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Bayesian Updating in Machine Learning: Making Sense of Training Data
A deep dive into conditional probability, priors, likelihood, and Bayesian updating in ML.
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Conditional Probability, Priors, Likelihood, and Bayes' Rule: The Foundations
A deep dive into conditional probability, priors, likelihood, and Bayesian updating.
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Expectation in Reinforcement Learning: The Way It Finally Made Sense to Me
A personal journey through understanding why expectation is everywhere in RL, from value functions to policy gradients, and why it's not just a math trick
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What Does "Likelihood of the Training Data" Actually Mean?
An explanation of likelihood and log-likelihood in machine learning detailing what's really being measured and why probability matters for data that already exists