What is the impact of batch size in deep learning?

What is the impact of batch size in deep learning?

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

How does a larger batch size affect the training process?

A larger batch size can speed up training but may lead to less generalization and overfitting. It also requires more memory.

What about smaller batch sizes?

Smaller batch sizes can improve generalization and allow the model to learn more nuanced patterns, but they may slow down training.

Is there an optimal batch size for all models?

There isn't a one-size-fits-all optimal batch size; it often depends on the specific model architecture and dataset.