What are the best practices for determining batch size in machine learning?

What are the best practices for determining batch size in machine learning?

When determining batch size, consider factors like dataset size, model complexity, and available memory.

How does batch size affect training time and model performance?

Larger batch sizes can speed up training but may lead to poorer generalization. Smaller batches often yield better performance.

Is there a recommended range for batch sizes?

A common range is between 32 and 256, but it can vary based on the specific model and dataset.

What tools can help me experiment with different batch sizes?

You can use frameworks like TensorFlow or PyTorch, which allow easy adjustments to batch sizes during training.