What are gradient descent optimization techniques?

What are gradient descent optimization techniques?

Gradient descent is an iterative optimization algorithm used to minimize a function by adjusting parameters in the direction of the steepest descent.

Can you explain how it works in simple terms?

Sure! Imagine you're on a hill and want to find the lowest point. You take small steps downhill, adjusting your direction based on the steepness of the slope.

What are the different types of gradient descent?

There are several types, including Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent, each with its own advantages.

When should I use each type?

Use Batch Gradient Descent for small datasets, Stochastic for large datasets, and Mini-batch for a balance between the two.