Logo

The Matrix Calculus You Need For Deep Learning

The Matrix Calculus You Need For Deep Learning
by

Publisher: arXiv.org
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.

Home page url

Download or read it online for free here:
Download link
(740KB, PDF)

Similar books

Book cover: Deep Learning: Technical IntroductionDeep Learning: Technical Introduction
by - 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.
(5211 views)
Book cover: Deep LearningDeep Learning
by - MIT Press
This book can be useful for the university students learning about machine learning and the practitioners of machine learning, artificial intelligence, data-mining and data science aiming to better understand and take advantage of deep learning.
(16473 views)
Book cover: Neural Networks and Deep LearningNeural Networks and Deep Learning
by
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.
(9577 views)
Book cover: Deep Learning in Neural Networks: An OverviewDeep Learning in Neural Networks: An Overview
by - 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.
(9929 views)