Mathematics of Deep Learning : : An Introduction /

Mathematics of Deep Learning : : An Introduction / / Leonid Berlyand, Pierre-Emmanuel Jabin.

The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point o...

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Bibliographic Details
Superior document:Title is part of eBook package: De Gruyter DG Plus DeG Package 2023 Part 1
VerfasserIn:
Berlyand, Leonid,
Jabin, Pierre-Emmanuel,
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2023]
©2023
Year of Publication:2023
Language:English
Series:De Gruyter Textbook
Online Access:
Physical Description:1 online resource (VI, 126 p.)
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Table of Contents:
  • Frontmatter
  • Contents
  • 1 About this book
  • 2 Introduction to machine learning: what and why?
  • 3 Classification problem
  • 4 The fundamentals of artificial neural networks
  • 5 Supervised, unsupervised, and semisupervised learning
  • 6 The regression problem
  • 7 Support vector machine
  • 8 Gradient descent method in the training of DNNs
  • 9 Backpropagation
  • 10 Convolutional neural networks
  • A Review of the chain rule
  • Bibliography
  • Index

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