Overview
Generative Adversarial Networks (GANs) are a type of deep learning model that can generate new data. They consist of two neural networks, a generator and a discriminator, that work against each other. The generator creates fake data, while the discriminator tries to tell the difference between real and fake data. This competition helps both networks improve, leading to high-quality data generation.
📚 Key Learning Objectives
- ✓ Explain the basic concept of GANs.
- ✓ Identify the roles of the generator and discriminator.
- ✓ Describe how GANs are trained.
- ✓ Discuss applications of GANs in real-world scenarios.
- ✓ Evaluate the strengths and weaknesses of GANs.
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