Perceptron
The “perceptron” is a simple algorithm that, given an input vector x of m values (x1, x2,..., and xm), often called input features or simply features, outputs either a 1 (“yes”) or a 0 (“no”). Mathematically, we define a function:
Where w is a vector of weights, 
is the dot product 
, and b is the bias. If you remember elementary geometry, wx + b defines a boundary hyperplane that changes position according to the values assigned to w and b. Note that a hyperplane is a subspace whose dimension is one fewer than that of its ambient space. See (Figure 1.2) for an example:
Figure 1.2: An example of a hyperplane
In other words, this is a very simple but effective algorithm! For example, given three input features, the amounts of red, green, and blue in a color, the perceptron could try to decide whether the color is “white” or not.
Note that the perceptron cannot express a “maybe”...
