Activation Functions

"Boss" of a Neuron

Activation functions in neural networks are an important part of every neuron. They take as input the sum of all the inputs multiplied by the weights (what the neuron "sees"), and then convert this sum into some value, which is then transmitted further through the network.

Imagine a department in an office. Employees in this department process the information they receive and decide what to do next. In our analogy, the department is a single neuron, the employees of the department are the weights of the neurons, and the information they receive is the input.

Each employee processes information, taking into account their specifics (weights). But the decision on what information to transfer further is made by the head of the department. This is where the activation function comes into play.

The activation function is the internal "boss" of each neuron. It looks at the information processed by the workers and decides what to do next. Depending on how "important" the boss considers the information, he may decide to pass it on down the chain (to another neuron in the next layer of the network) or forget about it.

Note

The workers in this example act as neuron connections. They take their input and transform it according to the weights they know.

In a more mathematical way, the activation function introduces a non-linearity into the operation of the neuron, which allows it to extract more complex patterns from data and introduces flexibility into the operation of a neural network.

Activation Function Options

Examples of activation functions include:

• Sigmoid Function: This function converts any input value to a number between 0 and 1. This allows the neuron to generate an output that is always in a certain range;

• ReLU (Rectified Linear Unit): This activation function converts any negative number to 0 and leaves any positive number unchanged. This is a simple feature that allows neurons to easily handle non-linear problems;

• Tanh (Hyperbolic Tangent): This function is very similar to the sigmoid function, but it converts the input to a number between -1 and 1, making it more versatile than the sigmoid function.

Activation Function Differences

Different activation functions are used in different cases, depending on what task the neural network needs to solve. It is important to remember that the choice of activation function can significantly affect the ability of a neural network to learn and make accurate predictions.

If we use the ReLU activation function, the “boss” will work according to the principle "everything that is important, I leave, and everything that is not important (that is, negative), I throw away."

If we use the sigmoid function, the boss will behave a little differently, trying to turn any information received into something between 0 and 1, which can be interpreted as a probability or degree of certainty. This may indicate how useful the information is.

It is important to understand that an activation function is simply a rule that determines how a neuron reacts to the information it receives. It helps to make the work of the neuron more flexible and adaptive, which in turn allows the neural network to learn and make more accurate predictions.