Gradient Descent is an optimization algorithm widely used in machine learning and numerical optimization. Its primary goal is to find the minimum of a function by iteratively moving towards the steepest or descending direction of the function's gradient. In machine learning, this function is typically associated with the error or cost of a model's predictions.
Here's a brief overview of how the Gradient Descent algorithm works:
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Initialization: The process starts with an initial guess for the parameter values. These parameters define the shape and behavior of the function that the algorithm is trying to optimize.
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Compute Gradient: The gradient represents the direction and magnitude of the steepest ascent of the function. It is calculated by finding the partial derivatives of the function with respect to each parameter.
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Update Parameters: The parameters are adjusted in the opposite direction of the gradient. This adjustment is proportional to the gradient and a user-defined learning rate. The learning rate controls the step size in each iteration.
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Convergence Check: Steps 2 and 3 are repeated until a stopping criterion is met. This criterion could be a predefined number of iterations, achieving a specific level of precision, or observing little change in the cost function.
Gradient Descent is a fundamental optimization technique used in training machine learning models, where the objective is to minimize the error or cost function. Its effectiveness lies in its simplicity, versatility, and ability to handle high-dimensional parameter spaces. However, choosing an appropriate learning rate is crucial to avoid convergence issues or slow convergence.