Deep Learning: From Algorithmic Essence to Industrial Practice - Softcover

Wang PhD, Shuhao; Xu PhD, Gang

 
9780443439544: Deep Learning: From Algorithmic Essence to Industrial Practice
Softcover
ISBN 10: 0443439540 ISBN 13: 9780443439544
Publisher: Elsevier, 2025

Synopsis

Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. This book provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, this book covers the applications of these models in areas including image classification, object detection, and semantic segmentation. This book also considers advancements in deep reinforcement learning and generative adversarial networks making it suitable for graduate and senior undergraduate students with backgrounds in computer science, automation, electronics, communications, mathematics, and physics, as well as professional technical personnel who wish to work or are preparing to transition into the field of artificial intelligence
The code for book may be accessed by visiting the companion website: https://www.
elsevier.com/books-and-journals/book-companion/9780443439544

  • Provides in-depth explanations and practical code examples for the latest deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers
  • Examines theoretical concepts and the engineering practices required for deploying deep learning models in real-world scenarios
  • Covers the use of distributed systems for training and deploying models
  • Includes detailed case studies and applications of deep learning models in various domains including image classification, object detection, and semantic segmentation

"synopsis" may belong to another edition of this title.

About the Authors

Dr Shuhao Wang received his from Tsinghua University; he is a fellow at the Institute for Interdisciplinary Information Sciences at Tsinghua University and is currently the co-founder and CTO of ‘Thorough Future.’ He has conducted research on data science and artificial intelligence at Baidu, NovuMind, and JD.com. He holds over 20 national patents.

Dr. Wang has received several key accolades, such as the "30 New Generation Digital Economy Talents" award at the 2019 Wuzhen Internet Summit and the Year 2022 Fall Asia-Pacific Signal and Information Processing Association Industrial Distinguished Leaders award, and was named one of Alibaba Cloud's "Seeing New Power" figures of 2022

Dr Gang Xu gained his Ph.D. from Tsinghua University; he is currently an Assistant Professor at the Institute of Complex Systems Multiscale Research at Fudan University. Dr. Xu’s primary research focuses on the application of artificial intelligence in medical imaging and computational biology

From the Back Cover

Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. This book provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, this book covers the applications of these models in areas including image classification, object detection, and semantic segmentation. This book also considers advancements in deep reinforcement learning and generative adversarial networks making it suitable for graduate and senior undergraduate students with backgrounds in computer science, automation, electronics, communications, mathematics, and physics, as well as professional technical personnel who wish to work or are preparing to transition into the field of artificial intelligence
The code for book may be accessed by visiting the companion website: https://www.
elsevier.com/books-and-journals/book-companion/9780443439544

"About this title" may belong to another edition of this title.

Publisher
Elsevier
Publication date
2025
Language
English
ISBN 10 
0443439540
ISBN 13 
9780443439544
Binding
Paperback
Edition number
1
Number of pages
472