1. Multi-Layer Perceptron
Ranga Rodrigo
February 8, 2014

2. Introduction Perceptron can only be a linear classifier.
We can have a network of neurons (perceptron-like structures) with an input layer, one or more hidden layers, and an output layer.
Each layer consists of many neurons and the output of a layer is fed as inputs to all neurons of the next layer.

3. N1xN2 weights
Layer L (output layer)
Layer 1
Layer 2
Layer k

4. Description of the MLP
In each layer, there are Nk elements (neurons), k = 1, ...,L, denoted as Nki ,
Each neuron may be a sigmoidal neuron.
There are N0 inputs, to which signals x1(t), ..., xN0(t), are applied, notated in the form of a vector
The output signal of i-th neuron in k-th layer is denoted as ,

5. Description of Parameters
Input vector for kth layer
Input for kth layer from the output of (k-1) layer (except for k=1, i = 0)
weights of neuron

6. i-th neuron in k-th layer

7. Forward Pass
Output signals of Lth layer
Output desired signals

8. Backpropagation
Weights update