2. What are Generative Adversarial Networks (GANs) and how do they work?

Generative Adversarial Networks (GANs) are a type of neural network consisting of two models: a generator and a discriminator. They are trained together in a process of competition, where the generator tries to create realistic data and the discriminator evaluates whether the data is real or generated.

How GANs work:

1. Generator: Creates fake data that mimics real data.
2. Discriminator: Evaluates whether the data is real or fake.
3. Training: The generator and discriminator are trained together. The generator tries to fool the discriminator, while the discriminator tries to correctly classify the data.

Example applications of GANs:

• Generating realistic images or videos.
• Creating new artistic styles.
• Denoising images.

GANs are a powerful tool in the field of artificial intelligence, enabling the creation of realistic data based on training examples.