Deep Learning Books (Updated April 2017)

Deep learning, especially Deep Neural Networks have become quite popular during last 2-3 years due to their amazing success in computer vision, speech recognition, and machine translation among others. Here is a list of most useful deep learning books out there as of April 2017

Deep Learning Books

Lot of new optimizations and techniques are getting discovered every month and hence it is difficult for a book to cover the cutting edge, especially if the book is about the latest tools and code to implement deep learning. That's why it is recommended to take a book published recently for tools/code etc.

Fundamental books

• Deep Learning Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville - very popular book, but math heavy. Available online for free. Ideal for graduate students and people with background in math or statistics. Don't pick this up if you are looking for a coding book to explore deep learning.

Applied Deep Learning

• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems - Amazing book using python and tensorflow.

• Deep Learning: A Practitioner's Approach by Adam Gibson and Josh Patterson. Uses DeepLearning4J as a technology to explore deep learning.