What are the advantages of using SGD in machine learning?

What are the advantages of using Stochastic Gradient Descent (SGD) in machine learning?

SGD is efficient for large datasets, helps escape local minima, and updates weights frequently, improving convergence.

Can you explain how SGD compares to other optimization methods?

Unlike batch gradient descent, SGD updates weights after each training example, making it faster and more scalable.

Are there any drawbacks to using SGD?

Yes, SGD can be noisy and may require careful tuning of the learning rate to ensure convergence.

How can I improve the performance of SGD?

You can use techniques like learning rate schedules, momentum, or mini-batch SGD to enhance performance.

Is SGD suitable for all types of machine learning models?

SGD is widely used, especially in deep learning, but may not be ideal for all models, depending on the problem.