What Is a Neural Network (For Non-technical People)?

From chat tools to smart recommendations, we use AI every day.

But have you ever asked yourself: how does it all really work?

The answer often points to a neural network AI, a core piece of artificial intelligence that’s easier to understand than you think.

What is a Neural Network?

You’ve probably heard neural networks described as being like the human brain, and that’s a good starting point for understanding them. More formally, it’s a type of machine learning model—often called an artificial neural network or simply a neural net—that’s loosely inspired by the structure of our biological brains.

And for good reason: not unlike a brain, a neural network learns to recognize patterns in data by figuring out complex relationships between inputs and outputs. It’s much like how humans learn from experience.

Although it’s not a “brain” in the sentient sense, it’s a highly effective, adaptable pattern-matching system that can find connections and make decisions in ways traditional, rule-based programming often can’t.

Think of it as a simplified decision-making system built from many interconnected switches, where each switch learns to respond in a certain way based on the signals it receives.

The Building Blocks: Neurons and Layers

To get a clearer picture of how these “brains” are constructed, let’s look at their basic components. A neural network is made up of many artificial neurons (often just called neurons or nodes) arranged in multiple layers.

These neurons are organized in a specific flow:

• The input layer is where your data first enters the network. Each neuron in this layer represents a piece of information you’re feeding in (e.g., a pixel from an image, a word from a sentence, or a characteristic of a customer).
• The hidden layer(s) are where the real “magic” of processing happens. Between the input and output layers, there can be one or multiple hidden layers. These layers perform complex calculations and transformations on the data, uncovering deeper patterns. The more complex the problem, often the more multiple hidden layers are involved.
• The output layer is where the network’s final decision or prediction comes out. For example, if it’s an image recognition network, this layer might tell you if the image contains a cat or a dog.

All these neurons are interconnected; each one in a layer typically connects to every neuron in the previous layer, passing information along. Together, these layers make up a vast web of interconnected nodes that allow information to flow and be processed, leading the network to its final conclusion.

Imagine a neural network diagram with lines connecting circles in a series of columns, like the image below. That’s what I’m talking about.

How Does a Neural Network Learn? (Simplified)

So, we know what a neural network is made of, but how do these interconnected layers of “neurons” actually get smart? They don’t just magically know things; they learn through a rigorous, iterative process, much like how a child learns or, perhaps even more simply, like training a dog.

The Training Process: Learning from Examples

Imagine you’re teaching a dog to sit. You give it a command (“Sit!” – that’s your input). The dog tries to respond (it might sit, or it might just stare at you – that’s its output). If it sits, you give it a treat (positive feedback). If it doesn’t, you gently guide it. You repeat this process over and over until the dog consistently sits on command.

That’s a simplified version of how a neural network learns, often through a process called supervised learning. The network is fed vast amounts of data; think millions of images, thousands of customer reviews, or reams of text. For each piece of data, it already knows the “right answer” or desired output.

The data first goes through forward propagation: this means the information flows from the input layer, through the hidden layers, all the way to the output layer, where the network makes its initial prediction.

Then, that prediction is compared to the actual “right answer.” If there’s an error, the network essentially gets a “correction” (like a treat or guidance for the dog). This feedback causes the network to adjust its internal workings, specifically, the strength of its connections (weights), to reduce that error.

The process is repeated millions of times with countless examples until the network becomes highly accurate at making predictions on new, unseen data. This iterative adjustment is a core part of what enables breakthroughs in areas like deep learning.

The Role of Weights and Connections

Within a neural network (or artificial neural network), the connections between neurons are incredibly important. Each connection has an associated “weight.” Think of a weight as a number that represents how much influence one neuron’s output has on the input of the next neuron.

During the learning process, the network is constantly adjusting these weights. If a connection leads to a correct prediction, its weight might be strengthened. If it leads to an error, its weight might be weakened.

This continuous fine-tuning of weights allows the network to “learn” and store information about patterns. These adjusted weights determine how much influence one neuron has on the next, allowing the network to recognize specific patterns, make classifications, or generate accurate responses.

Why Neural Networks Matter: What They Can Do

So, why has this “brain-inspired” technology become such a game-changer? Simply put, neural networks excel at solving problems that are far too complex for traditional, rule-based programming.

Imagine trying to write a set of explicit instructions for a computer to recognize every single breed of dog from a photo, regardless of lighting, angle, or background clutter. It’s almost impossible.

Neural networks cut through this complexity. They have an incredible ability to learn directly from raw data without needing explicit rules coded by a programmer. Instead, they discover intricate connections and nuanced relationships within the data, making them powerful tools for pattern recognition.

Key Capabilities and Common Tasks

Neural networks’ unique learning ability unlocks a vast array of practical applications:

• Image Recognition is perhaps one of the most visible applications. Neural networks are behind the technology that allows your phone to unlock with your face, automatically tag friends in photos, or even power self-driving cars. This falls under the broader field of computer vision, where machines “see” and interpret visual information.
• Natural Language Processing (NLP). From understanding your voice commands to powering sophisticated chatbots and summarizing articles, neural networks are fundamental to natural language processing. They allow computers to understand, interpret, and generate human language in ways that feel incredibly natural.
• Recommendation Systems. Ever wondered how Netflix knows exactly what movie you might like next, or how Amazon suggests products you’ll probably buy? That’s often a neural network at work, analyzing your past behavior and preferences to predict future interests.
• Classification Tasks involve categorizing data into predefined groups. A classic example is spam detection in your email inbox; a neural network can learn to classify incoming emails as “spam” or “not spam” based on patterns it has learned from countless examples.

Different Types of Neural Networks (Key Architectures)

Just like you wouldn’t use a screwdriver to pound in a nail, not all neural network architectures are built for the same job. While the core concept of interconnected neurons remains, these networks are designed with specialized structures to handle different types of data and solve specific problems more efficiently.

Feedforward Neural Networks

Feedforward Neural Networks are arguably the most basic and common type of neural network. In a feedforward neural network, information flows in only one direction: from the input layer, through any hidden layers, and directly to the output layer. There are no loops or cycles where information can go back to a previous layer.

If a feedforward network has one or more hidden layers, it’s often referred to as a Multi-Layer Perceptron (MLP). They’re excellent for tasks like classification and regression, where data is independent of past inputs.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are the rockstars of visual processing. They’re specifically designed to excel at handling grid-like data, with images being the most prominent example.

CNNs have special layers called convolutional layers that act like little filters, automatically scanning an image to detect specific features, edges, textures, shapes, and faces, regardless of where they appear in the picture. It’s an ability that makes them incredibly powerful for image recognition, facial recognition, and other computer vision tasks.

Recurrent Neural Networks (RNNs)

If you’re dealing with sequences of data where the order matters, you’re likely looking at a Recurrent Neural Network (RNN). Unlike feedforward networks, RNNs have loops, giving them a form of “internal memory,” which allows them to use information from previous steps in a sequence to inform current decisions.

This makes them perfectly suited for tasks involving language (Natural Language Processing), speech recognition, and analyzing time-series data. Think of how understanding a word in a sentence depends on the words that came before it; that’s what RNNs are built for.

Graph Neural Networks (GNNs)

Graph Neural Networks (GNNs) are specifically designed to process and learn from data that is structured as a graph, where nodes (like people or products) are connected by relationships (like friendships or purchases). It’s a level of flexibility that means neural networks are being adapted to tackle an ever-wider range of complex, interconnected data challenges.

Neural Networks and Deep Learning: Understanding the “Deep”

One last point before we sign off: You often hear “neural networks” and “deep learning” mentioned almost interchangeably, and it’s easy to get them mixed up. The truth is, deep learning isn’t a completely different thing from neural networks. Instead, it’s a subset of machine learning that specifically uses a particular kind of neural network: the deep neural network.

So, what makes a neural network “deep”? It simply means it has multiple hidden layers. Think back to our earlier diagram: instead of just one or two hidden layers between the input and output, a deep neural network has, well, many.

Why does “deep” matter so much? Because more layers allow the network to learn increasingly complex and abstract patterns. Each layer can learn a different level of representation from the data. For example, in an image, the first hidden layer might detect edges, the next might combine edges into shapes, and subsequent layers might recognize entire objects or faces.

This hierarchical learning is what has enabled incredible breakthroughs in areas like generative AI, the technology behind tools like ChatGPT or DALL-E. Large Language Models (LLMs), for instance, are a type of deep neural network, and their vast capabilities come directly from their deep architecture. This complex learning process is powered by a deep learning algorithm that continuously refines the deep learning model.

The development of sophisticated deep learning systems has even led to entirely new fields of study and deep learning specialization. Many of these models are also built upon pre-trained networks, further leveraging existing knowledge.

Pretty exciting stuff!

Conclusion and Next Steps

Alright, so those AI magic tricks you see everywhere? Now you’re practically an insider. Neural networks are the unsung heroes behind it all, quietly learning patterns through their interconnected layers and improving the tools many of us use and take for granted.

Here’s to you, neural networks.