How do I simulate an outlier scan?

Open this page and use the live Deep Net Playground tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.

Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.

Can I use my own numbers?

Absolutely — every input is adjustable, and with data import you can drive an outlier scan from your own measurements.

Use case · powered by Deep Net Playground

Deep Net Playground for an outlier scan

Simulate an outlier scan live in your browser. This runs the real Deep Net Playground solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

For:Students Researchers Educators Engineers Hobbyists & Makers K-12 Students First Responders

Neural Net

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Neural Network PlaygroundLive

2-layer MLP · live backprop

Controls

Presets

Hidden neurons

Learning rate

Speed1×

A tiny multilayer perceptron trained by real backpropagation. The background shows its learned decision boundary; dots are the training data, colored by true class.

▶ Run in Python

Data Inspector

Epoch0

Loss0.0000

Architecture2→8→1

Governing equation

a = tanh (W_{1} x + b_{1}), \overset{y}{^} = σ (W_{2} a + b_{2}), L = 0.0000

Reading this result: With 8 hidden units the net bends the boundary into curves — more units capture finer shapes like the spiral, but too many can overfit.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Gradient Descent

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Gradient Descent StudioLive

optimization on a loss landscape

Controls

Watch gradient descent (with momentum) roll downhill on different loss landscapes. Too high a learning rate overshoots.

Presets

Learning rate

Momentum

▶ Run in Python

Data Inspector

Surfaceripple

Step0/200

Optimizermomentum GD

Governing equation

x_{k + 1} = x_{k} - η \nabla f (x_{k}), η = 3

Reading this result: A learning rate of 3 is well matched to the curvature here: steps are large enough to make progress yet small enough to avoid overshoot, giving a smooth, roughly quadratic descent toward the minimum.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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About this simulation

The full Deep Net Playground tool models an outlier scan with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

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Other ways to simulate an outlier scan

Optimize + UQ for an outlier scan Hypothesis Test for an outlier scan Linear Regression for an outlier scan Central Limit Theorem for an outlier scan Confidence Intervals for an outlier scan Bayesian Inference for an outlier scan

Frequently asked questions

How do I simulate an outlier scan?: Open this page and use the live Deep Net Playground tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?: Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?: Absolutely — every input is adjustable, and with data import you can drive an outlier scan from your own measurements.