What Is Activation Function?

Activation functions can be compared to the security guards who work at nightclubs. They decide whether or not a neuron, also known as a "partygoer," is allowed to attend the celebration. (or "send a signal"). You must understand that every neuron receives information from other neurons or the outside world in artificial neural networks. After being subjected to processing and alteration at the hands of the activation function, this information is finally conveyed to the next neuron in the network. The job of the activation function is to determine whether or not the input is "exciting" enough to cause the neuron to "fire" and transmit a signal. If it is, then the activation function has done its job. Before a neuron is permitted to "join the party" and send a signal, the activation function has a threshold that must be reached. It is analogous to a bouncer at a nightclub. At this point, there are various activation functions, each with a persona and set of quirks. While some are extremely strict and will only let signals through if they are compelling, others are more relaxed and allow signals to travel through with greater ease. For instance, the sigmoid activation function can be compared to a pedantic and stern grandmother. It is incredibly picky about the signals it permits to pass through and demands that they be in perfect condition before doing so. On the other hand, the ReLU activation function is analogous to a chill, chill gentleman who surfs. It's a lot easier on the nerves and makes communication much smoother. The question remains: why do activation functions even become necessary in the first place? Simply put, our neural networks would only be as effective with them. If every neuron in a network fired every time it received input, the signals would quickly become overwhelming and impossible to understand. Think of it like this: if every neuron in a network fired every time it received input. Activation functions contribute to managing the flow of signals and ensure that the essential information is the only one transmitted further. They contribute to the network and introduce non-linearities, which can significantly increase the network's computational power. When you train a neural network the next time, remember that the activation functions you select are critically important.