What is Stochastic Gradient Descent in Deep Learning?

What is Stochastic Gradient Descent in deep learning?

Stochastic Gradient Descent (SGD) is an optimization algorithm used to minimize the loss function in deep learning models.

How does SGD differ from regular gradient descent?

SGD updates the model parameters using only a single or a few training examples, while regular gradient descent uses the entire dataset.

What are the advantages of using SGD?

SGD can converge faster and is more efficient for large datasets, as it reduces the computation time per update.

Are there any drawbacks to using SGD?

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

What techniques can improve SGD performance?

Techniques like learning rate scheduling, momentum, and using mini-batches can significantly improve the performance of SGD.