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:
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.
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.
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.
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.
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.
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.
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.
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.
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