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Generative AI refers to a class of artificial intelligence systems that have the ability to generate new content, whether it’s text, images, audio, or other forms of data. These systems are designed to understand patterns and structures within existing data and use that understanding to create novel content. Here’s a deep dive into generative AI, covering key concepts, techniques, and applications:
I. Key Concepts:
1. Generative Models:
Generative models are a type of machine learning model that learns the underlying probability distribution of the training data. The model can then generate new samples that resemble the training data. Popular generative models include:
- Variational Autoencoders (VAEs): VAEs learn a probabilistic mapping between input data and a latent space. They are trained to generate new data points by sampling from this latent space.
- Generative Adversarial Networks (GANs): GANs consist of a generator and a discriminator. The generator creates samples, and the discriminator distinguishes between real and generated samples. The two networks are trained adversarially, leading to high-quality generated content.
- Autoregressive Models: These models, such as LSTMs and Transformers, generate sequences of data one…
