What is Activation in Machine Learning?

An activation function helps an artificial neural network to learn complicated patterns in data. Many compare activation function with the way our brains work. It’s because activation performs the same purpose (it is responsible for determining what is to be fired to the next phase at the conclusion of the process). It takes the preceding cell’s output signal and transforms it into a format that may be used as input to the following cell.

  • Nonlinearity, squashing function and transfer function are all the different terms used to describe activation function.

Importance of Activation function in Neural network

Alright, but why do I need to use it? The activation function chosen has a significant influence on the neural network’s performance and effectiveness, and various activation functions could well be employed in different regions of the model.

  • The activation function determines how successfully the network model perform and learn from the data

Since networks are designed to employ the same activation function for all nodes in a layer, the activation function is applied within or after the internal processing of each node in the network.

  • In neural networks, there are many distinct types of activation functions, however, probably only a few are utilized in general.

There are three sorts of layers in a network:

  • input levels that accept raw domain data
  • hidden layers that take input from one layer transfer the output to another layer,
  • output layers that generate predictions.

The activation function is usually the same for all concealed layers. Depending on the sort of prediction required by the model, the output layer will generally employ a different activation function than the hidden layers.

Activation functions are usually distinguishable, which means that for a given set of input values, the first-order derivative can be computed. This is necessary because neural networks are usually trained using the backpropagation of error technique, which requires the derivative of prediction error to update the model’s weights.

Additionally, they also aid to limit the value of the neuron’s output to a certain limit as required. In the case of really deep neural networks with millions of parameters, this number can reach as high as a factor of a million. As a result, there will be difficulties with the computation. Examples of activation functions that output specified values for varying input values are the softmax function that will be discussed in our next article when we talk about different types of activation functions.

  • What makes an activation function so valuable is its capacity to introduce non-linearity into a neural network. Activation functions are placed between the linear layers to provide the model more power, a greater level of depth to learn non-linear patterns.

Neural networks must be able to estimate non-linear relationships between input characteristics and output labels if they are to compute truly interesting things. The non-linearity of the feature-to-ground-truth-label mapping increases in complexity with respect to learning anything new.

Without an activation function, a neural network cannot complete the tasks we want it to.