- A neural network in everyday words
- The building blocks inside a neural network
- How a neural network learns from examples
- Types of neural networks you'll hear about
- Where neural networks already help in a business
- What a neural network can't do for you
- Neural network, deep learning, AI: how they fit together
- Frequently asked questions
A neural network is a type of software, loosely inspired by the brain, made of many simple connected units called neurons arranged in layers. It learns by studying lots of examples and adjusting the strength of the connections. The part people trip over: it finds patterns, it doesn't understand like a person.
Neural networks sit underneath almost every AI tool you've heard of, from the chatbot that drafts your emails to the app that tidies a photo. The name sounds like biology homework, so most people skip it. You don't need a maths degree to follow the idea, though, and knowing it makes AI tools feel a lot less mysterious.
A neural network in everyday words
Wikipedia describes an artificial neural network as a computational model inspired by the structure and functions of biological neural networks. In other words, people looked at how brains pass signals around and built software that works in a loosely similar way.
Think of a team of very simple helpers, each of whom does one tiny job: take a few numbers in, do a small calculation, pass a number on. One helper is useless alone. Thousands of them wired together can spot a face in a photo or finish your sentence.
The building blocks inside a neural network
Three ideas cover most of it.
- Neurons. Wikipedia says artificial neurons receive signals, process them and pass signals on to the neurons they're connected to.
- Weights. Each connection has a weight that sets how strongly a signal passes along. Learning means adjusting these weights.
- Layers. Neurons are grouped into an input layer, an output layer and usually hidden layers in between.
AWS gives a handy picture of the simplest case. A basic network has an input, hidden and output layer, and for a yes or no question the output can be a single node that says yes or no. Say the input is a customer email and the answer is "complaint or not".
| Part | Plain meaning | Small business example |
|---|---|---|
| Input layer | Where the data goes in | The words of a customer message |
| Hidden layers | Where the patterns are worked out | Spotting angry or urgent wording |
| Output layer | The answer that comes out | Flag it as a complaint |
| Weights | How much each signal counts | Tuned during training |
How a neural network learns from examples
Nobody types in the rules. Instead, the network is shown lots of examples and nudged towards the right answers. Wikipedia explains that training aims to reduce the errors it makes on sample data, and a method called backpropagation spreads those errors back through the network so the weights can be adjusted.
AWS explains the most common route, supervised learning, where the data is labelled with the correct answers in advance. That's how a network can learn to tell sales enquiries from spam: someone has already labelled a pile of each.
Two practical points follow. First, Wikipedia says training is compute-intensive and relies on large datasets, with graphics processors (GPUs) speeding it up. Second, it notes training happens before the network is deployed, so a network can be retrained from scratch when more sample data becomes available. It isn't quietly learning from your chats as you go.
Types of neural networks you'll hear about
The jargon makes more sense once you know what each type is for. Wikipedia gives these examples:
- Convolutional networks significantly improved computer vision, which helps with photos and scanned documents.
- Recurrent networks were built for data that comes in order, such as speech.
- Transformers underpin many large language models, the engines behind chat assistants.
Where neural networks already help in a business
AWS lists computer vision, speech recognition, natural language processing and recommendation engines among common uses. You may meet these without noticing:
- Transcription tools that turn a call recording into text.
- Shop and streaming suggestions that say "you might like this".
- Photo tools that cut out a background or sort images.
- Chat assistants that draft, summarise and translate.
If you'd like a shortlist of tools built on this technology, my roundup of the best AI tools for small business owners is a sensible place to start.
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What a neural network can't do for you
It's easy to picture these systems as small brains. The reality is more modest, and it's worth keeping in mind when you rely on one.
- They need a lot of examples. Wikipedia says they typically need vast numbers of sample inputs, far more than biological brains.
- They're hard to explain. IBM notes that the internal parameters of deep models rarely map to real-world meaning, so they're often called black boxes.
- They copy their data. If the examples are patchy or one-sided, the answers can be too.
- They aren't people. Pattern spotting isn't the same as understanding your business.
So for anything that affects a customer, a contract or your reputation, keep a human check in the loop.
Neural network, deep learning, AI: how they fit together
People mix these up constantly. AI is the broad field. A neural network is one way of building AI. Wikipedia says a network with at least two hidden layers is typically called deep, which is where the term deep learning comes in. A language model is a particular kind of neural network trained on text.
Want a hand working out which of these tools is worth your time? That's the kind of question I help with as an AI consultant, and my guide to using AI in everyday life shows plenty of small ways to begin.
Frequently asked questions
What is a neural network in simple terms?
It's software made of many simple connected units, called neurons, arranged in layers. It learns patterns from examples by adjusting how strongly the units influence each other.
Is a neural network the same as the human brain?
No. Wikipedia says neural networks are inspired by biological ones, but they're a computational model, and they typically need far more sample data than a brain does.
What are neural networks used for?
AWS lists computer vision, speech recognition, natural language processing and recommendation engines. Many chat assistants, photo tools and transcription apps rely on them.
What is the difference between a neural network and deep learning?
Deep learning uses neural networks with many layers. A neural network is the general idea; deep learning describes the bigger, layered versions.
Do neural networks keep learning while I use them?
Usually not. Wikipedia says training happens before deployment, and a network may be retrained from scratch when more data arrives.
Why can't AI explain how it got an answer?
In deep models the internal settings rarely map to real-world meaning, so even experts struggle to give a simple reason. That's why these systems are called black boxes.
