AI neural neural network definition: Importance, Types & Functionality

Artificial Intelligence (AI) neural networks are a Machine Learning model designed to recognise patterns and solve complex problems. Neural networks resemble a human brain, which has inter-connected neuron-like nodes, that process and send information through layers. These layers learn from data sets to improve accuracy in different tasks.

Neural networks in AI help to improve how industries operate, make decisions‌ and interact with customers. Let’s see how…

Simplifying complex patterns

Artificial neural networks are great at understanding complex data patterns that are hard for humans to understand.

Whether it's recognising faces in a crowd or predicting weather patterns, these networks can analyse vast amounts of data.

This ability makes them essential in fields like healthcare for diagnosing diseases from medical images, or in finance for predicting stock market trends.

Improving decision-making

Artificial neural networks process historical data to drive better decisions and outcomes.

For example, in supply chain management, neural networks predict ‌inventory and even plan delivery routes well.

Automating routine tasks

Neural networks use ‌historical data to complete routine tasks faster and more accurately than humans. For example, they can sort emails and answer customer service questions. This automation not only speeds up work but also lets human workers focus on more strategic tasks.

Improving customer experiences

By analysing customer data, neural networks provide personalised recommendations, targeted advertisements‌ and dynamic pricing, all tailored to individual preferences.

An artificial neural network works much like a human brain. It’s a complex network of "neurons" ‌arranged in 3 layers: an input layer, hidden layers‌ and an output layer.

Here’s how these layers function step-by-step 👇

Input layer

This is where the neural network receives its data. The input layer — similar to how our senses work when we perceive new information — handles each piece of data you feed into the network.

Hidden layers

Data subsequently moves to ‌ hidden layers.

These layers are the main processing units of the network, where all the computations happen. Each neuron in these layers changes the inputs it receives by performing simple calculations.

These inputs are then passed on to the next layer.

Output layer

The processed information reaches the output layer, which determines the result.

For example, in a facial recognition system, the output layer might consist of several neurons, each representing a different person's face that the system has been trained to recognise.

The output layer neurons, which produce the highest values signal, form the system’s final result.

AI neural networks learn much like how humans do — by experience. These networks are Machine Learning models that involve training algorithms to learn from and make decisions based on data.

To facilitate this, AI neural networks are fed with a lot of data, which could be anything from pictures and videos to numbers and words. AI looks at this data and tries to find patterns or features that stand out or are relevant to their task, such as shapes in images or trends in numbers.

Initially, when a neural network is set up, its results might be wrong. Here’s where Machine Learning comes into play.

These systems improve their performance over time without being explicitly programmed, adapting to new information and patterns as they’re exposed to more data. In other words, the system learns and gets better at its tasks, delivering better results.

Neural networks come in various types, each designed for specific tasks and challenges. Here are some of the most common types 👇

Feedforward neural networks (FNN)

This is the simplest type of neural network. In FNN, information travels in a linear way, from input nodes, through hidden layers (if any), to output nodes. This type is widely used for straightforward results and classification tasks.

Recurrent neural networks (RNNs)

Recurrent neural networks help ‌process sequences of data, such as speech or text. They work by logging a memory of previous inputs and outputs, which are looped back into the new input. This helps networks understand context and give results based on past information.

Convolutional neural networks (CNNs)

CNNs are a type of neural network made up of multiple layers, particularly effective for processing data with ‌grid-like patterns, such as images. They identify patterns in data, making them excellent for image recognition tasks, such as identifying objects in photos.