The Building Block: Artificial Neurons
An artificial neuron takes in several numbers, multiplies each by a weight, adds them up, and passes the result through an activation function. If the output crosses a threshold, the neuron fires. This is loosely inspired by biological neurons, but the analogy should not be taken too far.
A single neuron can learn simple boundaries — separating spam from not-spam based on a few features, for example. Real power emerges when you connect thousands or millions of neurons together into a network.
Layers and Architecture
Neural networks organize neurons into layers. The input layer receives raw data, hidden layers transform it through successive abstractions, and the output layer produces a prediction or classification.
A network with one hidden layer can approximate any function in theory, but deeper networks learn hierarchical features more efficiently. In image recognition, early layers detect edges, middle layers detect shapes, and later layers detect objects. This hierarchy is what makes deep learning so effective.
Training: How the Network Learns
Training involves feeding the network examples, comparing its predictions to correct answers, and adjusting weights to reduce errors. The algorithm that calculates how to adjust weights is called backpropagation, and the method used to actually update them is called an optimizer (gradient descent being the most common family).
This process repeats over thousands or millions of examples. Over time, the network's internal representations become increasingly accurate. The amount of data and compute required is why training large models costs millions of dollars.
Why This Matters for You
You do not need to build neural networks from scratch to benefit from them. Pre-trained models and APIs let you add image recognition, language understanding, or speech processing to applications with a few lines of code.
Understanding the fundamentals helps you make better decisions: choosing the right architecture, knowing when a model is overfitting, and understanding why more data usually helps. For a broader perspective on how AI systems compare, see our guide on machine learning vs deep learning.