Neural Networks

Internal

• Machine Learning

Individual Neuron

Individual neurons are computational units that read input features, represented as an unidimensional vector x1 ... xn in the diagram below, and calculate the hypothesis function as output. Note that x0 is not part of the feature vector, but it represents a bias value for the unit.

A common option is to use a logistic function as hypothesis, thus the unit is referred to as a logistic unit with a sigmoid (logistic) activation function.

The θ vector represents the model's parameters (model's weights). For a multi-layer neural network, the model parameters are collected in matrices named Θ, which will be describe below.

The x0 input node is called the bias unit, and it is optional. When provided, it is equal with 1.

Multi-Layer Neural Network

When the output