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Modern computer vision with PyTorch : = a practical roadmap from deep learning fundamentals to advanced applications and Generative AI /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Modern computer vision with PyTorch :/ V. Kishore Ayyadevara, Yeshwanth Reddy.
Reminder of title:	a practical roadmap from deep learning fundamentals to advanced applications and Generative AI /
remainder title:	Practical roadmap from deep learning fundamentals to advanced applications and Generative AI
Author:	Ayyadevara, V. Kishore.
other author:	Reddy, Yeshwanth.
Published:	Birmingham, UK :Packt Publishing Ltd., : c2024.,
Description:	xxiv, 716 p. :ill. ; : 24 cm.;
Notes:	Includes index.
Subject:	Python (Computer program language) -
ISBN:	9781803231334 (pbk.) :

Modern computer vision with PyTorch : = a practical roadmap from deep learning fundamentals to advanced applications and Generative AI /
Ayyadevara, V. Kishore.

Modern computer vision with PyTorch :a practical roadmap from deep learning fundamentals to advanced applications and Generative AI /Practical roadmap from deep learning fundamentals to advanced applications and Generative AIV. Kishore Ayyadevara, Yeshwanth Reddy. - 2nd ed. - Birmingham, UK :Packt Publishing Ltd.,c2024. - xxiv, 716 p. :ill. ;24 cm. - Expert insight. - Expert insight..

Includes index.

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You'll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You'll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you'll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you'll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.

ISBN: 9781803231334 (pbk.) :NT1987Subjects--Topical Terms:

566246
Python (Computer program language)

LC Class. No.: TA1634

Dewey Class. No.: 006.3/7 / A989

Modern computer vision with PyTorch : = a practical roadmap from deep learning fundamentals to advanced applications and Generative AI /
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245 1 0 $a Modern computer vision with PyTorch : $b a practical roadmap from deep learning fundamentals to advanced applications and Generative AI / $c V. Kishore Ayyadevara, Yeshwanth Reddy.
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490 1 $a Expert insight
500 $a Includes index.
520 # $a Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You'll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You'll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you'll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you'll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.
650 # 0 $a Python (Computer program language) $3 566246
650 # 0 $a Computer vision. $3 561800
650 # 0 $a Machine learning. $3 561253
700 1 # $a Reddy, Yeshwanth. $3 1458371
830 0 $a Expert insight. $3 1406965