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🔡 Character-Level RNN for Name Origin Classification

Watch a recurrent neural network read a surname one letter at a time, updating a hidden-state memory with every character, until the final state reveals which language or culture the name most likely came from.

Machine Learning & Neural Networks3DModerate60 FPS

The simulation animates a name being fed into a recurrent cell one character at a time, showing the hidden-state vector morph with each new letter until the final state is mapped to a predicted language of origin.

🔬 What It Demonstrates

🎮 How to Use

Type or select a surname to feed into the network, then step or play through the characters one at a time to watch the hidden state update, and check the output panel for the predicted origin and its confidence across languages.

💡 Did You Know?

In the original PyTorch tutorial this design is based on, the network correctly learns that endings like '-ez', '-ov', and '-ski' are strong giveaways of origin, purely from being shown thousands of labeled examples, without ever being taught a single explicit spelling rule.

⚙ Under the hood

Feed a name into a recurrent network one character at a time and watch its hidden state evolve into a prediction of the name's language of origin.

rnncharacter-levelname classificationnlphidden statemachine-learningsequence modeling

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install