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...
Keras 2.x Projects
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Keras 2.x Projects
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Overview of this book
Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas.
To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more.
By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.
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Table of Contents (13 chapters)
Preface
Free Chapter
Getting Started with Keras
Modeling Real Estate Using Regression Analysis
Heart Disease Classification with Neural Networks
Concrete Quality Prediction Using Deep Neural Networks
Fashion Article Recognition Using Convolutional Neural Networks
Movie Reviews Sentiment Analysis Using Recurrent Neural Networks
Stock Volatility Forecasting Using Long Short-Term Memory
Reconstruction of Handwritten Digit Images Using Autoencoders
Robot Control System Using Deep Reinforcement Learning
Reuters Newswire Topics Classifier in Keras
What is Next?
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