Gradient Descent is an optimization algorithm used to minimize a cost function by iteratively updating the parameters of a model in the direction of the negative gradient of the cost function. It is used to find the set of parameters that minimize the cost function and is a key algorithm in machine learning.
Gradient Descent is an optimization algorithm used in machine learning to find the minimum of a given function. It is an iterative process that starts with an initial guess and then gradually adjusts the parameters of the function to minimize the cost or error. The algorithm works by taking the derivative of the cost function with respect to the parameters and then updating the parameters in the opposite direction of the gradient. This process is repeated until the cost is minimized.
Gradient Descent is used in a variety of machine learning algorithms such as linear regression, logistic regression, neural networks, and support vector machines. It is also used in deep learning algorithms such as convolutional neural networks and recurrent neural networks.
The main advantage of Gradient Descent is that it is relatively easy to implement and can be used to solve a wide variety of problems. It is also relatively efficient and can be used to find the global minimum of a function.
The main disadvantage of Gradient Descent is that it can be slow to converge and can get stuck in local minima. It is also sensitive to the choice of the learning rate and can be difficult to tune.
Overall, Gradient Descent is a powerful optimization algorithm that is widely used in machine learning and deep learning. It is relatively easy to implement and can be used to solve a wide variety of problems. However, it can be slow to converge and can get stuck in local minima.