What is Gradient Descent

Gradient Descent is a widely used optimization algorithm in machine learning and statistics. Its primary objective is to minimize a function iteratively, such as a cost or loss function.

The core idea of Gradient Descent is to compute the gradient of the function at a given point, which indicates the direction of the steepest descent. By updating the parameters in this direction, the algorithm gradually approaches the optimal solution.

In typical scenarios, Gradient Descent is employed in various applications, including deep learning, linear regression, and logistic regression. With the rise of big data and computational capabilities, variants of Gradient Descent, such as Stochastic Gradient Descent, Mini-batch Gradient Descent, and Momentum, have been introduced to improve efficiency and convergence speed.

Looking ahead, Gradient Descent will likely continue to evolve, integrating other optimization techniques to tackle more complex problems. However, despite its effectiveness, it has certain drawbacks, such as the potential to get stuck in local minima and sensitivity to the learning rate. Therefore, careful adjustments and choices are necessary when applying this method.