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Keras 2.x Projects

You're reading from   Keras 2.x Projects 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789536645
Length 394 pages
Edition 1st Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Keras FREE CHAPTER 2. Modeling Real Estate Using Regression Analysis 3. Heart Disease Classification with Neural Networks 4. Concrete Quality Prediction Using Deep Neural Networks 5. Fashion Article Recognition Using Convolutional Neural Networks 6. Movie Reviews Sentiment Analysis Using Recurrent Neural Networks 7. Stock Volatility Forecasting Using Long Short-Term Memory 8. Reconstruction of Handwritten Digit Images Using Autoencoders 9. Robot Control System Using Deep Reinforcement Learning 10. Reuters Newswire Topics Classifier in Keras 11. What is Next? 12. Other Books You May Enjoy

Implementing multilayer neural networks in Keras

We have gone through the theory of neural networks, we have learned about the activation functions and multilayer neural networks. We are now ready to implement our project on testing the quality of concrete using multilayer neural networks. Excited? Let's begin. Concrete is the most important material in civil engineering. Its compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. The test to calculate compressive strength is carried out on either a concrete cube or cylinder through the use of a compression testing machine (2,000 kilonewtons). The test is destructive and takes a long time, so the possibility of predicting the compressive strength takes on significant importance...

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