Implement a perceptron in python from scratch and use your implementation to show that the following boolean functions are

learnable:

i) the NOT function,

ii) the AND function,

iii) the OR function

vi) the NAND function.

That is, in each case, train a

perceptron model on the appropriate data/labels, depict each boolean function in the appropriate data/label space, and depict the decision

boundary showing that each of these functions is learnable by a perceptron. Finally, train a perceptron model on the XNOR function

and depict this function in the appropriate data/label space along with its decision boundary in order to confirm that the function is

not learnable by a perceptron. Now, pretend that no one else has ever seen these results before. Add some comments to your code that

explains these depictions.