These methods are used in machine learning.
The neural network activation function determines the output value of a neuron depending on the weighted sum of inputs. The selection of the activation function has a big impact on the neural network performance. Different model parts (layers) can use different activation functions.
In addition to all known activation functions, MQL5 also offers their derivatives. Function derivatives enable an efficient update of model parameters based on the error received in learning.
A neural network aims at finding an algorithm that minimizes the error in learning, for which the loss function is used. The value of the loss function indicates by how much the value predicted by the model deviates from the real one. Different loss functions are used depending on the problem. For example, Mean Squared Error (MSE) is used for regression problems, and Binary Cross-Entropy (BCE) is used for binary classification purposes.