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How Neural Networks Work - mklabgr - 09-07-2026
The freeCodeCamp article explains neural networks by starting from the familiar straight-line equation $y=ax+b$, where $a$ corresponds to a weight and $b$ to a bias. Using a teacher predicting students’ exam performance, it shows how adjusting these parameters resembles learning in linear regression, while classification adds an activation step to make decisions such as pass/fail. Deep neural networks extend the same idea to many inputs using matrix equations such as $z=Wx+b$, then apply nonlinear activation functions like $\mathrm{ReLU}(z)=\max(0,z)$ and stack many such layers so the model can capture complex interactions between features.
During training, weights and biases are repeatedly adjusted to reduce prediction error, allowing the network to generalize to new inputs. The article’s central message is that, despite their apparent complexity, deep neural networks are fundamentally built from repeated combinations of simple linear transformations plus nonlinear activation functions.
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