Gradient Descent

Gradient Descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of the steepest descent, as defined by the negative of the gradient. It is commonly employed in machine learning and statistics for training models, especially in contexts where the objective is to minimize the loss function. The process begins with an initial guess for the parameters of the model, and the algorithm calculates the gradient of the loss function with respect to the parameters. The parameters are then updated by taking a step proportional to the negative gradient, scaled by a learning rate, which determines the size of the steps taken. This procedure repeats until convergence is reached, meaning that changes in the loss function or parameters become negligible. Variants of gradient descent include batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent, each with different mechanisms for updating parameters. Gradient Descent is essential for training various algorithms, particularly in deep learning and neural networks, as it helps find optimal parameters that reduce prediction errors.