Batch Size is a term used in machine learning, particularly in training neural networks. It refers to the number of training examples used in one iteration of model training.
Imagine you’re a teacher checking the homework of your students. You could check and give feedback on each student’s work one by one, or you could collect all the homework, check them all, and then give feedback to the entire class. Or, you could do something in-between, like check five students’ homework at a time. The number of homework you check at a time is like the batch size in machine learning.
In machine learning, we often have a large dataset to train our model on. Instead of feeding the entire dataset or just one example at a time, we often feed a batch of data at each step of training.
The batch size can affect the speed and efficiency of the training process. If the batch size is too small, the training might be slow and less efficient. If it’s too large, it could overwhelm the computer’s memory and also lead to less accurate models. The optimal batch size often depends on the specific problem and the hardware you’re using, and it’s usually found through trial and error.
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