Generative Adversarial Networks (GANs) consist of two main components: a generator network and a discriminator network. The generator network generates synthetic data samples, while the discriminator network aims to distinguish between real and fake data. The two networks are trained simultaneously in an adversarial process, pushing each other to improve their performance. Here is a detailed explanation of the architecture and components of GANs.
Machine Learning Transformation involves a comprehensive and iterative process, combining data preparation, model development, evaluation, and deployment, with a strong focus on ethical and security considerations. The choice of techniques and technologies depends on the specific problem domain, data characteristics, and desired outcomes.