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Bücher Englische Bücher Wissen & Bildung Naturwissenschaft, Medizin, Informatik, Technik Informatik & EDV Informatik Topological Data Analysis for Neural Networks
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Topological Data Analysis for Neural Networks
Rubén Ballester, Carles Casacuberta, Sergio Escalera (Broschiert, Englisch)
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Beschreibung
This book offers a comprehensive presentation of methods from topological data analysis applied to the study of neural network structure and dynamics. Using topology-based tools such as persistent homology and the Mapper algorithm, the authors explore the intricate structures and behaviors of fully connected feedforward and convolutional neural networks. The authors discuss various strategies for… extracting topological information from data and neural networks, synthesizing insights and results from over 40 research articles, including their own contributions to the study of activations in complete neural network graphs. Furthermore, they examine how this topological information can be leveraged to analyze properties of neural networks such as their generalization capacity or expressivity. Practical implications of the use of topological data analysis in deep learning are also discussed, with a focus on areas including adversarial detection and model selection. The authors conclude with a summary of key insights along with a discussion of current challenges and potential future developments in the field. This monograph is ideally suited for mathematicians with a background in topology who are interested in the applications of topological data analysis in artificial intelligence, as well as for computer scientists seeking to explore the practical use of topological tools in deep learning. 53,49 €
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Technische Daten
Erscheinungsdatum
03.01.2026
Sprache
Englisch
EAN
9783032082824
Herausgeber
Springer International Publishing
Serien- oder Bandtitel
SpringerBriefs in Computer Science
Sonderedition
Nein
Autor
Rubén Ballester, Carles Casacuberta, Sergio Escalera
Seitenanzahl
103
Einbandart
Broschiert
Schlagwörter
Topological Data Analysis, Topological Machine Learning, Persistent Homology, Mapper Algorithm, Artificial Intelligence, Deep Learning, Feedforward Neural Networks, Convolutional Neural Networks, Generative Models, Neural Network Dynamics, Loss Functions, Generalization Gap, Regularization, Adversarial Attacks
Thema-Inhalt
UYQM - Maschinelles Lernen
UYQ - Künstliche Intelligenz
UN - Datenbanken
PBWH - Mathematische Modellierung
UYA - Theoretische Informatik
PBP - Topologie
Höhe
235 mm
Breite
15.5 cm
Hersteller: Birkhäuser, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH
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