What are the advantages of using stochastic gradient descent?

What are the advantages of using stochastic gradient descent?

Stochastic Gradient Descent (SGD) offers several advantages, including faster convergence, reduced memory usage, and the ability to escape local minima.

Can you explain how it achieves faster convergence?

SGD updates the model parameters more frequently than traditional methods, allowing it to make progress with each training example.

What about its effectiveness on large datasets?

SGD is particularly effective on large datasets because it processes one sample at a time, which reduces computation time and memory requirements.

Are there any downsides to using SGD?

Yes, SGD can be sensitive to the choice of learning rate and may require careful tuning to achieve optimal performance.

How does it compare to batch gradient descent?

Unlike batch gradient descent, which uses the entire dataset for each update, SGD uses only one sample, making it faster and more scalable.