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Neural Network Projects with Python

You're reading from   Neural Network Projects with Python The ultimate guide to using Python to explore the true power of neural networks through six projects

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789138900
Length 308 pages
Edition 1st Edition
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Author (1):
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James Loy James Loy
Author Profile Icon James Loy
James Loy
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Table of Contents (10) Chapters Close

Preface 1. Machine Learning and Neural Networks 101 FREE CHAPTER 2. Predicting Diabetes with Multilayer Perceptrons 3. Predicting Taxi Fares with Deep Feedforward Networks 4. Cats Versus Dogs - Image Classification Using CNNs 5. Removing Noise from Images Using Autoencoders 6. Sentiment Analysis of Movie Reviews Using LSTM 7. Implementing a Facial Recognition System with Neural Networks 8. What's Next? 9. Other Books You May Enjoy

The MNIST handwritten digits dataset

One of the datasets that we'll use for this chapter is the MNIST handwritten digits dataset. The MNIST dataset contains 70,000 samples of handwritten digits, each of size 28 x 28 pixels. Each sample contains only one digit within the image, and all samples are labeled.

The MNIST dataset is provided directly in Keras, and we can import it by simply running the following code:

from keras.datasets import mnist

training_set, testing_set = mnist.load_data()
X_train, y_train = training_set
X_test, y_test = testing_set

Let's plot out each of the digits to better visualize our data. The following code snippet uses matplotlib to plot the data:

from matplotlib import pyplot as plt
fig, ((ax1, ax2, ax3, ax4, ax5), (ax6, ax7, ax8, ax9, ax10)) = plt.subplots(2, 5, figsize=(10,5))

for idx, ax in enumerate([ax1,ax2,ax3,ax4,ax5, ax6,ax7,ax8,ax9,ax10...
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