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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

Digital images as neural network input

Recall that in previous chapters, we made the distinction that neural networks require numerical inputs. We saw how we can encode categorical features, such as day of week, into numerical features using one-hot encoding. How then do we use an image as input for our neural network? Well, the short answer is that all digital images are numerical in nature!

To see why this is so, consider a 28 x 28 image of a handwritten digit 3, as shown in the following screenshot. Let's assume for now that the image is in grayscale (black and white). If we look at the intensity of each pixel that makes up the image, we can see that certain pixels are totally white, while some pixels are gray and black. In a computer, white pixels are represented with the value 0 and black pixels are represented with a value of 255. Everything else in between white and...

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