How a Neural Network Is Actually Trained, Step by Step

Start with a machine that knows nothing

A neural network begins life as a large collection of numbers, usually called weights, arranged in layers. At the outset these numbers are essentially random. The network has no concept of a cat, a fraudulent transaction, or a tumour. Training is the process of nudging those numbers, step by tiny step, until the network’s outputs become useful.

It helps to think of the network as a very elaborate dial-tuning machine. Data goes in one end, a prediction comes out the other, and training is the repeated act of turning the dials so the prediction gets closer to the right answer.

Step one: show it an example

Training starts with a dataset where the correct answers are already known. This might be thousands of photos labelled ‘dog’ or ‘not dog’, or historical records of loan applications labelled ‘repaid’ or ‘defaulted’. Each example is fed into the network in what is called a forward pass. The input travels through the layers, each one performing simple mathematical operations, weighted sums followed by a nonlinear adjustment, until the final layer produces an output, such as a probability that the image contains a dog.

Step two: measure how wrong it was

The network’s guess is compared with the true label using something called a loss function. This is simply a formula that turns the size of the mistake into a single number. A confident wrong answer produces a high loss; a correct or nearly correct answer produces a low one. The entire goal of training is to make this number as small as possible, averaged across all the examples the network will ever see.

Step three: work out what caused the mistake

This is the part that made modern deep learning possible, and it is called backpropagation. Because every weight in the network contributed something to the final answer, the training process needs to work out how much each individual weight was to blame for the error. Backpropagation does this by applying calculus, specifically the chain rule, moving backwards from the output layer to the input layer and calculating a gradient for every weight. A gradient is essentially a sensitivity score: it tells the system whether increasing that particular weight would make the error better or worse, and by roughly how much.

Step four: nudge the dials

Once the gradients are known, an algorithm called gradient descent adjusts every weight a small amount in the direction that reduces the error. The size of that step is controlled by a setting called the learning rate. Too large a step and the network overshoots and never settles down; too small and training crawls along and may take an impractical amount of time. In practice, most systems use refined versions of gradient descent, such as Adam or RMSprop, which adapt the step size automatically as training proceeds.

Crucially, this adjustment does not happen after every single example. Examples are usually grouped into small batches. The network processes a batch, calculates an average error, and updates its weights once per batch. Working through the entire dataset once is called an epoch, and a typical training run might repeat this for many epochs, gradually refining the weights each time.

Step five: check it against data it hasn’t seen

A network can become very good at memorising its training examples without actually learning the underlying pattern, a problem known as overfitting. To guard against this, a portion of the data, the validation set, is held back and never used to update weights directly. Instead, it is used periodically to check how well the network generalises to fresh examples. If performance on the training data keeps improving while performance on the validation data gets worse, that is a warning sign that the network is memorising rather than learning, and adjustments such as simplifying the model or stopping training early may be needed.

Step six: stop, test, and deploy

Once performance on the validation data plateaus, training stops. The final check comes from a third, completely untouched dataset, the test set, which gives an honest estimate of how the network will perform in the real world. Only after this evaluation does a model get considered for deployment, and even then it typically needs ongoing monitoring, because real-world data can drift away from what the network was originally trained on.

Why this matters beyond the lab

Understanding this cycle, guess, measure error, calculate blame, adjust, repeat, explains a lot about AI’s real limitations. It shows why models are only as good as their training data, why more computing power helps but does not guarantee accuracy, and why a system that performs brilliantly on test data can still fail unpredictably when the world it encounters differs from what it learned on. None of this involves anything resembling understanding or intent; it is optimisation, repeated at a scale humans cannot do by hand.

For readers wanting to see how this underpins current policy debates around safety testing and accountability, the Alan Turing Institute and the UK’s AI Safety Institute publish accessible technical explainers that build on these same foundations.