Chapter 8 - Generating Images Using GANs

1. Discriminative models learn to find the decision boundary that separates the classes in an optimal way, while generative models learn about the characteristics of each class. That is, discriminative models predict the labels conditioned on the input, , whereas generative models learn the joint probability distribution, .
2. The generator learns the distribution of images in our dataset. It learns the distribution of handwritten digits in our training set. We feed random noise to the generator and it will convert the random noise into a new handwritten digit similar to the one in our training set.
3. The goal of the discriminator is to perform a classification task. Given an image, it classifies it as real or fake; that is, whether the image is from the training set or the one generated by the generator.

4. The loss function for the discriminator...