Overview
Generative Adversarial Networks (GANs) are a type of deep learning model that can create new data. They consist of two parts: a generator that creates data and a discriminator that evaluates it. GANs are used in various applications, such as image generation and data augmentation. Understanding GANs helps students learn how machines can generate realistic content and improve their skills in AI and machine learning.
📚 Key Learning Objectives
- ✓ Explain the basic concept of GANs.
- ✓ Identify the roles of the generator and discriminator.
- ✓ Describe applications of GANs in real-world scenarios.
- ✓ Demonstrate how to train a simple GAN model.
- ✓ Evaluate the performance of a GAN using metrics.
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