This tutorial describes the principal applications and concepts related to neural networks.
1. Model types
The most common learning tasks for neural networks are approximation, classification, and forecasting.
2. Data set
The data set contains the information available for creating the model.
3. Neural network
The neural network represents the approximation or classification model.
4. Training strategy
The training strategy fits the neural network with the data set.
5. Model selection
Model selection algorithms look for the neural network architecture with the best generalization capabilities.
6. Testing analysis
The testing analysis compares the outputs from the neural network against the targets in an independent set.
7. Model deployment
Model deployment is used to apply a model to predict new data.
