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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 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 11. Other Books You May Enjoy

Summary

We have covered basic, yet useful models for training classification tasks. We saw a simple model for an MNIST dataset with both Keras and TensorFlow APIs. We also saw how to utilize TensorBoard for watching the training process. Then, we discussed state-of-the-art architectures with some specific applications. Several ways to increase the accuracy such as data augmentation, training on bottleneck layers, and fine-tuning a pre-trained model were also covered. Tips and tricks to train models for new models were also presented.

In the next chapter, we will see how to visualize the deep learning models. We will also deploy the trained models in this chapter for inference. We will also see how to use the trained layers for the application of an image search through an application. Then, we will understand the concept of autoencoders and use it for the dimensionality of...

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