Iteration: Difference between revisions

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 In [[machine learning]], [[Iteration]] is when a [[model]] [[update]]s it's [[parameters]] ([[weights]] and [[biases]]) one time during [[training]].
-==The Importance of Iteration in Machine Learning==
+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 predictions on new data. This involves adjusting the parameters based on errors made during training on a 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 how well their parameters fit within a certain gradient.