Batch size

Introduction

Machine learning relies on a hyperparameter called batch size which indicates how many samples should be run before changing internal model parameters. This number can vary based on both machine memory capacity and the needs of each model and dataset.

Batch Size and Gradient Descent

Gradient descent relies on batch size as a key parameter that determines how many samples 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.

Batch Size and Stochastic Gradient Descent

[[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.

Explain Like I'm 5 (ELI5)

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.