How do different gradient descent algorithms compare?

How do different gradient descent algorithms compare?

There are several gradient descent algorithms, including standard gradient descent, stochastic gradient descent, and mini-batch gradient descent. Each has its own advantages and disadvantages depending on the problem at hand.

What are the main differences between stochastic and mini-batch gradient descent?

Stochastic gradient descent updates the model parameters using one training example at a time, which can lead to faster convergence but more noise. Mini-batch gradient descent, on the other hand, uses a small batch of examples, balancing the benefits of both speed and stability.

Which algorithm is generally preferred for large datasets?

For large datasets, mini-batch gradient descent is often preferred as it provides a good trade-off between convergence speed and stability, making it more efficient than standard gradient descent.

Can you explain the concept of learning rate in these algorithms?

The learning rate determines the size of the steps taken towards the minimum of the loss function. A small learning rate may lead to slow convergence, while a large learning rate can cause overshooting and divergence.