Multilayer perceptrons (MLP) are one of the basic architectures of neural networks. At a very high level, they consist of three components:
- The input layer: A vector of features.
- The hidden layers: Each hidden layer consists of N neurons.
- The output layer: Output of the network; depends on the task (regression/classification).
The input of each hidden layer is first transformed linearly (multiplication by weights and adding the bias term) and then non-linearly (by applying activation functions such as ReLU). Thanks to the non-linear activation, the network is able to model complex, non-linear relationships between the features and the target.
A multilayer perceptron contains multiple hidden layers (also called dense layers or fully connected layers) stacked against each other. The following diagram presents a network with a...
