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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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