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One of the key techniques used in generative AI is the **Generative Adversarial Network (GAN)**, which consists of two neural networks: a generator and a discriminator. The generator creates new content, while the discriminator evaluates the authenticity of that content. Through an iterative process of competition and refinement, the generator learns to produce increasingly realistic and convincing outputs. Another important technique is the use of **Variational Autoencoders (VAEs)**, which learn to encode data into a lower-dimensional latent space and then decode it to generate new samples. These techniques, along with others like transformers and recurrent neural networks, form the foundation of modern generative AI.
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├── models.py # Data models (Pydantic models)
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