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Explores the Gaussian conditional model for linear regression and the properties of Gaussian data, illustrated with the example of kidney stone treatment comparison.
Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.
Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
