How do I choose the right variant of gradient descent?

What factors should I consider when choosing a gradient descent variant?

You should consider factors like the size of your dataset, the complexity of your model, and the convergence speed you need.

Can you explain the differences between batch, stochastic, and mini-batch gradient descent?

Sure! Batch gradient descent uses the entire dataset, stochastic uses one sample at a time, and mini-batch is a compromise, using a small subset.

Which variant is best for large datasets?

For large datasets, mini-batch gradient descent is often preferred as it balances efficiency and convergence speed.

Are there any specific scenarios where stochastic gradient descent is advantageous?

Yes, stochastic gradient descent can be advantageous in online learning scenarios or when the dataset is too large to fit in memory.