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Deep Learning for Computer Vision

You're reading from Deep Learning for Computer Vision Expert techniques to train advanced neural networks using TensorFlow and Keras

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781788295628

Length 310 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Computer Vision

Author (1):

Shanmugamani

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Table of Contents (17) Chapters

Title Page

Copyright and Credits

Packt Upsell

Foreword

Contributors

Preface

1. Getting Started FREE CHAPTER

2. Image Classification

3. Image Retrieval

4. Object Detection

5. Semantic Segmentation

6. Similarity Learning

7. Image Captioning

8. Generative Models

9. Video Classification

10. Deployment

1. Other Books You May Enjoy

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Chapter 10. Deployment

In this chapter, we will learn how to deploy the trained model in the various platforms for maximum throughput and minimum latency. We will understand performance on various hardware such as a GPU and a CPU. We will follow the steps of deploying TensorFlow in platforms such as Amazon Web Services, Google Cloud Platform, and mobile platforms such as Android, iOS, and Tegra.

We will cover the following topics in this chapter:

Understanding the factors affecting the performance of the deep learning model training and inference
Improving the performance through various methods
Seeing the benchmarks of various hardware and learning the steps to tweak them for maximum performance
Using various cloud platforms for deployment
Using various mobile platforms for deployment

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Authors (1)

Shanmugamani

Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

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