Neural Networks and Numerical Analysis
Seiten
2022
De Gruyter (Verlag)
978-3-11-078312-4 (ISBN)
De Gruyter (Verlag)
978-3-11-078312-4 (ISBN)
The series is devoted to the publication of high-level monographs and specialized graduate texts which cover the whole spectrum of applied mathematics, including its numerical aspects. The focus of the series is on the interplay between mathematical and numerical analysis, and also on its applications to mathematical models in the physical and life sciences.
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.
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.
Bruno Despres, Sorbonne University, France
| Erscheinungsdatum | 10.05.2022 |
|---|---|
| Reihe/Serie | De Gruyter Series in Applied and Numerical Mathematics ; 6 |
| Zusatzinfo | 18 b/w and 11 col. ill. |
| Verlagsort | Berlin/Boston |
| Sprache | englisch |
| Maße | 170 x 240 mm |
| Gewicht | 434 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Analysis |
| Schlagworte | artificial initelligence • machine learning • Näherungseigenschaften • Neural networks • neural networks, machine learning, artificial initelligence, Tensorflow, numerical schemes, partial differential equations. • neural networks, machine learning, artificial initelligence, Tensor ow, numerical schemes, partial • neural networks, machine learning, artificial initelligence, Tensor ow, numerical schemes, partial d • Neuronale Netze • numerical schemes • Numerische Mathematik • Partial Differential Equations. • Tensorflow • Tensor ow |
| ISBN-10 | 3-11-078312-6 / 3110783126 |
| ISBN-13 | 978-3-11-078312-4 / 9783110783124 |
| Zustand | Neuware |
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