An artificial neural network's activation function is a function that is introduced to help the network learn complex patterns in the data. When compared to a neuron-based model seen in our brains, the activation function is responsible for determining what is to be fired to the next neuron at the end of the process. In an ANN, an activation function performs the same job. It takes the preceding cell's output signal and turns it into a format that may be used as input to the next cell.
Here, x0 and x1 are the inputs. w1 is the weight and a is the activation function.
The activation function introduces non-linearity into the neural network, allowing it to learn more complex functions. The neural network would only be able to learn a function that is a linear combination of its input data if it didn't have the Activation function.
The activation function converts inputs to outputs. The activation function is in charge of determining whether or not a neuron should be stimulated. It arrives at a decision by calculating the weighted total and then adds bias. The activation function's main goal is to introduce non-linearity into a neuron's output.
