The Matrix Calculus You Need For Deep Learning
by Terence Parr, Jeremy Howard
Publisher: arXiv.org 2018
Number of pages: 33
Description:
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 math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed.
Download or read it online for free here:
Download link
(740KB, PDF)
Similar books
Deep Learning in Neural Networks: An Overviewby Juergen Schmidhuber - arXiv
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium.
(12816 views)
Deep Learning Tutorialby 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.
(10597 views)
Deep Learning: Technical Introductionby 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.
(8189 views)
Neural Networks and Deep Learningby 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.
(13654 views)