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Mastering Computer Vision with TensorFlow 2.x

You're reading from   Mastering Computer Vision with TensorFlow 2.x Build advanced computer vision applications using machine learning and deep learning techniques

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781838827069
Length 430 pages
Edition 1st Edition
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Author (1):
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Krishnendu Kar Krishnendu Kar
Author Profile Icon Krishnendu Kar
Krishnendu Kar
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Introduction to Computer Vision and Neural Networks
2. Computer Vision and TensorFlow Fundamentals FREE CHAPTER 3. Content Recognition Using Local Binary Patterns 4. Facial Detection Using OpenCV and CNN 5. Deep Learning on Images 6. Section 2: Advanced Concepts of Computer Vision with TensorFlow
7. Neural Network Architecture and Models 8. Visual Search Using Transfer Learning 9. Object Detection Using YOLO 10. Semantic Segmentation and Neural Style Transfer 11. Section 3: Advanced Implementation of Computer Vision with TensorFlow
12. Action Recognition Using Multitask Deep Learning 13. Object Detection Using R-CNN, SSD, and R-FCN 14. Section 4: TensorFlow Implementation at the Edge and on the Cloud
15. Deep Learning on Edge Devices with CPU/GPU Optimization 16. Cloud Computing Platform for Computer Vision 17. Other Books You May Enjoy

Summary

In this chapter, you learned how to send image data to the cloud platform for analysis. In previous chapters, we learned how to perform training on a local PC, but in this chapter, you have learned how to perform the same task using a cloud platform and also, how to trigger training in multiple instances, using Google Cloud Shell for distributed training.

This chapter has included many examples and links and you will gain more knowledge by reviewing those links and performing the exercises. We then learned how to send images to a cloud platform for instance analysis. The image content analysis was extended to perform a visual search in the cloud platform. We also learned how to use all three cloud platforms—GCP, AWS, and Azure. Remember to make sure to shut down your project when you have completed your task, even though you are not training, to stop incurring...

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