Sharad Raj

Full Stack Developer driven by a deep fascination for transformers and their applications in the field of NLP & Computer Vision.

What is an artificial neuron & what are ANNs ?

04 Apr 2020

An artificial neuron is a simple mathematical model of a biological neuron. It is used as a building block for artificial neural networks (ANNs).

An artificial neuron has three main parts:

Artificial neurons work by adding up the weighted inputs and then applying a function to the sum. This function is called the activation function, and it determines how the neuron’s output is calculated.

The activation function is what allows artificial neurons to learn. By adjusting the weights of the inputs, the network can learn to produce the desired output for a given set of inputs.

ANNs are used for a variety of tasks, including classification, regression, and forecasting. They are particularly well-suited for tasks that involve pattern recognition.

Here are some additional facts about artificial neurons:

Despite the challenges, ANNs are a powerful tool that can be used to solve a wide variety of problems.

Inputs

The inputs to an artificial neuron are the values that it receives from other neurons. The number of inputs can vary depending on the design of the network.

Weights

The weights are numbers that determine how much influence each input has on the neuron’s output. The weights are initially set randomly, but they are adjusted during the training process to minimize the error between the desired output and the actual output.

Output

The output of an artificial neuron is the value that it produces. The output is calculated by applying the activation function to the sum of the weighted inputs.

Activation Function

The activation function is a mathematical function that determines how the neuron’s output is calculated. There are many different activation functions that can be used, and the choice of activation function depends on the task that the network is being trained to perform.

Training

ANNs are trained by adjusting the weights of the inputs. The training process is repeated until the network learns to produce the desired output for a given set of inputs. The training process can be time-consuming and computationally expensive.

Applications

ANNs are used for a variety of tasks, including classification, regression, and forecasting. They are particularly well-suited for tasks that involve pattern recognition.

Some examples of tasks that can be performed by ANNs include:

ANNs are a powerful tool that can be used to solve a wide variety of problems.