Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python / Pattanayak Santanu

Public ISBD Titre : Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python Type de document : texte imprimé Auteurs : Pattanayak Santanu, Auteur Editeur : Apress Année de publication : 2023 Importance : 1 vol.(652 p.) Format : 24 cm ISBN/ISSN/EAN : 978-1-4842-8930-3 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé : This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE. Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications. Côte titre : Fs/25028 Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python [texte imprimé] / Pattanayak Santanu, Auteur . - Usa : Apress, 2023 . - 1 vol.(652 p.) ; 24 cm. ISBN : 978-1-4842-8930-3 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé : This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE. Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications. Côte titre : Fs/25028

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
Fs/25028	Fs/25028	livre	Bibliothéque des sciences	Anglais	Disponible Sorti jusqu'au 22/04/2024

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