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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 3: Advanced Implementation of Computer Vision with TensorFlow

In this section, you will build on your understanding acquired from the previous sections and develop newer concepts and learn new techniques for action recognition and object detection. Throughout this section, you will learn different TensorFlow tools, such as TensorFlow Hub, TFRecord, and TensorBoard. You will also learn how to use TensorFlow to develop machine learning models for action recognition.

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

  • Understand the theory and develop an intuition behind various action recognition methods such as OpenPose, Stacked HourGlass, and PoseNet (chapter 9)
  • Analyze the OpenPose and Stacked HourGlass code to develop an understanding of how to build a very complex neural network and connect its different blocks. Hopefully, you can use this learning to...
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