| Titre : | PRINCIPLES AND LABS FOR DEEP LEARNING | | Type de document : | texte imprimé | | Auteurs : | Shih-Chia Huang, Auteur; Trung-Hieu Le, Auteur | | Mention d'édition : | 7 Editions | | Editeur : | Elsevier Inc. All rights rights reserved | | Année de publication : | 2021 | | Importance : | ix 337 Pages | | Présentation : | illustrator Black and whit Color | | Format : | 27.50 Cm | | ISBN/ISSN/EAN : | 978-0-323-90198-7 | | Langues : | Anglais (eng) Langues originales : Anglais (eng) | | Catégories : | 000 Généralités:006.31 Machine Learning
| | Mots-clés : | PRINCIPLES AND LABS FOR DEEP LEARNING | | Index. décimale : | 006.31 | | Résumé : | In 1943, Warren McCulloch and Walter Pitts introduced a computational model base on a threshold logic algorithm, Paving the way for the development of artificial intelligence (AI). The field of IA research was founded at the Dartmouth summer research project on artificial intelligence in 1956. In the nearly 80 years of development history, IA has experiences many ups and downs, especially the two "AI winter ," which are known as the periods of reduced funding and interesting AI research. |
PRINCIPLES AND LABS FOR DEEP LEARNING [texte imprimé] / Shih-Chia Huang, Auteur; Trung-Hieu Le, Auteur . - 7 Editions . - [S.l.] : Elsevier Inc. All rights rights reserved, 2021 . - ix 337 Pages : illustrator Black and whit Color ; 27.50 Cm. ISBN : 978-0-323-90198-7 Langues : Anglais ( eng) Langues originales : Anglais ( eng) | Catégories : | 000 Généralités:006.31 Machine Learning
| | Mots-clés : | PRINCIPLES AND LABS FOR DEEP LEARNING | | Index. décimale : | 006.31 | | Résumé : | In 1943, Warren McCulloch and Walter Pitts introduced a computational model base on a threshold logic algorithm, Paving the way for the development of artificial intelligence (AI). The field of IA research was founded at the Dartmouth summer research project on artificial intelligence in 1956. In the nearly 80 years of development history, IA has experiences many ups and downs, especially the two "AI winter ," which are known as the periods of reduced funding and interesting AI research. |
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