Gradient Descent: Linear Regression

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Linear and Weighted Regression: Optimal Parameters and Local Solutions

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Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

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Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.

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