Gradient Descent
Gradient descent is an optimization method used to minimize a function, often applied in machine learning. Imagine you are at the top of a hill and want to find the lowest point in the valley. Gradient descent involves taking small steps in the direction that slopes downward, guided by the steepness of the hill (the gradient). Each step gets you closer to the low point, which represents the best solution to a problem, like accurately predicting data. By repeatedly adjusting your position based on the slope, you gradually converge to the optimal solution.
Additional Insights
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Gradient descent is an optimization technique used to minimize a function, which often represents error in machine learning models. Imagine you're trying to find the lowest point in a hilly landscape while blindfolded. You can only feel the slope beneath your feet. By taking small steps downhill in the direction of the steepest decline, you gradually reach the lowest point. Similarly, in gradient descent, algorithms adjust parameters incrementally to reduce errors, ultimately improving the model's accuracy. This method efficiently finds the best solution to complex problems by iteratively refining estimates based on feedback from previous steps.