Compute derivatives and gradients and implement the optimization algorithms that actually train machine-learning models — gradient descent and its modern variants (SGD, Momentum, Adam) — while reasoning about convexity, learning rates, and convergence. The capstone trains a real model end-to-end with from-scratch optimizers; the Master's extension reads, implements, and reproduces the Adam paper.
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