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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Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2023] ©2023 |
Year of Publication: | 2023 |
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Berlyand, Leonid, author. aut http://id.loc.gov/vocabulary/relators/aut Mathematics of Deep Learning : An Introduction / Leonid Berlyand, Pierre-Emmanuel Jabin. Berlin ; Boston : De Gruyter, [2023] ©2023 1 online resource (VI, 126 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda De Gruyter Textbook 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 restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star 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 of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. The material is based on a one-semester course Introduction to Mathematics of Deep Learning" for senior undergraduate mathematics majors and first year graduate students in mathematics. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics. Issued also in print. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Mai 2023) Deep learning (Machine learning) Mathematics. Faltungsneuronale Netze. Künstliche Neuronale Netze. Maschinelles Lernen. Tiefes Lernen. COMPUTERS / Intelligence (AI) & Semantics. bisacsh Machine Learning, Deep Learning, Artificial Neural Networks (ANNs), Regression, Deep Neural Networks (DNNs),. Jabin, Pierre-Emmanuel, author. aut http://id.loc.gov/vocabulary/relators/aut Title is part of eBook package: De Gruyter DG Plus DeG Package 2023 Part 1 9783111175782 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2023 English 9783111319292 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2023 9783111318912 ZDB-23-DGG Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2023 English 9783111319124 Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2023 9783111318165 ZDB-23-DEI EPUB 9783111025803 print 9783111024318 https://doi.org/10.1515/9783111025551 https://www.degruyter.com/isbn/9783111025551 Cover https://www.degruyter.com/document/cover/isbn/9783111025551/original |
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English |
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Berlyand, Leonid, Berlyand, Leonid, Jabin, Pierre-Emmanuel, |
spellingShingle |
Berlyand, Leonid, Berlyand, Leonid, Jabin, Pierre-Emmanuel, Mathematics of Deep Learning : An Introduction / De Gruyter Textbook 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 |
author_facet |
Berlyand, Leonid, Berlyand, Leonid, Jabin, Pierre-Emmanuel, Jabin, Pierre-Emmanuel, Jabin, Pierre-Emmanuel, |
author_variant |
l b lb l b lb p e j pej |
author_role |
VerfasserIn VerfasserIn VerfasserIn |
author2 |
Jabin, Pierre-Emmanuel, Jabin, Pierre-Emmanuel, |
author2_variant |
p e j pej |
author2_role |
VerfasserIn VerfasserIn |
author_sort |
Berlyand, Leonid, |
title |
Mathematics of Deep Learning : An Introduction / |
title_sub |
An Introduction / |
title_full |
Mathematics of Deep Learning : An Introduction / Leonid Berlyand, Pierre-Emmanuel Jabin. |
title_fullStr |
Mathematics of Deep Learning : An Introduction / Leonid Berlyand, Pierre-Emmanuel Jabin. |
title_full_unstemmed |
Mathematics of Deep Learning : An Introduction / Leonid Berlyand, Pierre-Emmanuel Jabin. |
title_auth |
Mathematics of Deep Learning : An Introduction / |
title_alt |
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 |
title_new |
Mathematics of Deep Learning : |
title_sort |
mathematics of deep learning : an introduction / |
series |
De Gruyter Textbook |
series2 |
De Gruyter Textbook |
publisher |
De Gruyter, |
publishDate |
2023 |
physical |
1 online resource (VI, 126 p.) Issued also in print. |
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 |
isbn |
9783111025551 9783111175782 9783111319292 9783111318912 9783111319124 9783111318165 9783111025803 9783111024318 |
callnumber-first |
Q - Science |
callnumber-subject |
Q - General Science |
callnumber-label |
Q325 |
callnumber-sort |
Q 3325.73 B47 42023 |
url |
https://doi.org/10.1515/9783111025551 https://www.degruyter.com/isbn/9783111025551 https://www.degruyter.com/document/cover/isbn/9783111025551/original |
illustrated |
Not Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
000 - Computer science, knowledge & systems |
dewey-ones |
006 - Special computer methods |
dewey-full |
006.310151 |
dewey-sort |
16.310151 |
dewey-raw |
006.310151 |
dewey-search |
006.310151 |
doi_str_mv |
10.1515/9783111025551 |
oclc_num |
1376935109 |
work_keys_str_mv |
AT berlyandleonid mathematicsofdeeplearninganintroduction AT jabinpierreemmanuel mathematicsofdeeplearninganintroduction |
status_str |
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ids_txt_mv |
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Title is part of eBook package: De Gruyter DG Plus DeG Package 2023 Part 1 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2023 English Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2023 Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2023 English Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2023 |
is_hierarchy_title |
Mathematics of Deep Learning : An Introduction / |
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Title is part of eBook package: De Gruyter DG Plus DeG Package 2023 Part 1 |
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