manner from basics to advanced architectures, implementing everything from scratch. This book provides knowledge about:

  • How to apply multilayer neural network and convolutional networking
  • Mathematical and conceptual understanding of neural network
  • Implementation of neural network concepts using PyTorch
  • Extremely concise mental models accompanied by working codes and explanations

2. Neural Networks and Deep Learning by Michael Nielsen

It is a free online book that provides you with a perfect solution for many issues like NLP, image processing, and speech processing. This book will enhance your foundation of neural networks and deep learning. It will teach you about:

  • Neural network that helps computers learn from data
  • An amazing set of techniques for deep learning

This amazing book is authored by Micheal Nielsen who is a scientist by profession. He has contributed a lot to quantum computing and the modern open science movement.  

3. Hands-on Machine learning with Scikit-Learn, Keras, and TensorFlow by Aurelion Geron

The book is embedded with rigid examples and minimal theory that helps you gain in-depth knowledge of deep learning concepts and tools for building intelligent systems. The book progresses in an extremely understandable manner from basic linear regression to deep neural networks. You need prior programming experience to get started with this book. This book covers:

  • Machine learning landscape
  • Training models including decision trees, ensemble methods, and random forests
  • Using TensorFlow to build and train a neural network
  • And many more amazing concepts.

4. TensorFlow 1.x Deep Learning Cookbook

TensorFlow is an open-source platform for machine learning. This book will tell you how to use TensorFlow techniques for complex data manipulations. Further, it will help you dig deeper into data insights than ever before. The book is embedded concepts like:

  • Fundamentals of TensorFlow 
  • Linear regression techniques using TensorFlow 
  • High-level concepts like CNN, Natural language processing, RNN, and many more

Further, you will learn model evaluation, regression analysis, clustering analysis, and deep learning using TensorFlow. So overall it is a perfect book to get comfortable with the tensor flow library from scratch to production.  

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

This book provides you with the most practical approach to the subject and also helps you to create a coherent deep learning network. The book starts with the theory and then progresses to practical examples. It includes:

  • In-Depth understanding of deep learning and machine learning concepts
  • Evolution of neural network fundamentals from deep learning
  • Information about the deep network architecture
  • Using vectorization techniques for different data types
  • Understanding the usage of DL4J on Hadoop and Spark

The book is written by the collaborative efforts of Josh Patterson and Adam Gibson who are experts in the field of machine learning and deep learning.  

6. Hands-On Deep Learning Algorithms with Python by Sudharsan Ravichandran

This book enhances your knowledge about deep learning algorithms from basic to advanced and helps you to implement that using the TensorFlow library. The book is authored by Sudharsan Ravichandran who is a researcher, data scientist, and Artificial Intelligence enthusiast. This book is not recommended for complete beginners. You are required to have some basic knowledge of Python and machine learning concepts to understand this book. It covers the following topics:

  • Mathematics fundamentals behind deep learning
  • Overview about gradient descent variables like AMSGrad, Nadam, and AdaDelta
  • Improvise mathematical knowledge of convolutional and capsule networks
  • Implement generative adversarial networks like CycleGAN CGAN, and StackGAN

7. Deep Learning for Computer Vision with Python

This book will make you an expert in deep learning for computer vision and visual recognition tasks. This book is for students, researchers, and developers who have basic programming experience and are willing to become proficient in deep learning and machine learning concepts. This book focuses on:

  • Machine learning and Neural Network
  • Object detection and localization with machine learning
  • CNN and training large scale networks
  • Implementing deep learning concepts using python programming, TensorFlow, and Keras

These are the 7 must-read books for data science. Apart from these, several other books will enhance your knowledge:

  • Deep learning in Python/ Pytorch by Manning Publications
  • Deep Learning for Vision Systems, by Mohamed Elgendy    
  • Grokking Deep Learning by Andrew W. Trask 
  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville 
  • Artificial Intelligence by Example (2nd Edition), by Denis Rothman
  • Deep Learning for coders with fastai and PyTorch by Jeremy Howard & Sylvain Gugger 
  • Deep Learning: Methods and Applications By Li Deng and Dong Yu

We hope that you found this helpful. All the best and Happy learning!

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