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Description

An engaging and accessible introduction to deep learning perfect for students and professionals
In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples.
Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning toolsComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architecturesPractical discussions of recurrent neural networks and non-supervised approaches to deep learningFulsome treatments of generative adversarial networks as well as deep Bayesian neural networks
Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.
Pages
416 pages
Collection
n.c
Parution
2024-07-08
Marque
Wiley
EAN papier
9781119861867
EAN PDF
9781119861874
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
416
Taille du fichier
14838 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
89,46 €
EAN EPUB
9781119861881
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
416
Taille du fichier
31365 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
89,46 €

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