Gradiensekben

Gradiensekben, also known as gradient descent, is an optimization algorithm used to minimize the cost function in machine learning and deep learning models. It is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to tweak parameters iteratively to minimize a given function to its local minimum.

The algorithm works by starting with an initial guess of the parameters and then iteratively moving in the direction of the negative gradient of the cost function. The gradient is a vector of partial derivatives with respect to each parameter, indicating the direction of the steepest ascent. By moving in the opposite direction, the algorithm aims to decrease the cost function.

There are different variants of gradient descent, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. Batch gradient descent computes the gradient using the entire dataset, while SGD uses a single data point at a time. Mini-batch gradient descent is a compromise between the two, using a subset of the data.

Gradient descent is widely used due to its simplicity and effectiveness. However, it can be slow to converge, especially for large datasets or complex models. To address this, various techniques such as momentum, adaptive learning rates, and second-order methods have been developed.

In summary, gradient descent is a fundamental optimization algorithm in machine learning and deep learning, used to minimize cost functions and improve model performance. Its variants and enhancements continue to be an active area of research.