Deep Learning Visualizer — Watch a Neural Network Train, Live

This visualizer demystifies the core mechanics of deep learning by making the abstract process of neural network training tangible. Instead of just seeing an accuracy score, you watch the entire lifecycle of learning.

When you initiate a training run (e.g., on XOR data), three key processes unfold in real time:

• Forward Pass: Input data flows through the hidden layers, calculating an initial prediction. You can observe how each neuron weights its connection to the next layer.
• Error Calculation: The model’s prediction is compared to the true label, generating a quantifiable error signal (loss).
• Backpropagation: This crucial step sends the error backward through the network. You will see every weight and bias adjust itself mathematically to minimize future errors, refining the decision boundary step by step.

It’s a live look at how data shapes knowledge.

To maximize your learning experience with the Deep Learning Visualizer, approach it like a scientist observing an experiment.

• Start Simple (XOR): Begin with classic, simple datasets like XOR. This allows you to focus purely on observing the mechanism of backpropagation without being overwhelmed by complex data distributions.
• Manipulate Hyperparameters: Don't just run it once. Change the learning rate or the number of layers and watch how these single changes drastically alter the weight adjustments and final decision boundary.
• Observe Different Modes: Toggle between slow motion and fast mode. Slow motion helps map the precise adjustment trajectory of a key weight, while fast mode shows the power of convergence over many epochs.

Active manipulation is the fastest path to deep understanding.