Design and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch.
The book begins with the fundamentals of computer vision: convolutional neural nets, RESNET, YOLO, data augmentation, and other regularization techniques used in the industry. And then it gives you a quick overview of the PyTorch libraries used in the book. After that, it takes you through the implementation of image classification problems, object detection techniques, and transfer learning while training and running inference. The book covers image segmentation and an anomaly detection model. And it discusses the fundamentals of video processing for computer vision tasks putting images into videos. The book concludes with an explanation of the complete model building process for deep learning frameworks using optimized techniques with highlights on model AI explainability.
After reading this book, you will be able to build your own computer vision projects using transfer learning and PyTorch. What You Will Learn

Solve problems in computer vision with PyTorch.
Implement transfer learning and perform image classification, object detection, image segmentation, and other computer vision applications
Design and develop production-grade computer vision projects for real-world industry problems
Interpret computer vision models and solve business problems

Who This Book Is For
Data scientists and machine learning engineers interested in building computer vision projects and solving business problems

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Yes, you can access Computer Vision Projects with PyTorch by Akshay Kulkarni,Adarsha Shivananda,Nitin Ranjan Sharma in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Apress

Year

2022

ISBN

9781484282731

Topic

Mathematics

Subtopic

Probability & Statistics

Index

Mathematics

Cover
Front Matter
1. The Building Blocks of Computer Vision
2. Image Classification
3. Building an Object Detection Model
4. Building an Image Segmentation Model
5. Image-Based Search and Recommendation System
6. Pose Estimation
7. Image Anomaly Detection
8. Image Super-Resolution
9. Video Analytics
10. Explainable AI for Computer Vision
Back Matter