This module, we will continue exploring the perceptron. We'll delve into stochastic gradient descent (SGD), a fundamental optimization technique that enables the perceptron, and other models, to learn from data by iteratively updating the model's parameters to minimize errors. Afterward, we will look at kernel methods. These techniques can separate two sets of points in more complicated ways, drawing inspiration from how the human eye works.

This module, we will move to exploring fully-connected networks. These networks are sophisticated models that can be thought of as a perceptron sitting on top of another perceptron, continuing in such a fashion. Each layer in a fully-connected network takes inputs from the layer below it, working to separate data points (such as the red and the blue scattered points) a little better than the one before it, and then passes it on to the next layer.

We will finish this course by looking at backpropagation, which is an algorithm to train neural networks to find the best set of weights that minimize error on the data. Backpropagation applies the chain rule from calculus to efficiently calculate gradients of the loss function with respect to the weights, enabling the model to update its weights in the opposite direction of the gradient. We'll discuss the importance of typical datasets consisting of images, sentences, and sounds, and how neural networks can learn from the spatial regularities present in such data.