NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training
Training neural networks is difficult to interpret, particularly for newcomers. We introduce NeuroViz, an interactive visualization tool that supports real-time exploration of fully connected neural network training. Users can configure network architecture, activation functions, learning rates, and datasets, then observe activations, weight updates, and loss progression. NeuroViz visualizes weight changes in direct correspondence with activation signals in both forward and backward passes, enabling users to distinguish pre- and post-update states within individual epochs and view dynamically updating per-neuron equations. We conduct a comparative user study with 31 participants against s...
| Authors | Tanvi Sharma, Reza Rawassizadeh |
|---|---|
| Primary category | cs.LG |
| Published | 3 May 2026 |
| Last updated at source | 23 May 2026 |
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