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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
In [[machine learning]], [[Iteration]] is when a [[model]] [[update]]s it's [[parameters]] ([[weights]] and [[biases]]) one time during [[training]]. | In [[machine learning]], [[Iteration]] is when a [[model]] [[update]]s it's [[parameters]] ([[weights]] and [[biases]]) one time during [[training]]. The number of [example]]s the model processes in each iteration is determined by the [[hyperparameter]] [[batch size]]. If the batch size is 50, the model processes 50 examples before updating it's parameters - that is one iteration. | ||
Machine learning involves iteration, which is the process of optimizing parameters in a model to enable accurate [[prediction]]s on [[new data]]. This involves adjusting the parameters based on [[error]]s made during [[training]] on a [[training data|training]] [[dataset]]. By repeating this process multiple times, the model learns from its errors and improves its [[accuracy]]. | Machine learning involves iteration, which is the process of optimizing parameters in a model to enable accurate [[prediction]]s on [[new data]]. This involves adjusting the parameters based on [[error]]s made during [[training]] on a [[training data|training]] [[dataset]]. By repeating this process multiple times, the model learns from its errors and improves its [[accuracy]]. | ||
One common application of iteration in machine learning is gradient descent, an optimization algorithm designed to find the minimum cost function. In gradient descent, model parameters are updated based on | One common application of iteration in machine learning is [[gradient descent]], an [[optimization algorithm]] designed to find the minimum [[cost function]]. In gradient descent, the model's parameters are updated iteratively based on the [[gradient]] of the [[cost function]] with respect to the parameters. | ||
==Types of Iterations in Machine Learning== | ==Types of Iterations in Machine Learning== | ||