Deep Neural Networks in a Mathematical Framework
Mentés helye:
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| Testületi szerző: | |
| Közreműködő(k): | |
| Különgyűjtemény: | e-book |
| Formátum: | könyv |
| Nyelv: | angol |
| Megjelenés: |
Cham : Springer International Publishing,
2018
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| Sorozat: | SpringerBriefs in Computer Science, ISSN 2191-5768 |
| Tárgyszavak: | |
| Online elérés: | http://doi.org/10.1007/978-3-319-75304-1 |
| Címkék: |
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| id |
opac-EUL01-000977655 |
|---|---|
| collection |
e-book |
| institution |
L_042 EUL01 |
| spelling |
Caterini, Anthony L. EUL10001039756 Y Deep Neural Networks in a Mathematical Framework by Anthony L. Caterini, Dong Eui Chang Cham Springer International Publishing 2018 XIII, 84 p. online forrás szöveg txt rdacontent számítógépes c rdamedia távoli hozzáférés cr rdacarrier szövegfájl PDF rda SpringerBriefs in Computer Science 2191-5768. This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community. Nyomtatott kiadás: ISBN 9783319753034 Nyomtatott kiadás: ISBN 9783319753058 Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők. könyv e-book optikai alakfelismerés EUL10001010632 Y alakfelismerés EUL10000324145 Y neurális hálózatok mesterséges intelligencia EUL10000965239 Y Artificial intelligence. EUL10000183324 Y Optical pattern recognition. EUL10001087156 Y Artificial Intelligence. Pattern Recognition. elektronikus könyv Chang, Dong Eui Tft. EUL10001039761 Y SpringerLink (Online service) közreadó testület SpringerBriefs in Computer Science Online változat http://doi.org/10.1007/978-3-319-75304-1 Cham Springer International Publishing Imprint: Springer 2018 EUL01 |
| language |
English |
| format |
Book |
| author |
Caterini, Anthony L. |
| spellingShingle |
Caterini, Anthony L. Deep Neural Networks in a Mathematical Framework SpringerBriefs in Computer Science, ISSN 2191-5768. optikai alakfelismerés alakfelismerés neurális hálózatok -- mesterséges intelligencia Artificial intelligence. Optical pattern recognition. Artificial Intelligence. Pattern Recognition. elektronikus könyv |
| author_facet |
Caterini, Anthony L. Chang, Dong Eui, Tft. SpringerLink (Online service), közreadó testület |
| author2 |
Chang, Dong Eui, Tft. |
| author_corporate |
SpringerLink (Online service), közreadó testület |
| author_sort |
Caterini, Anthony L. |
| title |
Deep Neural Networks in a Mathematical Framework |
| title_short |
Deep Neural Networks in a Mathematical Framework |
| title_full |
Deep Neural Networks in a Mathematical Framework by Anthony L. Caterini, Dong Eui Chang |
| title_fullStr |
Deep Neural Networks in a Mathematical Framework by Anthony L. Caterini, Dong Eui Chang |
| title_full_unstemmed |
Deep Neural Networks in a Mathematical Framework by Anthony L. Caterini, Dong Eui Chang |
| title_auth |
Deep Neural Networks in a Mathematical Framework |
| title_sort |
deep neural networks in a mathematical framework |
| series |
SpringerBriefs in Computer Science, ISSN 2191-5768. |
| series2 |
SpringerBriefs in Computer Science |
| publishDate |
2018 |
| publishDateSort |
2018 |
| physical |
XIII, 84 p. : online forrás |
| isbn |
978-3-319-75304-1 |
| issn |
2191-5768. |
| callnumber-first |
Q - Science |
| callnumber-subject |
Q - General Science |
| callnumber-label |
Q334-342 |
| callnumber-raw |
14684 |
| callnumber-search |
14684 |
| topic |
optikai alakfelismerés alakfelismerés neurális hálózatok -- mesterséges intelligencia Artificial intelligence. Optical pattern recognition. Artificial Intelligence. Pattern Recognition. elektronikus könyv |
| topic_facet |
optikai alakfelismerés alakfelismerés neurális hálózatok -- mesterséges intelligencia Artificial intelligence. Optical pattern recognition. Artificial Intelligence. Pattern Recognition. elektronikus könyv optikai alakfelismerés alakfelismerés neurális hálózatok Artificial intelligence. Optical pattern recognition. Artificial Intelligence. Pattern Recognition. mesterséges intelligencia |
| url |
http://doi.org/10.1007/978-3-319-75304-1 |
| 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 |
006.3 |
| dewey-sort |
16.3 |
| dewey-raw |
006.3 |
| dewey-search |
006.3 |
| first_indexed |
2023-12-26T23:19:16Z |
| last_indexed |
2023-12-29T19:19:18Z |
| recordtype |
opac |
| publisher |
Cham : Springer International Publishing |
| _version_ |
1786641341237690369 |
| score |
13,368962 |
| generalnotes |
This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community. |