Neural networks are the most important technique for machine learning and artificial intelligence. They are dramatically improving the state-of-the-art in energy, marketing, health, and many other domains.
In this tutorial, the most critical applications and concepts related to neural networks are described.
The most common learning tasks for neural networks are approximation and classification.
The data set contains the information available for creating the model.
The neural network represents the approximation or classification model.
The training strategy fits the neural network with the data set.
Model selection algorithms look for the neural network architecture with the best generalization capabilities.
The testing analysis compares the outputs from the neural network against the targets in an independent set.
Model deployment is used to apply a model for the prediction of new data.
In the following pages, we describe each of these concepts in detail.Model Types ›