Ballester rubén (40 resultados)

Condición: As New. Unread book in perfect condition. Ballester, Tomás Benet (ilustrador).

Condición: New. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Condición: As New. Unread book in perfect condition. Fresneda, Rubén (ilustrador).

Condición: New. Fresneda, Rubén (ilustrador).

Condición: New. Ballester, Tomás Benet (ilustrador).

Condición: As New. Unread book in perfect condition. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Condición: As New. : Sumérgete en el Londres de finales del siglo XIX con esta edición de 'El doctor Jekyll y Míster Hide'. A través de la investigación del abogado Utterson, se desvela la dualidad del ser humano, personificada en la relación entre el respetado doctor Jekyll y el misterioso y malvado Mr. Hyde. Esta edición, publicada por CreateSpace Independent Publishing Platform, presenta la clásica historia de Robert Louis Stevenson con ilustraciones de Rubén Fresneda y Tomás Benet Ballester, ideal para los amantes de la ficción clásica y el suspense psicológico. EAN: 9781494290429 Tipo: Libros Categoría: Literatura y Ficción|Ciencia Ficción Título: El doctor Jekyll y Míster Hide Autor: Robert Louis Stevenson Editorial: CreateSpace Independent Publishing Platform Idioma: es-ES Páginas: 102 Formato: tapa blanda. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).…

Condición: As New. Unread book in perfect condition. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Condición: New. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Condición: As New. Unread book in perfect condition. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Condición: As New. Unread book in perfect condition. Fresneda, Rubén (ilustrador).

Condición: As New. Unread book in perfect condition. Ballester, Tomás Benet (ilustrador).

Condición: New. Fresneda, Rubén (ilustrador).

Condición: New. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Condición: New. Ballester, Tomás Benet (ilustrador).

Condición: As New. Unread book in perfect condition. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Condición: New. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Paperback. Condición: new. Paperback. 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. text-justify: inter-ideograph;">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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Condición: New. Print on Demand. Fresneda, Rubén (ilustrador).

Condición: New. Print on Demand. Ballester, Tomás Benet (ilustrador).

Condición: New. Print on Demand. Fresneda, Rubén (ilustrador).

Condición: New. Print on Demand. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Paperback. Condición: Brand New. 120 pages. 9.25x6.10x9.25 inches. In Stock.

Condición: New.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Paperback. Condición: new. Paperback. 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. text-justify: inter-ideograph;">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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condición: New. Print on Demand. Fresneda, Rubén; Ballester, Tomás Benet (ilustrador).

Taschenbuch. Condición: Neu. Topological Data Analysis for Neural Networks | Rubén Ballester (u. a.) | Taschenbuch | SpringerBriefs in Computer Science | xii | Englisch | 2026 | Springer | EAN 9783032082824 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condición: new. Paperback. 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. text-justify: inter-ideograph;">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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…