Neural Networks and How They Function

 

In recent years, the artificial intelligence field has experienced growth and development at unprecedented levels. As the name suggests, the field of Artificial Intelligence is focused on making computers think; the immediate source of inspiration for this is ourselves – how we think. Hence, AI programs also think like our brain using Neural Networks or Neural Nets for short. Neural Networks mimic the human brain’s functioning, allowing machines to make intelligent decisions and perform complex tasks.

A neural network is a network of interconnected artificial neurons or perceptrons. As a neural network can be characterized as a (directed) graph, perceptrons may also be referred to as nodes. Every perceptron receives input signals, processes them, produces an output and isorganized into layers, with an input layer receiving external data, an output layer producing the final results, and one or more hidden layers in between. The input layers are used for different parameters and/or variables that the AI model is designed to take into consideration. The output layer is generally a set of nodes with all possible outputs. The actual calculations take place in the hidden layers – undoubtedly the backbone of a neural net – which carry out all the “thinking” taking place.

The most beautiful thing about neural networks is that they have the ability to learn and adapt through training, where their internal parameters can be adjusted based on the input data and the desired output. Here are the components that make neural networks function:

Perceptrons:
A neural network’s building blocks are the perceptrons, also known as nodes or neurons. Every perceptron takes multiple input signals, typically numerical values, and applies a mathematical transformation to produce an output.

Biases:
Bias terms are added to perceptions to provide an additional degree of freedom. A neural network is able to make predictions even when all inputs are zero due to the bias. It essentially acts as an offset, allowing the network to adjust its output independently of the inputs.

Weights:
Weights are the parameters of a neural network controlling the strength of connections between perceptions. A weight is assigned to every connection between two perceptions to determine its influence on another.

The process of training a neural network involves adjusting the weights and biases of the neurons to minimrror between the predicted output and the actual output. This is typically done using an algorithm called backpropagation, which calculates the gradient of the error with respect to the weights and biases of the neurons and adjusts them accordingly. Usually, the outputs in the final layer are from 0 to 1 so the results are never actually perfect. It is possible for an AI to be confident and sure about an answer but it can never be 100% sure. It chooses the output with the highest value. Usually, these outputs are coupled with probabilities of them being correct.

Layers:
Typically, neural networks are organized into layers composed of interconnected perceptions. The input layers receive external data like text or images and pass it to the next layer. The final results are produced by the output layer, such as predicting a numerical value or classifying an image.

Neural networks excel at tasks such as decision-making, speech and image recognition, and natural language processing. Their ability to generalize and learn from vast amounts of data, along with their ability to capture intricate patterns, has helped propel advancements in numerous fields.