What is a batch in the context of neural networks?

In machine learning, a batch refers to a subset of the training data that is processed at once during the training process. Instead of processing the entire dataset in one go, it is often more efficient to break it down into smaller batches and feed them into the model sequentially. This allows the model to update its parameters more frequently, which can help it converge to a better solution faster.

For example, if you have a training set of 1000 examples, you might choose to process it in batches of 100. This means that the model will see the data in 10 passes, with each pass updating its parameters based on the loss calculated for the 100 examples in that batch. The size of the batch is a hyperparameter that can be tuned to find the optimal balance between training time and performance.