This segment introduces the concept of generative modeling using neural networks, shifting from the traditional autoencoder approach to a method that transforms random noise into samples from a data distribution. This innovative approach lays the groundwork for understanding Generative Adversarial Networks (GANs). The core concept of GANs is explained here, introducing the generator and discriminator networks and their opposing roles. The generator aims to create realistic samples, while the discriminator tries to distinguish between real and generated data, creating an adversarial relationship that drives the learning process. Generative Adversarial Networks (GANs) use two neural networks: a generator creating synthetic data (e.g., images) from random noise, and a discriminator classifying data as real or fake. They are trained adversarially: the generator aims to fool the discriminator, while the discriminator improves its classification. Careful training is crucial to avoid generator overfitting to the training data, enabling generation of novel, realistic samples. This segment details the training process of GANs, explaining how the discriminator and generator are trained iteratively. It also discusses potential failure modes, such as the generator memorizing the training data, highlighting the challenges and complexities involved in training these networks effectively.