Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	Februar 2025
Genre:	Ratgeber
ISBN:	9789365898408
EAN-Code:	9789365898408
Verlag:	BPB Publications
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 191 mm / D 23 mm
Gewicht:	795 gr
Seiten:	428
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

DESCRIPTION Mastering New Age Computer Vision is a comprehensive guide that explores the latest advancements in computer vision, a field that is enabling machines to not only see but also understand and interpret the visual world in increasingly sophisticated ways, guiding you from foundational concepts to practical applications. This book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks. By the time you have turned the final page of this book, you will be a confident computer vision practitioner, armed with a comprehensive grasp of core principles and the ability to apply cutting-edge techniques to solve real-world problems. You will be prepared to develop innovative solutions across a broad spectrum of computer vision challenges, actively contributing to the ongoing advancements in this dynamic field. WHAT YOU WILL LEARN ¿ Use PyTorch for both basic and advanced image processing. ¿ Build object detection models using CNNs and modern frameworks. ¿ Apply multi-task and multi-instance learning to complex datasets. ¿ Develop segmentation models, including panoptic segmentation. ¿ Improve feature representation with metric learning and bilinear pooling. ¿ Explore transformers and self-supervised learning for computer vision. WHO THIS BOOK IS FOR This book is for data scientists, AI practitioners, and researchers with a basic understanding of Python programming and ML concepts. Familiarity with deep learning frameworks like PyTorch and foundational knowledge of computer vision will help readers fully grasp the advanced techniques discussed.