Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Engineers · ML Model Trainer

ML Model Trainer for a sampling error

Built for engineers using it for real design work. Go from concept to a running model in the browser, then scale to the cloud when needed. Simulate a sampling error live below — adjust the inputs and watch it respond, right in your browser.

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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Kmeans

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k-Means ClusteringLive

Lloyd's algorithm · live

Controls

Speed1×

Presets

k (centroids)

True clusters

Lloyd's algorithm: assign each point to its nearest centroid, then move each centroid to the mean of its members. Repeat until stable. Try setting k different from the true cluster count.

▶ Run in Python

Data Inspector

Iteration0

Inertia0.00e+0

Governing equation

J = k = 1 \sum 4 x \in C_{k} \sum ∥ x - μ_{k} ∥^{2}, μ_{k} = \frac{1}{∣ C _{k} ∣} x \in C_{k} \sum x

Reading this result: k matches the 4 true clusters, so each centroid can settle onto one real group and inertia falls to a clean minimum.

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

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PCA

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Principal Component AnalysisLive

finding the axes of variation

Controls

Presets

Correlation

Spread (x)

PCA finds the directions along which data varies most. The eigenvectors of the covariance matrix are the principal components (cyan = most variance, green = least), and their eigenvalues are the variances along each. Projecting onto the top components is the basis of dimensionality reduction.

▶ Run in Python

Data Inspector

Variance PC10.00

Variance PC20.00

PC1 explains0%

PC1 angle0°

Governing equation

C v = λ v, \frac{λ _{1}}{λ _{1} + λ _{2}} = \frac{0.00}{0.00} = 0.00

Reading this result: PC1 explains 0% of the variance here; the stronger the correlation between features, the more PCA concentrates information on that first component.

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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Frequently asked questions

Is this good for engineers?: Yes — this version of "ML Model Trainer for a sampling error" is framed for engineers using it for real design work. Go from concept to a running model in the browser, then scale to the cloud when needed.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.