Gradient Descent Unpacked: From Valleys to Neural Networks

A concise, STEM-minded tour of gradient descent. We start with the valley-floor intuition, trace its 19th–20th century roots (Cauchy and Hadamard), and show how the method recasts equations as minimization problems. The episode dives into learning rate, local minima vs saddle points, and practical variants—SGD, momentum, Nesterov, and ADAM—before looking at real-world applications in training deep neural networks and other nonlinear optimization tasks.

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