Neural Network Playground
Overview
The Neural Network Playground lets you build, train, and visualize neural networks in real-time. Construct networks by adding hidden layers and neurons, train them on classic datasets like spirals and XOR patterns, and watch decision boundaries evolve during training. Experiment with different architectures, activation functions, and learning rates to develop intuition about how neural networks learn complex patterns. This interactive tool demystifies deep learning by making it visual and hands-on.
Tips
- Start simple: try a single hidden layer with 4 neurons on the circles dataset
- The XOR problem requires at least one hidden layer - linear models can’t solve it
- Watch the decision boundary evolve in real-time as the network trains
- Try the spiral dataset with 2 hidden layers of 6 neurons each to see deep learning in action
- Experiment with different activation functions - ReLU vs Tanh often behave differently
- Lower learning rates train more slowly but more stably; higher rates may overshoot
- The loss curve should decrease steadily - if it jumps around, reduce the learning rate
- Add more neurons or layers if the network can’t capture the pattern complexity
- Visualize neuron activations to see how the network transforms the input space layer by layer
- Notice how deeper networks can create more complex, curved decision boundaries