Peng Long – författare

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 learningDelves 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 deploymentHighlights current research outcomes of multiple new technologies in the field of computer visionExamples and case studies are included

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

This book comprehensively and systematically introduces the theory of generative adversarial networks and its applications in image and voice processing. This book consists of 12 chapters, of which the first four chapters present basic knowledge, including the principle of GAN, optimization objectives, training methods and evaluation indicators. The last eight chapters introduce the applications of GAN in various vertical fields, covering image generation, video generation, image translation, face image editing, image quality improvement, general image editing, anti-attack, voice signal processing and other fields. Through reading this book, readers will thoroughly understand the principles of GAN, various GAN model designs, and learn to apply GAN for most vision and voice tasks. This book is suitable for the junior researchers, students or industrial practitioners in related areas.