Activation Function

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Activation functions are critical for artificial intelligence models as they define the output of a model, its accuracy, and its computational efficiency. They introduce non-linearity into the neural network, allowing it to learn more complex functions and hence patterns from the data.

There are various types of activation functions such as:

  1. Sigmoid Function: It is a smooth, S-shaped function that maps any input value into a range between 0 and 1. It is often used in the output layer for binary classification problems.
  2. ReLU (Rectified Linear Unit): It outputs the input directly if it is positive, otherwise, it outputs zero. ReLU is often used in the hidden layers of neural networks.
  3. Softmax Function: It calculates the probabilities distribution of the event over ‘n’ different events.
  4. Tanh (hyperbolic tangent): It’s similar to sigmoid but better. It scales the output to range between -1 and 1.
  5. Leaky ReLU: It’s a version of ReLU that allows small negative values when the input is less than zero, solving the problem of dying neurons in ReLU.

These functions help control the output and prevent it from going too high or too low. Each type of activation function has its advantages and use cases, and the choice of which to use depends on the specific requirements of the neural network.