Learning patterns with neural networks

The core building blocks for the deep learning algorithms we will examine are Neural Networks, a predictive model that simulates the way cells inside the brain fire impulses to transmit signals. By combining individual contributions from many inputs (for example, the many columns we might have in a tabular dataset, words in a document, or pixels in an image), the network integrates signals to predict an output of interest (whether it is price, click through rate, or some other response). Fitting this sort of model to data therefore involves determining the best parameters of the neuron to perform this mapping from input data to output variable.

Some common features of the deep learning models we will discuss in this chapter are the large number of parameters we can tune and the complexity of the models themselves. Whereas the regression models we have seen so far required us to determine the optimal value of ~50 coefficients, in deep learning models...