This entry has been updated to TensorFlow v2.10.0 and PyTorch 1.12.1 An activation function is a function that is applied to a neuron in a neural network to help it learn complex patterns of data, deciding what should be transmitted to the next neuron in the network. A perceptron is a neural network unit that inputs the data to be learned into a neuron and processes it according to its activation function. The perceptron is a simple algorithm that, given an input vector x of m values(x_1, x_2, ..., x_m), outputs a 1 or a 0 (step function), and its function is defined as follows: Here, ω is a vector of weights, ωx is the dot product, and b is the bias. If x is on this line, the answer is positive; otherwise, it is negative.