One neuron, one tiny decision
Despite the name, a neuron here is not a brain cell. It is a tiny calculation. It receives a few numbers as inputs. From a photo, those might be things like how much fur there is, or how pointy the ears are. Each input has a weight, a number that says how much that input counts. The neuron multiplies each input by its weight, adds everything up, and checks the total against a line. If the total crosses the line, the neuron fires, meaning it sends a strong signal onward. If not, it stays quiet. That is the whole unit.
Deciding whether to go to a party
You already run a neuron in your head. Should you go to the party tonight? Inputs: how many friends are going, how much homework is due, how tired you are. Each has a weight. For some people "friends going" counts for a lot; for others "homework due" wins every time. You add it all up, and if the total beats your personal line, you go. Training is what happens when you regret a decision and quietly change your weights for next time.
Layers: small decisions stacked into big ones
One neuron cannot recognise a cat. But neurons can be arranged in layers, where the outputs of one layer become the inputs of the next. In an image model, the first layer tends to notice simple things: edges, spots of colour. The next layer combines those into shapes like an ear or a whisker. A later layer combines shapes into "cat". Nobody assigns those jobs. They emerge during training. "Deep" in deep learning just means many layers. The networks behind today's chatbots have a great many layers and billions of weights. All those weights together are called the model's parameters.
What training actually changes
- Training changes only the weights. The layout of the network is fixed before training starts.
- The weights begin as random numbers, which is why an untrained model is useless. Every training step nudges them a little.
- After training, the weights hold everything the model knows. There is no rulebook inside to read, which is why even experts find these models hard to explain.
Inspired by the brain, but not a brain
Neural networks were loosely inspired by the brain: lots of simple units, connected, passing signals. That is where the likeness ends. A neuron here is arithmetic. It does not feel, understand or want anything. A better picture is a calculator with billions of dials. Next, the most famous network of all: the one behind ChatGPT.
Check yourself · 3 quick questions
What does a weight do inside a neuron?
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It says how much one input counts — Each input is multiplied by its weight before the totals are added, so the weight sets how much that input matters.
What does training change in a neural network?
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The weights — The structure is fixed before training; every training step nudges the weights, and the finished weights hold what the model learned.
What does the word deep mean in deep learning?
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The network has many layers — "Deep" simply refers to a network with many layers stacked one after another.