by Yoshua Bengio, Ian Goodfellow, Aaron Courville
Publisher: MIT Press 2014
Number of pages: 274
This book can be useful for the university students (undergraduate or graduate) learning about machine learning and the engineers and practitioners of machine learning, artificial intelligence, data-mining and data science aiming to better understand and take advantage of deep learning.
Home page url
Download or read it online for free here:
by Terence Parr, Jeremy Howard - arXiv.org
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math.
by Michael Nielsen
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.
by LISA lab - University of Montreal
This book will introduce you to some of the most important deep learning algorithms and show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.
by Thomas Epelbaum - arXiv.org
This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. For each network, their fundamental building blocks are detailed.