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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 criteria - signs oman telegram data 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.