What is gradient descent and how does it work?

What is gradient descent and how does it work?

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent.

Can you explain the steps involved in gradient descent?

Sure! The main steps are: 1) Initialize parameters, 2) Compute the gradient, 3) Update parameters, and 4) Repeat until convergence.

What are some common applications of gradient descent?

Gradient descent is widely used in training machine learning models, especially in neural networks and linear regression.

Are there different types of gradient descent?

Yes, there are several types, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent.

What are the advantages and disadvantages of using gradient descent?

Advantages include simplicity and efficiency for large datasets, while disadvantages can be local minima and slow convergence.