The sigmoid function and its applications in machine learning is that Sigmoid Function is one that act as a kind of an activation function in regards to machine learning which is said to be used to input any form of non-linearity in a machine learning model.
One can say that it decides the type of value that one can pass as output and what cannot be pass.
Why use a sigmoid function instead of, say, a simple Z-score for data normalization?
The main reason that one can say is the key reason why people make use of the sigmoid function is due to the fact that it is one that exists between (0 to 1).
So, it is one that is used for models where a person can have to be able to know the probability as an output.
Therefore, The sigmoid function and its applications in machine learning is that Sigmoid Function is one that act as a kind of an activation function in regards to machine learning which is said to be used to input any form of non-linearity in a machine learning model.
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