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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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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

Section 4: TensorFlow Implementation at the Edge and on the Cloud

In this section, you will use all your knowledge of computer vision and CNN acquired so far to package, optimize, and deploy a model in edge devices to solve real-life computer vision problems. Training large datasets in local machines takes time, so you will learn how to package your data and upload to containers in the cloud and then initiate training. You'll also see how to overcome some common bugs to complete your training and generate models successfully.

By the end of this section, you will be able to do the following:

  • Understand how edge devices use various hardware acceleration and software optimization techniques to make inferences based on a neural network model with minimum delay (chapter 11)
  • Understand the theory of the MobileNet model, as this is often deployed in edge devices due to its speed...
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