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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

Training the MNIST model in TensorFlow

In this section, we will learn about the Modified National Institute of Standards and Technology (MNIST) database data and build a simple classification model. The objective of this section is to learn the general framework for deep learning and use TensorFlow for the same. First, we will build a perceptron or logistic regression model. Then, we will train a CNN to achieve better accuracy. We will also see how TensorBoard helps visualize the training process and understand the parameters. 

The MNIST datasets

The MNIST data has handwritten digits from 0–9 with 60,000 images for training and 10,000 images for testing. This database is widely used to try algorithms...

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