Gradient Descent Theorem

Gradient Descent is an optimization method used to find the lowest point in a landscape, which represents the best solution for a problem. Imagine a hiker walking down a mountain to reach the valley bottom; each step is guided by the steepest slope at the current position. The theorem guarantees that, by taking these steps repeatedly, the process will eventually arrive at the minimum point of a function, assuming certain conditions. This method is foundational in training machine learning models, helping them improve accuracy by minimizing errors through iterative updates.