Activation Functions In Neural Network

Activation is a very important component of neural networks in intensive learning. This helps us to determine the output of a deep learning model, its accuracy, and the computational efficiency of training a model. They also have a major impact on how the neural network will change and what the convergence speed will be. In some cases, activation functions may also prevent neural networks from converging.

Therefore, understand the activation function, types of activation function and their importance and limitations in the description.

What is the activation function?

Activation functions help us to determine the output of a neural network. These types of functions are associated with each neuron in the neural network, and determine whether the input of each neuron is relevant to the prediction of the model.

The activation function also helps us to normalize the output of each neuron between 1 and or to the range between -1 and 1.

As we know, sometimes neural networks are trained on millions of data points, so the activation function must be efficient enough that it should be able to reduce computation time and improve performance.

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