Concrete Quality Prediction Using Deep Neural Networks

Deep learning is the recent hot trend in machine learning and Artificial Intelligence (AI). It's all about building advanced neural networks. By making multiple hidden layers work in a neural network model, we can work with complex nonlinear representations of data. We can create deep learning by using basic neural networks. Artificial neural networks (ANNs) are information-processing systems that try to simulate, within a computer system, the functioning of biological nervous systems that are made up of a large number of nerve cells, or neurons, connected to each other in a complex network. Each neuron is connected, on average, with tens or thousands of other neurons, with hundreds or billions of connections. Intelligent behavior emerges from the many interactions between these interconnected units. Deep learning has numerous use cases in real life. In this chapter, we'll address a prediction problem to estimate the quality of concrete.

The following are the topics that we'll be covering in this chapter:

• Basic concepts of ANNs
• Multilayer neural networks
• Implementing multilayer neural networks in Keras
• Keras deep neural network model
• Improving model performance by removing outliers 

By the end of the chapter, we'll have learned the basic concepts of prediction problems and discovered multilayer neural networks and how to implement them in the Keras environment. We'll also have learned the following:

• How to train, test, and deploy a model
• How to prepare data
• How to create the model using a real-world example
• How to evaluate the model's performance
• How to tune a model to improve the model performance