Python: Deeper Insights into Machine Learning

Logistic units

As a starting point, we use the idea of a logistic unit over the simplified model of a neuron. It consists of a set of inputs and outputs and an activation function. This activation function is essentially performing a calculation on the set of inputs, and subsequently giving an output. Here, we set the activation function to the sigmoid that we used for logistic regression in the previous chapter:

We have Two input units, x₁ and x₂ and a bias unit, x₀, that is set to one. These are fed into a hypothesis function that uses the sigmoid logistic function and a weight vector, w, which parameterizes the hypothesis function. The feature ...