What are some common mistakes when choosing batch sizes in machine learning?

What are some common mistakes when choosing batch sizes in machine learning?

Some common mistakes include using a batch size that's too small, which can lead to noisy gradients, or too large, which can cause memory issues.

How does batch size affect training time?

Larger batch sizes can speed up training time per epoch but may require more epochs to converge, while smaller batches can slow down training.

Is there an optimal batch size for all models?

No, the optimal batch size varies depending on the model architecture, dataset size, and available hardware.

What are the trade-offs of using a larger batch size?

Larger batch sizes can improve training speed but may lead to poorer generalization and require more memory.

Can I adjust the batch size during training?

Yes, techniques like dynamic batch sizing allow you to adjust the batch size based on the training progress.