what is gradient descent in machine learning

Gradient descent is an optimization algorithm commonly used in machine learning to train models and neural networks. It works by iteratively adjusting the parameters of the model to minimize a cost function, which measures the error between predicted and actual values. The algorithm uses the gradient of the cost function to determine the direction and magnitude of parameter updates, gradually moving towards the local or global minimum of the function. There are different types of gradient descent, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent, each with its own advantages and trade-offs. The algorithm is particularly useful for minimizing the cost or loss function in machine learning and deep learning applications.