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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

Denoising autoencoders

Another interesting application of autoencoders is image denoising. Image noise is defined as a random variations of brightness in an image. Image noise may originate from the sensors of digital cameras. Although digital cameras these days are capable of capturing high quality images, image noise may still occur, especially in low light conditions.

Denoising images has been a challenge for researchers for many years. Early methods include applying some sort of image filter (that is, mean averaging filter, where the pixel value is replaced with the average pixel value of its neighbors) over the image. However, such methods can sometimes fall short and the effects can be less than ideal.

A few years ago, researchers discovered that we can train autoencoders for image denoising. The idea is simple. Instead of using the same input and output when training conventional...

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