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Convergence: Difference between revisions
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(Created page with "==Introduction== 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 outputs. Convergence refers to when model parameters stop changing or do so slowly after being trained. ==Defining Convergence== Convergence is typical...") |
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==Introduction== | ==Introduction== | ||
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 | [[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. | ||
==Defining Convergence== | ==Defining Convergence== | ||
Convergence is typically measured by monitoring the value of an objective function over time. As model parameters are adjusted, this number should decrease, signaling that it's getting better at its task. When this objective function stops decreasing completely or does so slowly, however, then we say the model has converged. Exact definitions of what constitutes convergence vary depending on specific models and tasks and may include factors like training set size, model complexity and learning rate used in optimization algorithm. | Convergence is typically measured by monitoring the value of an objective function over time. As model parameters are adjusted, this number should decrease, signaling that it's getting better at its task. When this objective function stops decreasing completely or does so slowly, however, then we say the model has converged. Exact definitions of what constitutes convergence vary depending on specific models and tasks and may include factors like [[training set]] size, model [[complexity]] and [[learning rate]] used in [[optimization algorithm]]. | ||
==Convergence Criteria== | ==Convergence Criteria== | ||