A neural network is a mathematical structure made of many simple computing units — neurons — arranged in layers and connected by weighted links. Each neuron takes a weighted sum of its inputs, applies a non-linear activation function, and passes the result to the next layer. Train the weights against examples and the network becomes a general-purpose function approximator that can learn almost any input-to-output mapping.
The biology metaphor is loose. A real neuron is wildly more complex than its artificial counterpart. But the abstraction is enough to get the job done: stack enough layers, give the network enough data, and it learns features hierarchically — from edges to shapes to objects in vision, from letters to words to ideas in language.
The most influential architectures you will hear about:
- Feedforward networks — the simplest pattern, every layer connected to the next. Still used in tabular models and the final layers of larger systems.
- Convolutional neural networks (CNNs) — share weights across spatial locations, dominant in computer vision until transformers caught up.
- Recurrent networks (RNNs, LSTMs) — process sequences token by token, important historically for language and speech, now mostly replaced by transformers.
- Transformers — use attention to weigh relationships between every pair of tokens; the foundation of modern LLMs and increasingly of vision and audio models too.
- Diffusion models — iteratively denoise random noise into images or video; behind Midjourney, Stable Diffusion, Sora and Kling.
For a US engineering team, "should we use a neural network?" is rarely the right question in 2026. The right question is whether the task involves unstructured data — text, images, audio, video — at a scale where rules cannot keep up. If yes, you almost certainly want a neural network, usually a pretrained one. If no — for instance, predicting churn from a clean tabular dataset — gradient-boosted trees or logistic regression may still be the cheaper, faster, more interpretable answer.
Two practical concepts make neural networks feel less mystical. The forward pass is the network making a prediction; the backward pass (backpropagation) is the network adjusting its weights based on the error. Train long enough, on enough data, with enough compute, and the network discovers patterns no human would have hand-coded — which is exactly the source of both its power and its risks.
