What are the advantages of mini-batch gradient descent?

What are the advantages of mini-batch gradient descent?

Mini-batch gradient descent combines the benefits of both batch and stochastic gradient descent. It improves convergence speed and reduces variance.

Can you explain how it improves convergence speed?

Sure! By using mini-batches, the algorithm can update weights more frequently than in batch gradient descent, leading to faster convergence.

What about the reduction in variance?

Using mini-batches helps to smooth out the noise in the gradient estimates, which can lead to more stable and reliable updates.

Are there any downsides to using mini-batch gradient descent?

Yes, one downside is that choosing the right batch size can be tricky. Too small may lead to noisy updates, while too large may slow down training.