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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

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788295628
Length 310 pages
Edition 1st Edition
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Author (1):
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Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started 2. Image Classification FREE CHAPTER 3. Image Retrieval 4. Object Detection 5. Semantic Segmentation 6. Similarity Learning 7. Image Captioning 8. Generative Models 9. Video Classification 10. Deployment 11. Other Books You May Enjoy

Summary

In this chapter, we covered the basics of similarity learning. We studied algorithms such as metric learning, Siamese networks, and FaceNet. We also covered loss functions such as contrastive loss and triplet loss. Two different domains, ranking and recommendation, were also covered. Finally, the step-by-step walkthrough of face identification was covered by understanding several steps including detection, fiducial points detections, and similarity scoring. 

In the next chapter, we will understand Recurrent Neural Networks and their use in Natural Language Processing problems. Later, we will use language models combined with image models for the captioning of images. We will visit several algorithms for this problem and see an implementation of two different types of data. 

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