Batch size: Difference between revisions

Gradient descent relies on batch size as a key parameter that determines how many examples are used in each iteration of the algorithm. Gradient descent works by iteratively updating model parameters in order to minimize cost function costs by computing gradients between them and batch size. Each iteration uses samples that make up this gradient calculation.

[[Stochastic Gradient Descent]] (SGD) is a variant of gradient descent that uses only one sample to update model parameters each iteration. Compared to batch gradient descent, which utilizes all samples for this task, SGD requires only one sample for parameter updating - making it both computationally more efficient but also prone to noise in gradient estimates.

The batch size is a number that instructs the computer how many examples to consider before it changes its interpretation of data patterns. It's like learning your times tables: start with simpler ones (small numbers) and then progress onto more difficult ones (bigger figures). This helps the machine comprehend data more clearly and rapidly.

[[Category:Terms]] [[Category:Machine learning terms]]