Finding meaning in generative adversarial networks

Learn to build your own generative adversarial network using TensorFlow, with this free interactive tutorial, "General adversarial networks for beginners." If you ask a child to draw a cat, you'll learn more about the child than you will about cats. In the same way, asking neural networks to generate images helps us see how they reason about the information they're given. It's often difficult to interpret neural networks--that is, to relate their functioning to human intuition--and generative algorithms offer a way to make neural nets explain themselves. Neural networks are most commonly implemented as classifiers--models that are able to distinguish, say, an image of a cat from an image of a dog, or a stop sign from a fire hydrant.