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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

Summary

In this chapter, we have designed and implemented an MLP that is capable of predicting the onset of diabetes with ~80% accuracy.

We first performed exploratory data analysis where we looked at the distribution of each variable, as well as the relationship between each variable and the target variable. We then performed data preprocessing to remove missing data and we also standardized our data such that each variable has a mean of 0 with unit standard deviation. Finally, we split our original data randomly into a training set, a validation set, and a testing set.

We then looked at the architecture of the MLP that we used, which consists of 2 hidden layers, with 32 nodes in the first hidden layer and 16 nodes in the second hidden layer. We then implemented this MLP in Keras using the sequential model, which allows us to stack layers on one another. We then trained our MLP...

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