Activation Functions
An activation function in a neural network defines how the weighted sum of the input is transformed
into an output from a node or nodes in a layer of the network.
https://machinelearningmastery.com/choose-an-activation-function-for-deeplearning/#:~:text=An%20activation%20function%20in%20a,a%20layer%20of%20the%20network.
An activation function is a mathematical function used in a neural network to determine the output
of a neuron. Activation functions are used to transform the inputs into an output signal and can
range from simple linear functions to complex non-linear functions. Activation functions are an
important part of neural networks and help the network learn patterns and generalize data. Types of
activation functions include sigmoid, ReLU, tanh, and softmax.
