MLP (Multi-Layer Perceptron)

Training of MLP

MLP learns weights and biases to minimize the difference between actual response and neural network predictions.

\[{\rm H}_k^i=\sum_{j=1}^nw_j\cdot{\rm H}_{k-1}^j+\theta\cdot f_{act.}({\rm H}_k^i)\]

\(w_1,\,w_2,\,\cdots,\,w_n\) : weight, θ : bias, \(f_{act.}\) : activation function

Advantages

- Expresses the nonlinear relationship of input and output variables well.
- Able to create a meta model regardless of a lot of data.

Disdvantages

- User know-how dependent variables → the number of hidden layer / neuron, the activation function form.
- Time consuming for training to create.

Source : https://www.pidotech.com