Multilayer Perceptrons (MLPs) are the buiding blocks of neural network. They are comprised of one or more layers of neurons. Data is fed to the input layer, there may be one or more hidden layers providing levels of abstraction, and predictions are made on the output layer, also called the visible layer.
MLPs are suitable for:
- classification prediction problems where inputs are assigned a class or label
- regression prediction problems where a real-valued quantity is predicted given a set of inputs
- Tabular datasets
Read this article to understand the intuition behind when to use multilayer perceptrons
This is a companion discussion topic for the original entry at http://iq.opengenus.org/when-to-use-multilayer-perceptrons-mlp/