How Deep Learning Works: A Simple Explanation

1. Neural Networks: Think of them as virtual brains made up of layers of connected “neurons.”
2. Input Layer: Data (like images or text) goes in here.
3. Hidden Layers: Where the magic happens—these layers learn patterns from data.
4. Weights and Biases: They decide how strongly each neuron responds.
5. Training (Backpropagation): The network checks how well it performed, then adjusts weights to reduce errors.
6. Output Layer: Gives you the final prediction (like “dog” or “cat”).

This cycle repeats until the model becomes good at recognizing patterns in data.