Generative AI Tutorial – Slide 7
Understanding how generative models learn patterns and create new data from training distributions.
Understanding how generative models learn patterns and create new data from training distributions.
Slide 7 focuses on how generative AI models learn the underlying distribution of training data and then use what they learn to produce entirely new samples that resemble the original data. This concept is central to AI models such as GANs, VAEs, and modern diffusion models.
Models learn patterns, relationships, and structures from large datasets and approximate the probability distribution that generated the data.
Data is encoded into a compressed “idea space.” Models generate new content by sampling and transforming points in this latent space.
Using learned patterns, the model creates new outputs similar to—but not copies of—training examples.
Images, text, audio, or structured data.
Model learns correlations and distributions.
Random vector sampled as creative seed.
New, realistic synthetic data produced.
They transform complex data into a compact representation that can be manipulated to generate new content.
No, generative models approximate probability distributions, producing new variations rather than duplicates.
It visually explains how sampling from a learned distribution enables creativity and variation in outputs.
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