Batch size determines how many samples are processed before the model’s internal parameters are updated. A smaller batch size allows for quicker updates but might result in less accurate gradients, while a larger batch size provides more accurate gradients but requires more memory and time to process. Choosing the right batch size is crucial for balancing training speed and accuracy.
Batch Size Example
For example, in training a neural network, if a batch size of 32 is chosen, the model will process 32 data points before updating its parameters.