Convergence in Machine Learning

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a finite number in an infinite multiplication of numbers.
For example, a function may have a limit that tends to some specific value (lim 1/x tends to 0 as x-> ∞).

Accordingly, non-convergence is the absence of such a finite value. In mathematics, the terms are applied to a wide variety of entities: series, sequences, sums, and products of numbers.

There are also several types of convergence. For example, absolute convergence means that the sequence converges in all cases, and conditional convergence means that the series itself converges, but the series of its absolute values ​​does not.

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The concept of convergence in machine learning is slightly different from convergence in classical mathematics, although the essence is similar. The difference is that the term is applied not to numerical sequences, but to the optimization of an ML model. Convergence means that the difference between the actual results and those predicted by the model has become minimal.

Here's how it works: During training, the models use two parameters, among others:

learning rate, or Learning Rate, is a hyperparameter that shows how much the model needs to be changed after each error;
Errors, or Errors, are how much the model's results deviate from the truth.