NN Visual

NN Visual lets users configure and train neural networks while viewing layer activations and learning steps.

nn-visual.com
NN Visual homepage screenshot

About NN Visual

NN Visual is a web tool for configuring, initializing and training neural networks while viewing layer activations and learning steps. Users can choose a dataset, set up hidden layers, train the model, and follow forward and backpropagation behavior through the interface.

Built-in examples shown include auto_mpg regression, iris classification, XOR binary classification and MNIST classification with a drawing input. During training, the site displays training loss, held-out test loss, mean absolute error and weight distribution. A leaderboard records accuracy at epoch 100, and entries can be submitted after training to epoch 100. Pricing information is not stated.

Key features

  • Choose datasets for regression, classification, XOR, or drawn MNIST digits
  • Configure hidden layers and initialize a model before training
  • Train networks and view forward and backpropagation steps
  • See layer activations through node brightness or color intensity
  • Track training loss and held-out test loss over epochs
  • Inspect mean absolute error and weight distribution histograms
Best for: Students, educators and beginners who want an interactive way to observe how neural networks learn from data.
Good for
  • Combines dataset selection, network configuration and training in one web interface.
  • Includes built-in regression, classification and image datasets.
  • Displays training loss, held-out test loss, mean absolute error and weight distribution.
Watch out for
  • Available only as a web tool.
  • Leaderboard submission requires training to epoch 100.
  • No pricing information is stated.

NN Visual at a glance

Type
Online tool
Pricing
Not confirmed. Check the site
Platforms
Web
Open source
Not stated
Language
English
Last checked
Oct 5, 2026 · Online when checked
Listed
Oct 5, 2026

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NN Visual FAQ

What datasets are available?
The homepage shows auto_mpg regression, iris classification, XOR binary classification, and MNIST classification with a drawing input.
What can I monitor while training a network?
The interface shows training loss, held-out test loss, mean absolute error, weight distribution, and node brightness or color for activation strength.
How do I submit a score to the leaderboard?
Train to epoch 100; the leaderboard measures accuracy at epoch 100 and entries can be submitted after reaching that epoch.
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