What is gradient descent in machine learning?

What is gradient descent in machine learning?

Gradient descent is an optimization algorithm used to minimize the loss function in machine learning models by iteratively adjusting parameters.

How does gradient descent work step by step?

It works by calculating the gradient of the loss function, then updating the model parameters in the opposite direction of the gradient.

What are the different types of gradient descent?

The main types are batch gradient descent, stochastic gradient descent, and mini-batch gradient descent, each with different data handling.

What are the advantages and disadvantages of using gradient descent?

Advantages include simplicity and efficiency for large datasets, while disadvantages can be getting stuck in local minima and sensitivity to learning rates.

Can you explain the learning rate in gradient descent?

The learning rate determines the size of the steps taken towards the minimum; a small rate may take longer to converge, while a large rate can overshoot.