Deep Learning and Practice with MindSpore

Lei Chen

ISBN 10: 9811622329 / ISBN 13: 9789811622328

Editore: Springer Nature Singapore, Springer Nature Singapore, 2021

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.

Codice articolo 9789811622328

Dati bibliografici

• Titolo: Deep Learning and Practice with MindSpore
• Editore: Springer Nature Singapore, Springer Nature Singapore
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 9811622329
• ISBN 13: 9789811622328
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 28 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 28 millimetri di profondità
• Peso: 781 grammi

Descrizione articolo

Riassunto

This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.

Informazioni sull?autore

Chen Lei is a Chair Professor of the Department of Computer Science and Engineering and the Director of the Big Data Institute at Hong Kong University of Science and Technology (HKUST). His research focuses on data-driven AI, human-powered machine learning, knowledge graphs, and data mining on social media. He has published more than 400 papers in world-renowned journals and conference proceedings and won the 2015 SIGMOD Test of Time Award. Currently, he serves as the Editor-in-Chief of the VLDB 2019 Journal, the Associate Editor-in-Chief of the IEEE TKDE Journal, and an executive member of the VLDB Endowment. He is also IEEE Fellow and ACM Distinguished Scientist.