notes.txt

#Perceptron usage:
In a Supervised Learning Classification problem, the perceptron tries to predict if a data point belongs to a class or another. 
A random dataset is generated and a diagonal line divides the distinction between both classes.

The perceptron receaves all the data points as input and returns a prediction for each one. 

The result is then a evaluated using a error function:
error = answer - prediction

if the answer was right, the error is 0 and the weights are correct for that input.

If the answer was not correct, the weights are updated, usign Gradient Descent: 
calculates a delta weight, to be used to update it towards a smaller error.

```
updated_weight = current_weight + delta_weight

delta_weight = error * input

so:
updated_weight = current_weight + error * input * learning_rate
```

#Perceptron algorithm:
1) Provide to the perceptron inputs with known ansers.
2) Ask the perceptron to guess a answer.
3) Compute the error.
4) Update weights according to the error.
5) Repeat from 1)


#Feed forward: processing the inputs

1) SUM of (each input multiplied by its weight)
2) ACTIVATION function, SIGN() converts results to +1 OR -1
3) Calculate the error