How AI Thinks 🧠

This is a simple neural network that learns to tell if an animal is a rabbit 🐇 or a dog 🐶 based on two things:

Weight in grams
Ear length in centimeters

You can teach the AI using training data, then try new values to see what it predicts!

Training Data (weight in grams, ears in cm, label):

Try new animal features:

Weight (g): 2000
Ears (cm): 10

AI thinks it's a: ❓

🧠 What are you seeing above?

The yellow bubbles are neurons. They take numbers (like weight or ear length), pass them through math functions, and share their results with the next layer.

The more yellow a circle is, the more it is activated. This means it's reacting more strongly to the input. The final layer has two neurons: one for 🐇 Rabbit and one for 🐶 Dog.

The number inside each circle shows how "active" that neuron is (from 0.00 to 1.00). The AI picks the one with the highest value.

For example, if the Rabbit neuron is 0.25 and Dog is 0.75 → AI says: it’s a Dog! 🐶