# Perceptron

The Perceptron is one of the simplest ANN architectures, invented in 1957 by Frank Rosenblatt. It is based on a slightly different artificial neuron called a threshold logic unit (TLU), or sometimes a linear threshold unit (LTU):

The inputs and output are now numbers (instead of binary on/off values) and each input connection is associated with a weight. The TLU computes a weighted sum of its inputs (z = w1 x1 + w2 x2 + ⋯ + wn xn = xT w), then applies a step function to that sum and outputs the result: hw(x) = step(z), where z = xT w.


#### Most common step function used are:
- Heaviside function
- sign function


- A single TLU can be used for simple linear binary classification. It computes a linear combination of the inputs and if the result exceeds a threshold, it outputs the positive class or else outputs the negative class (just like a Logistic Regression classifier or a linear SVM).

- Training a TLU in this case means finding the right values for w0, w1, and w2.

- A Perceptron is simply composed of a single layer of TLUs, with each TLU connected to all the inputs.

- When all the neurons in a layer are connected to every neuron in the previous layer (i.e., its input neurons), it is called a fully connected layer or a dense layer.

- To represent the fact that each input is sent to every TLU, it is common to draw special passthrough neurons called input neurons: they just output whatever input they are fed. All the input neurons form the input layer.

- Moreover, an extra bias feature is generally added (x0 = 1): it is typically represented using a special type of neuron called a bias neuron, which just outputs 1 all the time.

- A Perceptron with two inputs and three outputs is represented in Figure below. This Perceptron can classify instances simultaneously into three different binary classes, which makes it a multioutput classifier.