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Most machine learning models are built on just two numbers. In generative AI, we use probability distributions to help models "imagine" new data without memorizing every pixel. Think of a crowd of people. Instead of tracking every single person’s height, you only need to know the average and how much people vary from it. This is the Gaussian approach. By using just two parameters - the mean and the variance - models like VAEs and Diffusion can turn random noise into clear images. They don't stor
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Most machine learning models are built on just two numbers. In generative AI, we use probability distributions to help models "imagine" new data without memorizing every pixel. Think of a crowd of people. Instead of tracking every single person’s height, you only need to know the average and how much people vary from it. This is the Gaussian approach. By using just two parameters - the mean and the variance - models like VAEs and Diffusion can turn random noise into clear images. They don't stor
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