What is gradient descent in AI?

Gradient descent is an optimization algorithm used in machine learning and AI to minimize a function. In the context of machine learning, this function is typically a loss function that measures the error of a model's predictions. By minimizing the loss function, gradient descent helps the model improve its predictions.

How does gradient descent work?

Gradient descent works by iteratively adjusting the parameters of the model in the direction that reduces the loss function the most. This direction is determined by the gradient of the loss function, hence the name "gradient descent".

The size of the steps taken in each iteration is determined by a parameter called the learning rate. Choosing the right learning rate is important, as a learning rate that is too large can cause the algorithm to overshoot the minimum, while a learning rate that is too small can make the algorithm slow to converge.

What are the different types of gradient descent?

There are several types of gradient descent, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent. These differ in how they compute the gradient and update the parameters. Batch gradient descent uses the entire dataset to compute the gradient, while stochastic gradient descent uses a single data point. Mini-batch gradient descent is a compromise between the two, using a small batch of data points.