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.- 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
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
