Back in Chapter 1, Introduction to Machine Learning, we briefly introduced the basic model of a neuron and the perceptron learning algorithm (PLA). Here, in this chapter, we will now revisit and expand the concept and show how that is coded in Python. We will begin with the basic definition.
The visual concept
The perceptron is an analogy of a human-inspired information processing unit, originally conceived by F. Rosenblatt and depicted in Figure 5.1 (Rosenblatt, F. (1958)). In the model, the input is represented with the vector 
, the activation of the neuron is given by the function 
, and the output is 
. The parameters of the neuron are 
and 
:
The trainable parameters of a perceptron are 
, and they are unknown. Thus, we can use input training data 
to determine these parameters using the PLA. From Figure 5.1, 
multiplies 
, then 
multiplies 
, and 
is multiplied by 1; all these products are added and then passed...
