Activation function is a crucial component that takes the input in the form of a weighted sum and bias, transforms it, and produces an output that is used for predictions or to feed the next layer. By introducing non-linearity into the output of a neuron, it allows us to model complex, non-linear patterns that are observable in real-world data.
The activation function is about deciding, given the inputs into the node, whether it should be ‘activated’ or not, hence influencing the weighted sum of the inputs, passed through the function, whether to progress in the network. It’s akin to the operations of neuron cells in the human brain, that either fire or don’t based on the strength of the received signals. The activation functions either escalate or dampen the input, depending upon the function’s nature. Popular types of activation functions include sigmoid, tanh, ReLU (Rectified Linear Units) and softmax.
The essence of an activation function within a neural network is to provide complexities and layers through which the network learns from the vast amount of data given to it. Importantly, it helps the neural network use the backpropagation gradient descent algorithm to adjust the weight of neurons and optimize network performance. It is crucial to driving the feasibility and efficiency of a neural network model, enabling it to create useful hierarchies of learned features, and ultimately, make accurate predictions or classifications.