What is the difference between stochastic and batch gradient descent?

Can you explain the difference between stochastic and batch gradient descent?

Sure! Stochastic gradient descent (SGD) updates the model using one training example at a time, while batch gradient descent uses the entire dataset.

What are the advantages of using stochastic gradient descent?

SGD can converge faster and is more efficient for large datasets, as it updates weights more frequently.

Are there any downsides to using stochastic gradient descent?

Yes, SGD can be noisy and may lead to fluctuations in the loss function, making it harder to converge to the minimum.

When should I use batch gradient descent instead?

Batch gradient descent is preferable when you have a smaller dataset or when you want more stable convergence.