What is the impact of batch size in machine learning?

What is the impact of batch size in machine learning?

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

How does a smaller batch size affect training?

A smaller batch size can lead to more updates per epoch, which may help the model converge faster but can also introduce more noise.

What about larger batch sizes?

Larger batch sizes can stabilize the training process and make better use of hardware, but they may lead to poorer generalization.

Is there an optimal batch size?

The optimal batch size often depends on the specific dataset and model architecture, so it's best to experiment with different sizes.