Computer Vision Projects with PyTorch

Design and Develop Production-Grade Models

de

, ,

Éditeur :

Apress


Paru le : 2022-07-18



eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
62,11

Téléchargement immédiat
Dès validation de votre commande
Ajouter à ma liste d'envies
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

Description

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 applicationsDesign and develop production-grade computer vision projects for real-world industry problemsInterpret 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
Pages
346 pages
Collection
n.c
Parution
2022-07-18
Marque
Apress
EAN papier
9781484282724
EAN PDF
9781484282731

Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
34
Taille du fichier
10588 Ko
Prix
62,11 €
EAN EPUB
9781484282731

Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
34
Taille du fichier
21350 Ko
Prix
62,11 €

Suggestions personnalisées