What are some effective techniques for Stochastic Gradient Descent (SGD) optimization?

What are some effective techniques for Stochastic Gradient Descent (SGD) optimization?

Some effective techniques for SGD optimization include learning rate scheduling, momentum, and mini-batch training.

Can you explain how learning rate scheduling works?

Learning rate scheduling adjusts the learning rate during training, often starting high and decreasing it to improve convergence.

What is the role of momentum in SGD?

Momentum helps accelerate SGD in the relevant direction and dampens oscillations, leading to faster convergence.

How does mini-batch training improve the optimization process?

Mini-batch training reduces the variance of the gradient estimate, leading to more stable updates and faster training.

Are there any common pitfalls to avoid when using SGD?

Yes, common pitfalls include choosing a poor learning rate, not using momentum, and overfitting due to small batch sizes.