What does slow convergence mean?

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Often, gradient optimization methods, such as gradient descent, are used when training models. In this case, the cause of divergence may be too large a step of the gradient - a vector that shows how quickly the values ​​of the function change. And if the gradient of the loss function is zero, the model will never converge - this means that the value does not change.

The most difficult situation is when the algorithm does not converge due to architectural errors. This sometimes happens with deep neural networks. For example, divergence can occur due to the lack of batch normalization or an inappropriate activation function.

Due to architectural errors, function gradients can decay qatar telegram data or explode during training. When decaying, gradients approach zero, and training slows down significantly. When exploding, the gradient grows sharply, and further training becomes impossible.

Divergence is dealt with in different ways depending on the cause. To avoid it, an ML specialist should:

carefully prepare data before training;
correctly select loss functions depending on the task at hand;
use various optimization methods;
add regularization - additional conditions when updating weights;
closely monitor the learning process and weights updating.
Then, even if something goes wrong, it will be quickly noticed and the situation will be corrected.

Divergence is not the only possible problem. It also happens that the algorithm converges, but does so very slowly. To reach the optimal point, a huge number of iterations are needed. That is, a lot of time and computing power.