What is the difference between batch size and epoch in machine learning?

What is the difference between batch size and epoch in machine learning?

Batch size refers to the number of training examples utilized in one iteration, while an epoch is one complete pass through the entire training dataset.

How does changing the batch size affect training?

Changing the batch size can impact the training speed and model performance. A smaller batch size often leads to more updates and can help the model generalize better, while a larger batch size can speed up training but may lead to overfitting.

Is there an ideal batch size I should use?

The ideal batch size can vary depending on the dataset and model architecture. Common sizes range from 16 to 256, but it's best to experiment to find what works best for your specific case.

What happens if I set the batch size too high?

If the batch size is too high, it may lead to memory issues, slower convergence, and potentially worse generalization performance.