Deep Learning for Image Recognition

by Peng Long MSc, Yu Song PhD

Overview

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.

- A comprehensive introduction to the technology and applications of image recognition based on deep learning

- Delves into the core concepts of image recognition, from pre-processing to modelling and algorithm implementation. This is supported by clear descriptions of neural networks, including convolutional neural network principles, model visualization, model compression and model deployment

- Highlights current research outcomes of multiple new technologies in the field of computer vision

- Examples and case studies are included

Product Details

ISBN-13:	9780443439513
Publisher:	Elsevier Science
Publication date:	11/03/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	560
File size:	102 MB
Note:	This product may take a few minutes to download.

About the Author

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 processingDr 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

Table of Contents

1. Fundamentals of Neural Networks and Convolutional Neural Networks2. Fundamentals of Deep Learning Optimization3. Data Process Methods in Deep Learning4. Image Classification5. Object Detection6. Image Segmentation7. Model Visualization8. Model Compression9. Model Deployment and Launch