Beyond distances: Understanding statistical divergences in data
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Beyond distances: Understanding statistical divergences in data
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Statistical divergences measure how different two datasets are. In AI, these measurements are crucial for comparing and analyzing data. Imagine two groups of photos: cats and dogs. An AI must learn to distinguish cats from dogs. To do this, it uses statistical divergences to compare the characteristics of cat and dog photos and learn to differentiate them. AI algorithms, such as those used for image recognition or machine translation, rely on statistics to improve their accuracy. For example, by analyzing the divergences between correct and incorrect translations, the AI can learn to translate sentences better. This episode aims to explore the most commonly used divergences in data analysis, understand their implications, and examine their practical applications.
Read the original article here.