Neural Networks

A neural network is software that learns patterns from examples. You feed it lots of data, it finds relationships, and then it can guess or classify new stuff it’s never seen.

What it actually is

Think of a neural network as a giant stack of “if this, then maybe that” units, called neurons. Each neuron looks at some numbers, makes a tiny decision, and passes that result forward. After millions of these tiny decisions layered together, you get surprisingly smart behaviour: “that’s a cat”, “this email is spam”, “next word is probably ‘however’…”

The network doesn’t start smart. It improves by being shown examples and adjusting itself to be less wrong each time. That training process is what people mean when they say “the model learned”.

Why it matters

• It generalises: You don’t have to hand-code rules like “cats have pointy ears.” It just learns ‘catness’ from loads of cat photos.
• It scales: Give it more data and more compute, and it often gets better automatically.
• It underpins modern AI: Large language models, image generators, voice clones — they’re all fancy neural networks at heart.

Where you’ll see it in real life

- Face ID on your phone - Auto-transcription / subtitles on YouTube or Teams - Spam filtering in your email - Chatbots like ChatGPT and DeepSeek - Image generators like Midjourney and DALL·E - Self-driving car perception (“that’s a cyclist; slow down”)

Common misunderstandings

• Myth: “Neural networks think like a human brain.”
  Reality: The ‘inspired by the brain’ story is mostly branding. They do maths (matrix multiplication), not biological thought.
• Myth: “If it learned from data, it must be correct.”
  Reality: If the training data is biased or wrong, the network will happily learn that bias and repeat it.

Try it yourself

Good places to poke neural networks hands-on without coding a full PhD:

• ChatGPT: Ask it to classify tone (“Is this message angry or polite?”). That’s a neural net doing text classification.
• Midjourney / DALL·E: Type a prompt, get an image. That’s a neural network mapping text patterns to visual patterns.
• Whisper-style speech-to-text: Record yourself, get instant transcript. That’s audio → text via a trained network.

Want a friendlier analogy, or want maths? Ask and we’ll add it.