"What do R2, laboratory error analysis, ensemble learning, meta-analysis, and financial portfolio risk all have in common? The answer is that they all depend on a fundamental principle of statistics that is not as widely known as it should be. Once this principle is understood, a lot of stuff starts to make more sense."
Read it!A journey starting from the standard formulation of linear regression, moving on to the probabilistic approach, and then progressing to Bayesian linear regression.
Read it!Developing an intuitive understanding of the key feature in the architecture of transformer neural networks.
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