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Building Agentic AI Systems
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Generative AI is an exciting domain of AI that deals with the generation of new, synthetic data by learning patterns from existing datasets, aiming to generate outputs that share similar statistical properties and characteristics with the training data. Here is a broad overview of some of the most prominent types of generative models: VAEs, GANs, and autoregressive models.
One of the most popular generative models is the VAE. The core idea behind VAE consists of learning a probabilistic mapping between data and a latent space, and vice versa. This means learning how to convert real data into a simplified representation (such as a compressed form) and then back again into data that looks real. VAEs are designed to ensure a high likelihood of the data while preserving a well-structured latent space to enable the generation of new data samples similar to the training data. Some of the most common flavors of VAE are as follows: