ELI5: How do neural networks work

Neural networks learn by looking at lots of examples and adjusting themselves to give better answers, just like learning to ride a bike.

Imagine you're learning to ride a bike. At first, you wobble and fall a lot. A neural network is like your brain trying to learn.

• Input: The "input" is like seeing the road ahead, feeling the bike wobble, or noticing the wind.
• Neurons: Inside your brain are tiny helpers called neurons. They're like little switches that turn on or off. Some neurons might notice you're leaning too far to the left, others might sense you're not pedaling fast enough.
• Weights: Each neuron has a "weight" or importance. Some neurons are more important than others for keeping you balanced. These "weights" are adjusted as you learn.
• Learning: When you fall, your brain gets feedback that something went wrong. The neurons adjust their "weights". Maybe the neuron that tells you to lean right gets a little stronger, and the neuron that tells you to pedal faster gets a little weaker.
• Output: The "output" is what you actually do – lean, steer, pedal. After many tries, your brain figures out the best way to adjust the weights so you can stay balanced and ride smoothly.

• You show the neural network lots of pictures of cats (the input).
• Some neurons might look for pointy ears, others for whiskers, and others for furry tails.
• The weights determine how important each of these features are for deciding if it's a cat.
• If the network says "dog" when it sees a cat, it gets feedback that it's wrong.
• The weights are adjusted so that pointy ears, whiskers, and furry tails become more important for recognizing cats.
• After seeing many pictures, the network learns to accurately identify cats (the output).