How does batching work in machine learning?

Can you explain what batching means in machine learning?

Batching in machine learning refers to the process of dividing the training dataset into smaller subsets, or batches.

Why is batching important during training?

Batching helps in optimizing memory usage and can speed up the training process by allowing parallel processing.

How do I choose the right batch size?

The right batch size often depends on the dataset size, model architecture, and available computational resources.

What are the effects of using a small vs. large batch size?

Smaller batch sizes can lead to more noisy gradient estimates, while larger batch sizes provide more stable estimates but may require more memory.