Neural Networks and Numerical Analysis / / Bruno Després.
This book uses numerical analysis as the main tool to investigate methods in machine learning and neural networks. The efficiency of neural network representations for general functions and for polynomial functions is studied in detail, together with an original description of the Latin hypercube me...
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Superior document: | Title is part of eBook package: De Gruyter DG Plus DeG Package 2022 Part 1 |
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Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2022] ©2022 |
Year of Publication: | 2022 |
Language: | English |
Series: | De Gruyter Series in Applied and Numerical Mathematics ,
6 |
Online Access: | |
Physical Description: | 1 online resource (XVI, 158 p.) |
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072 | 7 | |a MAT041000 |2 bisacsh | |
100 | 1 | |a Després, Bruno, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
245 | 1 | 0 | |a Neural Networks and Numerical Analysis / |c Bruno Després. |
264 | 1 | |a Berlin ; |a Boston : |b De Gruyter, |c [2022] | |
264 | 4 | |c ©2022 | |
300 | |a 1 online resource (XVI, 158 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
347 | |a text file |b PDF |2 rda | ||
490 | 0 | |a De Gruyter Series in Applied and Numerical Mathematics , |x 2512-1820 ; |v 6 | |
505 | 0 | 0 | |t Frontmatter -- |t Introduction -- |t Acknowledgement -- |t Contents -- |t 1 Objective functions, neural networks, and linear algebra -- |t 2 Approximation properties -- |t 3 A functional equation -- |t 4 Datasets -- |t 5 Stochastic gradient methods -- |t 6 Examples and research in the field -- |t Bibliography -- |t Index |
506 | 0 | |a restricted access |u http://purl.org/coar/access_right/c_16ec |f online access with authorization |2 star | |
520 | |a This book uses numerical analysis as the main tool to investigate methods in machine learning and neural networks. The efficiency of neural network representations for general functions and for polynomial functions is studied in detail, together with an original description of the Latin hypercube method and of the ADAM algorithm for training. Furthermore, unique features include the use of Tensorflow for implementation session, and the description of on going research about the construction of new optimized numerical schemes. | ||
530 | |a Issued also in print. | ||
538 | |a Mode of access: Internet via World Wide Web. | ||
546 | |a In English. | ||
588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Mai 2023) | |
650 | 4 | |a Neuronale Netze. | |
650 | 4 | |a Numerische Mathematik. | |
650 | 4 | |a Näherungseigenschaften. | |
650 | 7 | |a MATHEMATICS / Numerical Analysis. |2 bisacsh | |
653 | |a neural networks, machine learning, artificial initelligence, Tensorflow, numerical schemes, partial differential equations. | ||
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t DG Plus DeG Package 2022 Part 1 |z 9783110766820 |
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE COMPLETE 2022 English |z 9783110993899 |
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE COMPLETE 2022 |z 9783110994810 |o ZDB-23-DGG |
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE Mathematics 2022 English |z 9783110993868 |
773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t EBOOK PACKAGE Mathematics 2022 |z 9783110770445 |o ZDB-23-DMA |
776 | 0 | |c EPUB |z 9783110783261 | |
776 | 0 | |c print |z 9783110783124 | |
856 | 4 | 0 | |u https://doi.org/10.1515/9783110783186 |
856 | 4 | 0 | |u https://www.degruyter.com/isbn/9783110783186 |
856 | 4 | 2 | |3 Cover |u https://www.degruyter.com/document/cover/isbn/9783110783186/original |
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