Inside a Neural Network
The big idea: A neural network is not a mysterious digital brain. It is a large collection of very simple calculators, called artificial neurons, connected in layers. Each neuron takes in numbers, multiplies them by "importance" values called weights, adds them up, and decides whether to pass a signal forward. Stack thousands of these simple units together, tune their weights during training, and the whole system can recognize faces, translate languages, and recommend videos — even though no single neuron understands anything at all.
By the end of this lesson you will be able to:
- Explain how an artificial neuron works: inputs, weights, bias, and activation.
- Compute a simple weighted sum with small numbers and decide whether the neuron "fires".
- Name the three kinds of layers — input, hidden, output — and say what each one does.
- Explain what "deep" means in deep learning (many hidden layers).
- Describe training as the process of adjusting millions of weight "knobs" to reduce errors.
- Explain why early layers detect simple patterns while later layers detect combinations.
1. Loose Inspiration from the Brain
Your brain contains roughly 86 billion cells called neurons. A biological neuron receives electrical signals from its neighbors through branching fibers. If the combined incoming signal is strong enough, the neuron "fires" and sends its own signal onward to other neurons. Thinking, seeing, and remembering all emerge from billions of these tiny fire-or-stay-quiet decisions happening together.