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Advances and applications in deep learning / Marco Antonio Aceves-Fernandez (2020)  

Public Titre : Advances and applications in deep learning Type de document : Monographie Auteurs : Marco Antonio Aceves-Fernandez, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 122 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-1-83962-879-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] constante diélectrique [Termes IGN] méthode fondée sur le noyau [Termes IGN] programmation stochastique [Termes IGN] temps réel [Termes IGN] vision par ordinateur Résumé : (auteur) Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this context, Deep Learning (DL) is one of the techniques that has taken the lead for cognitive processes, pattern recognition, object detection, and machine learning, all of which have played a crucial role in the growth of AI. As such, this book examines DL applications and future trends in the field. It is a useful resource for researchers and students alike. Note de contenu : 1- Advancements in deep learning theory and applications: Perspective in 2020 and beyond 2- Advances in convolutional neural networks 3- Transfer learning and deep domain adaptation 4- Deep learning enabled nanophotonics 5- Explainable artificial intelligence (xAI) approaches and deep meta-learning models 6- Dynamic decision-making for stabilized deep learning software platforms Numéro de notice : 28565 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87786 En ligne : https://doi.org/10.5772/intechopen.87786 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97647