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Modern Computer Vision with PyTorch

You're reading from   Modern Computer Vision with PyTorch A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781803231334
Length 746 pages
Edition 2nd Edition
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Authors (2):
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V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Yeshwanth Reddy Yeshwanth Reddy
Author Profile Icon Yeshwanth Reddy
Yeshwanth Reddy
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Toc

Table of Contents (26) Chapters Close

Preface 1. Section 1: Fundamentals of Deep Learning for Computer Vision
2. Artificial Neural Network Fundamentals FREE CHAPTER 3. PyTorch Fundamentals 4. Building a Deep Neural Network with PyTorch 5. Section 2: Object Classification and Detection
6. Introducing Convolutional Neural Networks 7. Transfer Learning for Image Classification 8. Practical Aspects of Image Classification 9. Basics of Object Detection 10. Advanced Object Detection 11. Image Segmentation 12. Applications of Object Detection and Segmentation 13. Section 3: Image Manipulation
14. Autoencoders and Image Manipulation 15. Image Generation Using GANs 16. Advanced GANs to Manipulate Images 17. Section 4: Combining Computer Vision with Other Techniques
18. Combining Computer Vision and Reinforcement Learning 19. Combining Computer Vision and NLP Techniques 20. Foundation Models in Computer Vision 21. Applications of Stable Diffusion 22. Moving a Model to Production 23. Other Books You May Enjoy
24. Index
Appendix

Index

Symbols

2D and 3D facial keypoint detection 186-188

A

action recognition

video, performing 385-389

training, on custom dataset 389-392

activation function 12-14

in code 18, 19

adversarial attack

on images 418-421

age estimation 189-199

agent 516

agent implementation, autonomous driving 544

CARLA environment, setting up 544

self-driving agent, training 547

anchor boxes 271, 272

API, image classifier

sdd.py file 667, 668

server.py 668, 669

server, running 669, 670

API, model quantization

latency benchmark function 670

API module and dependencies

installing 666

application programming interface (API)

basics 664, 665

creating 665

image classifier, serving 666, 667

predictions, on local server 665

servers 664, 665

artificial neural network (ANN) 3

building blocks 7-9

layers 7

autoencoders 396

convolutional autoencoders, implementing 404-408

t...

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