Gradient Descent is a method often used in data science (as well as math in general), to optimize the model that you ultimately build for analyzing new data. To sum it up, it finds the global minimum in our function, where the lowest value is the optimal value for us. As shown in this graphical representation, there are peaks and valleys in this key value. The valleys are the optimal points, so we seek to optimize the model using various forms of gradient descent to find the best of those valleys. So, as we say at Gradient Valley, it's nice in the Valley! The Gradient Valley.
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