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Modern Computer Vision with PyTorch

You're reading from   Modern Computer Vision with PyTorch Explore deep learning concepts and implement over 50 real-world image applications

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781839213472
Length 824 pages
Edition 1st Edition
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Authors (2):
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Yeshwanth Reddy Yeshwanth Reddy
Author Profile Icon Yeshwanth Reddy
Yeshwanth Reddy
V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
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Toc

Table of Contents (25) 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. Training with Minimal Data Points 19. Combining Computer Vision and NLP Techniques 20. Combining Computer Vision and Reinforcement Learning 21. Moving a Model to Production 22. Using OpenCV Utilities for Image Analysis 23. Other Books You May Enjoy Appendix

Moving the API to the cloud

So far, we have learned about making predictions on a local server (http://127.0.0.1 is a URL of the local server that cannot be accessed on the web) – so, only the owner of the local machine can use the model. In this section, we will learn about moving this model to the cloud so that anyone can predict using an image.

In general, companies deploy services in redundant machines to ensure reliability and there is little control over the hardware provided by the cloud provider. It is not convenient to keep track of all folders and their code, or copy-paste all the code, then install all the dependencies, ensuring the code works as expected on the new environment, and forward ports on all the cloud machines. There are too many steps to be followed for the same code on every new machine. Repeating these steps is a waste of time for the developer and such a process is highly prone to mistakes.

We would rather install one package that has everything than...

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