Gradient Descent

Gradient Descent is an optimization algorithm used to minimize the cost function in machine learning models.

Gradient Descent is widely used in training machine learning models, particularly in neural networks and linear regression. It works by iteratively adjusting the model parameters to minimize the cost function, which measures the difference between the predicted and actual values. The algorithm calculates the gradient of the cost function with respect to the model parameters and updates the parameters in the opposite direction of the gradient.

For example, in linear regression, the cost function is typically the mean squared error between the predicted and actual values. Gradient Descent starts with an initial set of parameters and iteratively updates them by subtracting a fraction of the gradient. This fraction is known as the learning rate. The process continues until the cost function converges to a minimum value or a specified number of iterations is reached.

There are several variants of Gradient Descent, including Batch Gradient Descent, Stochastic Gradient Descent (SGD), and Mini-Batch Gradient Descent. Batch Gradient Descent uses the entire dataset to compute the gradient, while SGD uses a single data point at each iteration. Mini-Batch Gradient Descent strikes a balance by using a small subset of the data.

In summary, Gradient Descent is a fundamental optimization technique in machine learning that iteratively adjusts model parameters to minimize the cost function. It is important to know because it is the backbone of many machine learning algorithms and helps improve model accuracy.