## Deep Learning has gained a lot of momentum since the early 80s. To explore the dynamics of it, we have shortlisted nine best Deep Learning books for you.

Although the emergence of Deep Learning traces back to the 1980s, the 2010s marked the beginning of the modern Deep Learning era. For many years, people have been using Neural Networks and Deep Learning interchangeably even though there’s a difference between the two.

### What is Deep Learning?

In its simplest form, an Artificial Neural Network can consist of three layers, viz. an input layer

where the data is given, a hidden layer where the information is processed and an output layer

where decision making is performed. Deep Learning is a self-teaching and learning Deep Neural

Network system that is made up of multiple layers, i.e., more than one hidden layer. It is the driving power behind Artificial Intelligence and Big Data. These are good enough reasons to learn how Deep Learning works as an essential step towards establishing a Deep Learning career.

Do keep in mind that even though we’ve tried to cover as many beginner-friendly books as possible, Deep Learning itself is an advanced technology that requires prior knowledge in Machine Learning and Mathematics. If you are not equipped with the prerequisites, we suggest

you gain an understanding of it before proceeding with Deep Learning.

With that being said, here are some books we recommend for mastering Deep Learning.

**Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville: The queen on a Chessboard**

Deep Learning was written to target University students who are in Deep Learning and Artificial

Intelligence research and Software Engineers who lack Machine Learning and Statistics

background. It is broken down into three sections and 20 chapters. The first chapter gives a

mathematical background and Machine Learning fundamentals, the second chapter focuses on

the core concepts of Neural Networks such as feedforward networks, recurrent networks, etc.,

and the third chapter covers less-developed research areas like probabilistic graphical models

and plenty more. This book might be a huge leap for beginners but for intermediates and

experts, it is the holy grail for Convolutional Neural Networks and Deep Learning.

**Deep Learning with Python by François Chollet: From the master of Neural Network library**

Written by the creator of Keras, one of the leading high-level neural networks APIs, this book has extensive explanations and practical examples to enhance your understanding of Deep Learning. Without focusing much on the mathematical notations, it explains quantitative

concepts via code snippets. This book is organised in two parts, part one focusing on a high-

level introduction to deep learning with explanations of all the notions required to get started

with Machine Learning and Neural Networks and part two consists of an in-depth dive into

practical applications of deep learning in computer vision and natural-language processing.

Overall, It is an excellent choice for anyone who wishes to explore Deep Learning thoroughly.

**Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron: For a more pragmatic learning**

This is a classic book that is recommended for beginners as it assumes that the user has no

knowledge of Machine Learning. It uses Python, Scikit-Learn, and Keras framework with TensorFlow as the backend engine to explain the various Machine Learning concepts. It starts

with the basics of Machine Learning and some important algorithms and slowly builds on to

more advanced topics like reinforcement learning in Deep Learning. Through a hands-on

approach, you will be able to grow an intuitive understanding of Machine Learning using

working examples and a little bit of theory. Although you require no Machine Learning background, you need to have programming experience in Python and be familiar with NumPy,

Pandas and Matplotlib.

**Deep Learning for Computer Vision with Python-Starter Bundle by Adrian Rosebrock:Filled with bundles of Intelligence**

Since traditional Computer Vision has been deemed to be slow and inflexible, Deep Learning

has been implemented with it to increase accuracy and performance. This book is a great

introductory book to start with Deep Learning for Computer Vision. It gives a practical walk-

through of main concepts such as image classification, object detection, training networks on

large-scale datasets, and much more. It is suitable for everyone from beginners to specialists

with various bundles available for each level. With the right proportion of theory taught in a

classroom/textbook and the actual hands-on knowledge, the gap between the two is filled up

for a more efficient and better understanding of Computer Vision.

**Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence by Sandro Skansi: A trip from math equations to simulations**

If you’re a complete beginner to Deep Learning, we’ve got your covered. This straightforward

and brief yet informative book consists of many simple examples to go with the theory you

encounter throughout the course of this book so that you won’t get lost in the dark. After a

crisp introduction to Machine Learning and Calculus, the book discusses Neural Networks and

the different types available and advances to more intricate concepts such as language

processing with deep learning. Throughout the last few chapters, the author touches Artificial

Intelligence and the role Deep Learning plays in it, opening up on research problems in connectionism. Overall, it serves as a complete guide to anyone who is new to Deep Learning.

**Hands-On Deep Learning Algorithms with Python by Sudharsan Ravichandran: The one for the Do-it-yourself attitude**

This book focuses primarily on Deep Learning and helps you build your own Neural Networks from scratch. It gives you an insight into each algorithm, the mathematical principles behind it,

and how to implement it in the simplest possible manner. Just like many other books on the

list, it uses TensorFlow to explain various examples throughout the course of the book. Through

this book, you’ll be able to get up to speed with gradient descent variants, math for convolutional and capsule networks, Convolutional Neural Networks, and many more advanced topics. If you’re aspiring to be a Data Scientist or a Machine Learning Engineer, you should go

for this book.

**Grokking Deep Learning by Andrew Trask and Andrew W. Trask: For a light-hearted and enlightening journey**

From the title itself, many of you would’ve guessed that this book is not a regular academic

book. Grokking Deep Learning helps you lay down the foundation of Deep Learning so that you

can master it over time. It assumes that you have no knowledge of linear algebra, calculus,

convex optimisation, or even Machine Learning. It does, however, require a programming

background in Python. The first few chapters give you a brief introduction of Machine Learning

and Neural Networks and slowly progresses to an in-depth look at advanced layers and

architectures. If you’re looking for a creative book that is not only fun and easy but also filled

with great content, then this book is the one for you.

**Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal: Old head on young shoulders**

The book “Neural Networks and Deep Learning” includes both classical as well as modern Deep

Learning models. It is focused on an academic, textbook-like treatment of the subject but doesn’t lack abundant applications to support the theory. To help readers navigate more easily, the book is divided into three sections. The basics of Neural Networks gives a brief introduction

to Neural Networks and its relationship with Machine Learning, The fundamentals of Neural

Networks lay the fundamental concepts, and Advanced Topics in Neural Networks deals with

complex concepts like Neural Turing machines. Because of the degree of complexity, this book

is recommended to graduate students aiming to be researchers and practitioners.

**Advanced Deep Learning with Keras by Rowel Atienza: For the adept looking for more**

We’re ending this list with a comprehensive guide for advanced readers who wish to explore

and build their own cutting-edge models using AI using Keras, powered by TensorFlow. The

book starts with an introduction to Convolutional Neural Networks, Recurrent Neural Network,

and Multilayer Perceptron which serve as the building blocks of Deep Learning. From there, it

slowly builds on to more advanced topics such as ResNet and DenseNet, and how to create

Autoencoders. It doesn’t stop there and explores Generative Adversarial Networks (GANs),

Variational AutoEncoders (VAEs), Deep Reinforcement Learning, etc. If these topics don’t

resonate with what you’ve learned so far, then this book is not for you.

For many years, books have been an excellent source of information that can be delivered

within a limited period of time. It will give you a fundamental and structured understanding of the subject with plenty of examples and exercises to work on. One could argue that there are various courses on Deep Learning and that books are just a waste of money but nothing beats a

good old-fashioned book that can make way for the realisation of your objectives. The books

we’ve compiled for you to learn Deep Learning will change your views and if you’re already an

avid reader, we hope you found some hidden gems through it.

To read more about Deep Learning, click here.

Good