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Recent Trends in Learning From Data : Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) / edited by Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita

Contributor(s): Oneto, Luca, editor | Navarin, Nicolò, editor | Sperduti, Alessandro, editor | Anguita, Davide, editor | SpringerLink (Online service)
Material type: materialTypeLabelE-bookSeries: (Studies in Computational Intelligence, 1860-949X; 896); (Intelligent Technologies and Robotics (Springer-42732)).Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: First edition.Description: 1 recurso en línea (VII, 221 páginas) : 81 ilustraciones, 55 ilustraciones a color.ISBN: 9783030438838.Subject: Datos masivos | Redes neuronales artificialesOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction: Recent Trends in Learning From Data -- Learned data structures -- Deep Randomized Neural Networks -- Tensor Decompositions and Practical Applications -- Deep learning for graphs -- Limitations of Shallow Networks -- Fairness in Machine Learning -- Online Continual Learning on Sequences.
Abstract: This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería QA76.87 2020 EB (Browse shelf(Opens below)) Acceso electrónico eBook.26062040
Total holds: 0

Introduction: Recent Trends in Learning From Data -- Learned data structures -- Deep Randomized Neural Networks -- Tensor Decompositions and Practical Applications -- Deep learning for graphs -- Limitations of Shallow Networks -- Fairness in Machine Learning -- Online Continual Learning on Sequences.

This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.

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