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Beschreibung
This book presents in-depth explanations of well-known and recognized behaviors of neural networks in machine learning. In addition, the author provides novel technical analyses of behaviors of discrete-time dynamical systems modeled as difference equations. These analyses and their outcomes are closely related to models of very well-known neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, which are widely used in machine learning and artificial intelligence (AI) applications. The author also discusses difference equations and their relevance to neural networks, machine learning, and AI.
Details
| Verlag | Springer International Publishing |
| Ersterscheinung | 20. Oktober 2025 |
| Maße | 24 cm x 16.8 cm |
| Gewicht | 479 Gramm |
| Format | Hardcover |
| ISBN-13 | 9783032009098 |
| Seiten | 143 |
Schlagwörter
Analysis, Angewandte Mathematik, Applications of Mathematics, Artificial Intelligence, Difference and Functional Equations, Differentialrechnung und -gleichungen, Künstliche Intelligenz, Machine Learning, Maschinelles Lernen, Mathematical Models of Cognitive Processes and Neural Networks, Mathematische Analysis - allgemein, Mathematische Modellierung
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