A neural network is a web of tiny "neurons" stacked in LAYERS and joined by WEIGHTS that adjust as it learns.
🕸️The Big Picture: Layers Joined by Weights
🕸️ Neurons in three kinds of layers
Input layer: numbers from the new example go in on the left.
Hidden layer(s): neurons combine the clues and pass them along.
Output layer: the answer pops out on the right — cat or dog!
Every connecting line carries a weight — how strongly one neuron pushes the next.
🔬Zoom In: One Neuron Doing Its Math
Each input is multiplied by its weight, the neuron adds them all up, and if the total is big enough it "fires" a signal to the next layer.
🎛️In Real Life: Learning = Turning the Weight "Dials"
🎛️ Weights are like volume dials
Picture each weight as a tiny dial. At the start they are set randomly, so the network guesses wrong. During training the network sees its mistakes and nudges every dial a little — over and over — until the whole web is tuned and the answers come out right. Learning is just tuning the weights.
🔑Key Terms
🕸️Neural NetworkA web of tiny connected "neurons" that learns patterns from examples — loosely inspired by the brain.
🔵Neuron (Node)One little unit that takes in numbers, adds them up, and passes a signal on to the next neurons.
📚LayerA row of neurons. Networks stack an input layer, one or more hidden layers, and an output layer.
🎛️WeightA number on each connection that says how strongly one neuron affects the next. Training adjusts the weights.
⬅️Input LayerThe first layer, where the numbers from a new example enter the network.
➡️Output LayerThe last layer, where the network's final answer or prediction comes out.
⭐Remember: a neural network learns by adjusting its weights — the network's "knowledge" lives in those connection numbers, not in any single neuron!
✏️ Future-Ready Anchor Chart · Inside a Neural Network