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

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

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

Why is batch size important for training models?

Batch size affects the training speed and the stability of the learning process. A smaller batch size can lead to more noisy updates, while a larger batch size provides a more accurate estimate of the gradient.

How do I choose the right batch size for my model?

Choosing the right batch size often involves experimentation. Common sizes range from 16 to 256, but it depends on your dataset and model architecture.

Can you explain how epochs and batch size interact during training?

Sure! During training, the model processes the dataset in batches for a number of epochs. Each epoch consists of multiple iterations, where each iteration processes one batch.