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8 Free E-Books To Learn Deep Learning

8 Free E-Books To Learn Deep Learning

Ambika Choudhury

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Deep Learning is a powerful method when it comes to dealing with unstructured data. This technique helps a machine learn from its own experience and solve complex problems. Some of the breakthroughs accomplished through deep learning techniques are self-driving cars, virtual assistants, Google’s AlphaGo, among others. 

In this article, we list down – in no particular order – eight free e-books to deep dive into Deep Learning.



1| Deep Learning

About: This book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. It will help them understand how computers learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. It includes topics like regularisation for deep learning, convolutional networks, linear algebra, probability and information theory, deep feedforward networks, and more. 

Read the book here.

2| Deep Learning Methods & Applications

About: Microsoft researchers Li Deng and Dong Yu wrote this book. It provides an overview of deep learning methodologies and their application in a variety of signal and information processing tasks, such as automatic speech recognition (ASR), computer vision, language modeling, text processing, multimodal learning, and information retrieval. 

Read the book here.

3| Applied Deep Learning

About: In this book, you will learn how to implement advanced techniques in the right way in Python and TensorFlow, debug and optimise advanced methods, such as dropout and regularisation, carry out error analysis to realize if one has a bias problem, a variance problem, a data offset problem, and so on and lastly, setting up a machine learning project focused on deep learning on a complex dataset. 


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Read the book here.

4| A Brief Introduction To Neural Networks

About: Deep Learning is mostly about diving deep into the neural networks, optimisation algorithms, error analysis along with other such topics. A Brief Introduction to Neural Networks by David Kriesel delves into the introduction, motivation, and history of neural networks. The topic includes components of artificial neural networks, fundamentals on learning and training samples, supervised learning network paradigms, recurrent perceptron-like networks, Hopfield networks, and more.  

Read the book here.

5| Neural Networks & Deep Learning

About: This book will help you master the core concepts of neural networks, including modern techniques for deep learning. It also includes the written code that uses neural networks and deep learning to solve complex pattern recognition problems. According to the author, Michael Nielsen, after reading this book, you will have a foundation to use neural networks and deep learning to attack various problems. 

Read the book here.

See Also

6| First Contact With Deep Learning

About: The First Contact with Deep Learning book will guide you to understand the basics of deep learning with the help of the Keras library, which you will learn to use to develop and evaluate deep learning models. According to the author, this is an introductory book, which will focus on practical issues to show the reader the exciting world that can be opened up with the use of this technology. 

Read the book here.

7| Neural Networks & Learning Machines

About: Neural Networks and Learning Machines is written by Simon Haykin. This book will introduce you to several important topics of deep learning. The topics included are an introduction to neural networks, model building through regression, multi-layer perceptrons, principal component analysis, information-theoretic learning models, and more. 

Read the book here.

8| Machine Learning, Neural, & Statistical Classification

About: This book aims to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging datasets, and draw conclusions on their applicability to realistic industrial problems. The topics covered are an introduction to classification, neural networks, machine learning of rules and trees, and more.

Read the book here.

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