What are the effects of batch size in machine learning?

What are the effects of batch size in machine learning?

Batch size refers to the number of training examples utilized in one iteration. It can significantly impact training speed and model performance.

How does a smaller batch size affect training?

A smaller batch size often leads to more noisy gradient estimates, which can help escape local minima but may also slow down convergence.

And what about larger batch sizes?

Larger batch sizes provide more accurate gradient estimates and can speed up training, but they may lead to poorer generalization.

Is there an optimal batch size?

The optimal batch size can vary depending on the dataset and model architecture, but it's often a balance between speed and performance.

Can batch size affect overfitting?

Yes, batch size can influence overfitting. Smaller batches may help regularize the model, while larger batches can lead to overfitting.