| Autore: |
Ye, Jong Chul
|
| Titolo: |
Geometry of Deep Learning : A Signal Processing Perspective / Jong Chul Ye
|
| Pubblicazione: |
Singapore, : Springer, 2022 |
| Descrizione fisica: |
xvi, 330 p. : ill. ; 24 cm |
| Soggetto topico: |
68-XX - Computer science [MSC 2020] |
| |
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] |
| |
68T07 - Artificial neural networks and deep learning [MSC 2020] |
| |
92B20 - Neural networks for/in biological studies, artificial life and related topics [MSC 2020] |
| |
92C20 - Neural biology [MSC 2020] |
| |
94A12 - Signal theory (characterization, reconstruction, filtering, etc.) [MSC 2020] |
| Soggetto non controllato: |
Deep Learning |
| |
Deep neural networks |
| |
Mathematical principle of deep learning |
| |
Optimal transport |
| |
Review of state-of-the art deep learning methods |
| Titolo autorizzato: |
Geometry of deep learning |
|
| Formato: |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione: |
Inglese |
| Record Nr.: | VAN00278399 |
|
| Lo trovi qui: | Univ. Vanvitelli |
| Localizzazioni e accesso elettronico |
https://doi.org/10.1007/978-981-16-6046-7 |
| Opac: |
Controlla la disponibilità qui |
