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The principle is the same: the algorithm converges if the quality metric reaches the desired value or stabilizes and stops changing actively.
This convergence more accurately indicates how well the model solves a problem for end users, such as classifying or extracting data.
Parameter convergence. This type of convergence describes how the weights or other parameters of the model change at each training step.

The algorithm converges when the parameters stop changing or the changes become very small.
Convergence by parameters allows us to evaluate the stability of the model. If the weights hardly change, it means that the model has adapted to the data and will continue to produce stable results.
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How to understand that convergence has been achieved
The moment of convergence is determined by several lithuania telegram data criteria - signs that show that the optimal value has been achieved. Here are what these signs are.

Minimizing the loss function. The loss, or error, function gradually decreases during training. Convergence is achieved when the algorithm has achieved a minimum or close to minimum value of the function. This can be:

global minimum - the smallest and therefore best of all possible values ​​of the error function;
local minimum — the smallest value on a certain interval. Reaching a local minimum gives a less optimal but stable result, especially when it comes to a complex nonlinear model.
Slowing improvements. During training, the target metric, such as the error function or the accuracy rate, constantly changes. If it changes significantly, training should be continued. If the change becomes very insignificant at each step, then the moment of convergence has been reached.

Parameter stabilization. After each training step, the model updates the weights — the coefficients it uses to calculate the result. If the values ​​of these parameters stop changing significantly between iterations, this means that the model has converged.

Convergence is achieved at the moment when the difference between real and calculated data becomes minimal.
On the graph, convergence looks something like this. It is achieved at the moment when the difference between real and calculated data becomes minimal. Source
Once the function converges, the training algorithm stops to avoid overfitting the model.