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Convergence: Difference between revisions
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{{see also|Machine learning terms}} | |||
==Introduction== | ==Introduction== | ||
[[Convergence]] is reached when [[loss]] values change very little or not at all with each [[iteration]]. [[Machine learning]] aims to [[train]] a [[model]] to perform a specific [[task]], such as [[recognizing images]] or predicting stock prices. The [[training]] process involves altering [[parameters]] in the model based on [[input]] data in order to minimize some [[objective function]] such as [[mean squared error]] between predicted and actual [[output]]s. Convergence refers to when model parameters stop changing or do so slowly after being trained and additional training will not improve the model. | [[Convergence]] is reached when [[loss]] values change very little or not at all with each [[iteration]]. [[Machine learning]] aims to [[train]] a [[model]] to perform a specific [[task]], such as [[recognizing images]] or predicting stock prices. The [[training]] process involves altering [[parameters]] in the model based on [[input]] data in order to minimize some [[objective function]] such as [[mean squared error]] between predicted and actual [[output]]s. Convergence refers to when model parameters stop changing or do so slowly after being trained and additional training will not improve the model. | ||
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Convergence can also be affected by the optimization algorithm used to adjust the model's parameters. Some algorithms, like [[gradient descent]], may get stuck in local minima or saddle points while others - like [[stochastic gradient descent]] - jump out of these regions and explore other areas in the parameter space. Furthermore, the learning rate used within the optimization algorithm also plays a role in affecting convergence as it provides feedback to the computer during optimization. | Convergence can also be affected by the optimization algorithm used to adjust the model's parameters. Some algorithms, like [[gradient descent]], may get stuck in local minima or saddle points while others - like [[stochastic gradient descent]] - jump out of these regions and explore other areas in the parameter space. Furthermore, the learning rate used within the optimization algorithm also plays a role in affecting convergence as it provides feedback to the computer during optimization. | ||
==Explain Like I'm 5 (ELI5)== | |||
Machine learning convergence can be likened to a chef who keeps tasting their food until it's perfectly prepared. | |||
Imagine you're making soup, and you keep adding ingredients and tasting until it tastes exactly how desired. Convergence is like this but with a machine learning model instead of soup. | |||
When creating a machine learning model, we aim for maximum accuracy. To achieve this, we need to train the model on numerous examples and adjust its parameters until it makes accurate predictions. Convergence occurs when all these corrections have been made enough times that no further adjustments are necessary. | |||
Like when you keep tasting a soup and adding ingredients until it's just right, and then there is no need for further adjustments. The model acts like that soup, and convergence occurs when it has learned everything necessary and can make accurate predictions without needing further adjustments. | |||
[[Category:Terms]] [[Category:Machine learning terms]] | |||