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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

Application of TensorFlow Lite

TensorFlow Lite is the TensorFlow deep learning framework for inference on edge devices. Similar to OpenVINO, TensorFlow Lite has built-in pre-trained deep learning modules. Alternatively, an existing model can be converted into TensorFlow Lite format for on-device inference. Currently, TensorFlow Lite provides inference support for PCs with a built-in or external camera, Android devices, iOS devices, Raspberry Pis, and tiny microcontrollers. Visit https://www.tensorflow.org/lite for details on TensorFlow Lite.

The TensorFlow Lite converter takes a TensorFlow model and generates a FlatBuffer tflite file. A FlatBuffer file is an efficient cross-platform library that can be used to access binary serialized data without the need for parsing. Serialized data is usually a text string. Binary serialized data is binary data written in string format. For...

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