Best Deep Learning Books

Deep learning is the newest trend emerging from machine learning, many students and professionals really care, but there is so much learning material available online that selecting the right book to learn is hard work.

If you are looking for books on Deep Learning to advance your knowledge, here is the best list in various formats available for free:

1. Deep Learning Tutorial
By LISA Lab, University of Montreal

Developed by the LISA Laboratory at the University of Montreal, this free, concise tutorial presented in book form explores the basics of machine learning. The book emphasizes the use of the Theano library (originally developed by the university itself) to create deep learning models in Python.

2. Deep Learning
By Ian Goodfellow, Yoshua Bengio and Aaron Courville

The Deep Learning textbook is a resource intended to help students and professionals enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will continue to be available online for free.

3. Deep Learning: Methods and Applications
By Li Deng and Dong Yu

This book provides an overview of the general deep learning methodology and its applications to a variety of signal and information processing tasks.

4. First Contact with TensorFlow, get started with Deep Learning Programming
By Jordi Torres

This book is for engineers with a basic understanding of machine learning who want to expand their wisdom into the exciting world of deep learning with a hands-on approach using TensorFlow.

5. A Brief Introduction to Neural Networks
By David Kriesel

This title covers neural networks in depth. Neural networks are a biology-inspired data processing mechanism, which allows computers to technically learn in a similar way to the brain and even generalize once solutions to enough problem cases are taught. Available in English and German.

6. Neural Networks and Deep Learning
By Michael Nielsen

This book teaches you about neural networks, a beautiful biologically-inspired programming paradigm that allows a computer to learn from observational data. It also covers deep learning, a powerful set of techniques for learning in neural networks.

7. Neural Networks and Learning Machines (3rd edition)
By Simon Haykin

This third edition of Simon Haykin's book provides an up-to-date, comprehensive and readable treatment of neural networks, divided into three sections. The book begins by looking at the classic approach to supervised learning, before moving on to core methods based on radially based function networks (RBF). The final part of the book is devoted to the theory of regularization, which is the core of machine learning.

8. Neural Network Design (2nd edition)
By Martin T. Hagan, Howard B. Demuth, Mark H. Beale and Orlando D. Jess

NEURAL NETWORK DESIGN (2nd Edition) provides a clear and detailed survey of the fundamental neural network architectures and learning rules. In it, the authors emphasize a fundamental understanding of the main neural networks and the methods to train them. The authors also discuss the applications of networks to practical engineering problems in pattern recognition, clustering, signal processing, and control systems. The readability and natural flow of the material are emphasized throughout the text.

And here Some early Access Books :

• Deep Learning with Python
• Deep Learning with JavaScript
• Grokking Deep Learning
• Deep Learning for Search
• Deep Learning and the Game of Go
• Machine Learning for Business