Recap In the last post we discussed how a perceptron could ‘learn’ the best set of weights and biases to classify some data. We discussed that what ‘best’ really means, in the context of minimising some error function. Finally, we motivated the need to move to a continuous model of the…

Recap In the last post, we introduced the concept of a perceptron and how it can be used to model a linear classifier. A perceptron takes in n input features, x, and multiplies each by a corresponding weight, w, adds on a bias term and finally applies an activation function to…

Motivation The general task of any model is to take in some known quantities and predict some unknown quantity we care about. Whether it be predicting the price of a house, given its location and the number of bedrooms, or predicting the probability of passing a test, given previous mock test…