Deep Learning for Image Recognition
Published on 30. October 2025
Book
Paperback/Softback
542 pages
978-0-443-43950-6 (ISBN)
Description
Deep Learning for Image Recognition provides a detailed explanation of the fundamental theories underpinning image recognition and code for recognition tasks in specific application scenarios. Readers can manipulate the existing code, thereby deepening their understanding. Chapters include project work enabling readers to apply the skills and knowledge gained from that section of the book. Projects are based on the accessible Pytorch framework, which is straightforward to learn and can be replicated and modified. Readers are presented with current research findings and up to date techniques in image recognition and deep learning.
More details
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Weight
870 gr
ISBN-13
978-0-443-43950-6 (9780443439506)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Peng Long MSc | Yu Song
Deep Learning for Image Recognition
E-Book
11/2025
Elsevier
€251.99
Available for download
Persons
Peng Long received the B.S. degree in Electronic science and technology in 2012 from Huazhong University of Science and Technology, and the M.S. degree in electronic circuit and system from university of Chinese Academy of Sciences, in 2015. He is currently CEO of YouSan Educational Technology Co., Ltd., and Most Valuable Professional of Alibaba Cloud and HUAWEI Cloud. He has published five books in China. His current research interests include pattern recognition, computer vision, and image processing Dr Yu Song obtained her PhD degree from the National Laboratory of Pattern Recognition at the Institute of Automation, Chinese Academy of Sciences, and a master's degree in automation from Tianjin University; she currently works in the Department of Industrial Design at the College of Mechanical Engineering, University of Science and Technology Beijing. Her research interests include artificial intelligence content generation, aesthetic computation, image collage, image scaling, and machine learning
Content
1. Fundamentals of Neural Networks and Convolutional Neural Networks
2. Fundamentals of Deep Learning Optimization
3. Data Process Methods in Deep Learning
4. Image Classification
5. Object Detection
6. Image Segmentation
7. Model Visualization
8. Model Compression
9. Model Deployment and Launch
2. Fundamentals of Deep Learning Optimization
3. Data Process Methods in Deep Learning
4. Image Classification
5. Object Detection
6. Image Segmentation
7. Model Visualization
8. Model Compression
9. Model Deployment and Launch