Mathematics for Deep Learning (Part 7)

In the road so far, we have talked about MLP, CNN, and RNN architectures. These are discriminative models, that is models that can make predictions. Discriminative models essentially learn to estimate a conditional probability distribution p( x); that is, given a value, they try to predict the outcome based on what they learned about the probability distribution of x. Generative models are architectures of neural networks that learn the probability distribution of the data and learn how to generate data that seems to come from that probability distribution. Creating synthetic data is one use of generative models, but is not the only one.