Free interactive neural network playground. Train on Titanic, vehicle, or handwritten digit datasets - or your own CSV - and watch backpropagation and gradient descent happen live. 100% in-browser, no install, exports Keras code.
The Neural Network Playground is an interactive way to learn how deep learning actually works - by doing it. Instead of reading about neurons, layers, and backpropagation in the abstract, you train a real neural network on real data, watch every step happen live, and end up with a working model you can question.
Everything runs in your browser on your own hardware. There is nothing to install, no account to create, and no data ever leaves your device - including any CSV files you upload.
1. Choose a dataset. Start with one of three built-in datasets: Titanic passenger survival (predict who survived the 1912 sinking), a vehicle classifier (the same problem from our scikit-learn tutorial), or handwritten digit recognition on a subset of the famous MNIST dataset. Or upload any CSV with a category column you want to predict.
2. Design your network. Add hidden layers, choose how many neurons each one has, and pick the activation function, optimizer (Adam or SGD), learning rate, batch size, and number of training epochs. The live diagram shows your architecture as you build it.
3. Train and watch. Hit Start and watch the network learn in real time: the loss curve falls, accuracy climbs, and the connection weights in the network diagram strengthen and change color as patterns are discovered. Pause, fast-forward, or reset and try different settings.
4. Test and ask questions. When training finishes, you get an honest accuracy score on data the network never saw, plus a confusion matrix showing exactly which classes it confuses. Then ask the model your own questions - move sliders to describe a hypothetical Titanic passenger, or draw a digit with your mouse - and watch it predict with confidence scores.
5. Export real code. Any model you build can be exported as equivalent Python code using TensorFlow/Keras - the bridge from playground to production machine learning.
It is a real neural network. The playground implements the same mathematics used by professional frameworks: forward propagation, cross-entropy loss, backpropagation via the chain rule, and the Adam and SGD optimizers. Nothing is simulated or faked.
Your hardware is enough. The sample datasets train in seconds to under a minute on a typical laptop or phone. Training runs in a background thread, so the page stays responsive while you watch.
Privacy by architecture. Because everything is client-side, uploaded CSV data is processed entirely in your browser memory. You can disconnect from the internet after the page loads and everything still works.
Students meeting machine learning for the first time, developers who want intuition before diving into TensorFlow or PyTorch, teachers looking for a classroom demonstration, and anyone curious what is actually happening inside the AI systems they use every day.
A neural network is a mathematical model made of layers of simple units called neurons. Each neuron multiplies its inputs by adjustable numbers (weights), adds them up, and passes the result through an activation function. Training automatically tunes those weights using examples until the network's predictions match reality. The "knowledge" of a neural network lives entirely in its weights.
No. Training runs 100% on your device using a Web Worker (a background thread in your browser). Sample datasets are downloaded once, and any CSV you upload is parsed and processed entirely in browser memory - nothing is sent to any server. You can disconnect from the internet after loading the page and everything still works.
Backpropagation is the algorithm that figures out how much each weight in the network contributed to a wrong prediction. After measuring the error, it works backwards through the layers using calculus, computing each weight's share of the blame. Every weight then gets nudged in the direction that reduces the error. This is the core of how all modern neural networks learn.
Gradient descent is the process of repeatedly nudging weights downhill on the "error landscape" until predictions are good. The learning rate controls how big each nudge is: too small and learning crawls, too large and the network overshoots and never settles. Try setting the learning rate to 0.1 with SGD in the playground to see instability happen live.
One epoch is one complete pass through all the training data. Networks need many epochs because each pass only adjusts the weights a small amount. In the playground you can watch accuracy improve epoch by epoch - early epochs make big gains, later ones fine-tune.
Overfitting is when a network memorizes its training data instead of learning general patterns. You can spot it in the playground's charts: training accuracy (solid line) keeps climbing while test accuracy (dashed line) flattens or drops. The gap between the two lines is memorization. Try a large network (3 layers of 64 neurons) with 50 epochs on the Titanic data to see it happen.
Three built-in datasets: Titanic survival (712 real passengers - predict who survived), vehicle classification (predict motorcycle/sedan/SUV/truck/bus from specs), and handwritten digits (a 6,000-image subset of MNIST - then draw your own digits). You can also upload any CSV that has a category column to predict, like customer churn or survey results.
No. The entire experience is point-and-click: choose data, design the network with buttons, and train with one click. When you're ready to go deeper, the playground generates the equivalent Python/Keras code for any model you build, and our companion guides walk you through writing real machine learning code.
Convert between CSV and JSON formats instantly. Parse CSV files, generate JSON arrays, and transform data for APIs and databases.
Generate realistic fake data for testing and development. Create mock names, emails, addresses, phone numbers, credit cards, UUIDs, and more. Export to JSON, CSV, or SQL.