What is the difference between gradient descent and stochastic gradient descent?

What is the difference between gradient descent and stochastic gradient descent?

Gradient descent uses the entire dataset to compute the gradient, while stochastic gradient descent updates the gradient using one data point at a time.

Which one is better for training machine learning models?

It depends on the context. Stochastic gradient descent can converge faster and handle large datasets better, but it may be less stable.

Can you explain why stochastic gradient descent is faster?

Stochastic gradient descent updates weights more frequently, allowing it to escape local minima and find a solution faster.

Are there any drawbacks to using stochastic gradient descent?

Yes, it can introduce more noise in the updates, which may lead to fluctuations in the loss function during training.

What are some techniques to improve stochastic gradient descent?

Techniques like learning rate scheduling, momentum, and mini-batch gradient descent can help improve its performance.