Deep Learning Architectures / Ovidiu Calin

Public ISBD Titre : Deep Learning Architectures : A Mathematical Approach Type de document : texte imprimé Auteurs : Ovidiu Calin, Auteur Mention d'édition : 1ère édition Editeur : Springer Année de publication : 2020 Importance : 760 pages ISBN/ISSN/EAN : 978-3-030-36723-7 Prix : 25670f Langues : Français (fre) Index. décimale : K.45 Intelligence artificielle et big-data, machine Learning Résumé : This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. Deep Learning Architectures : A Mathematical Approach [texte imprimé] / Ovidiu Calin, Auteur  . -  1ère édition . - Springer, 2020 . - 760 pages. ISBN : 978-3-030-36723-7 : 25670f Langues : Français (fre) Index. décimale : K.45 Intelligence artificielle et big-data, machine Learning Résumé : This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

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Programming with TensorFlow / Kolla Bhanu Prakash

Public ISBD Titre : Programming with TensorFlow : Solution for Edge Computing Applications Type de document : texte imprimé Auteurs : Kolla Bhanu Prakash, Auteur Editeur : Springer Année de publication : 2021 Importance : 190 pages ISBN/ISSN/EAN : 978-3-030-57079-8 Prix : 44910F Langues : Anglais (eng) Index. décimale : B.10.4 Ouvrages Universitaires et Généraux Résumé : This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs). Programming with TensorFlow : Solution for Edge Computing Applications [texte imprimé] / Kolla Bhanu Prakash, Auteur . - Springer, 2021 . - 190 pages. ISBN : 978-3-030-57079-8 : 44910F Langues : Anglais (eng) Index. décimale : B.10.4 Ouvrages Universitaires et Généraux Résumé : This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).

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