false,So increasing the batch size does not necessarily increase the capacity of the process.
The batch size is the number of units produced in a single production run. When there is a high setup cost, managers tend to raise the batch size to spread the cost across more units.
In practice, we recommend experimenting with smaller batch sizes first (typically 32 or 64), keeping in mind that small batch sizes need slow learning rates. To fully utilize the GPU's processing, the number of batch sizes should be a power of two.
When training neural networks, the batch size influences the accuracy of the error gradient estimate. The learning algorithm comes in three flavors: batch, stochastic, and minibatch gradient descent.
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