Gradient Descent

Gradient descent is like a treasure hunter trying to find the bottom of a valley. Imagine you're blindfolded and placed on a mountain. Your goal is to reach the lowest point. You can only feel the slope using your hands or feet. To start, you take a small step downhill in the steepest direction you can sense. You repeat this process, taking small steps and adjusting your direction each time. Gradually, you'll find yourself descending closer to the valley floor. The Gradient descent algorithm in Machine Learning works the same way. The valley represents the error or loss in our model's predictions. The goal is to minimize this error. The slope at each point corresponds to the gradient, which tells us the direction of steepest descent. 

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