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Deep Learning By Example

You're reading from   Deep Learning By Example A hands-on guide to implementing advanced machine learning algorithms and neural networks

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788399906
Length 450 pages
Edition 1st Edition
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Author (1):
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Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
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Toc

Table of Contents (18) Chapters Close

Preface 1. Data Science - A Birds' Eye View 2. Data Modeling in Action - The Titanic Example FREE CHAPTER 3. Feature Engineering and Model Complexity – The Titanic Example Revisited 4. Get Up and Running with TensorFlow 5. TensorFlow in Action - Some Basic Examples 6. Deep Feed-forward Neural Networks - Implementing Digit Classification 7. Introduction to Convolutional Neural Networks 8. Object Detection – CIFAR-10 Example 9. Object Detection – Transfer Learning with CNNs 10. Recurrent-Type Neural Networks - Language Modeling 11. Representation Learning - Implementing Word Embeddings 12. Neural Sentiment Analysis 13. Autoencoders – Feature Extraction and Denoising 14. Generative Adversarial Networks 15. Face Generation and Handling Missing Labels 16. Implementing Fish Recognition 17. Other Books You May Enjoy

Compressing the MNIST dataset

In this part, we'll build a simple autoencoder that can be used to compress the MNIST dataset. So we will feed the images of this dataset to the encoder part, which will try to learn a lower compressed representation for them; then we will try to construct the input images again in the decoder part.

The MNIST dataset

We will start the implementation by getting the MNIST dataset, using the helper functions of TensorFlow.

Let's import the necessary packages for this implementation:

%matplotlib inline

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist_dataset = input_data.read_data_sets('MNIST_data...
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