What is the role of Stochastic Gradient Descent in deep learning?

What is the role of Stochastic Gradient Descent in deep learning?

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

How does SGD differ from regular Gradient Descent?

Unlike regular Gradient Descent, which uses the entire dataset to compute gradients, SGD updates weights using only a single or a few training examples, making it faster and more efficient.

What are the advantages of using SGD?

SGD can handle large datasets, converge faster, and escape local minima more effectively than traditional methods.

Are there any drawbacks to using SGD?

Yes, SGD can be noisy due to its reliance on individual samples, which may lead to fluctuations in the loss function during training.

How can I improve the performance of SGD?

You can improve SGD performance by using techniques like learning rate scheduling, momentum, or adaptive learning rate methods like Adam.