Interactive platform.
Choose an experiment, open fullscreen mode, enter your own data, or use automatic animation to explain concepts step by step.
ML Methods
Core algorithms from dimensionality reduction to deep learning.
ML Workflow
Configure a dataset and features, preprocess, train a model, read the evaluation, then use the model to predict.
Data → Model
Dimensionality ReductionOpen
PCA
Choose 2 or 3 principal components and compare PCA projections in 2D and 3D using explained variance.
Z = XW
ClassificationOpen
KNN
Add points, change K, and observe how nearest neighbors shape the decision boundary.
d(x,y) = sqrt(sum((x-y)^2))
ClusteringOpen
K-Means
Run automatic or step-by-step iterations to follow centroid movement.
min sum ||x - mu_k||^2
OptimizationOpen
Gradient Descent
Pick a starting point, compare learning rates, and follow the path toward local or global minima.
theta <- theta - alpha * grad L
RegressionOpen
Linear Regression
Enter your own observations and watch the best-fit line and residuals update.
y = beta_0 + beta_1 x
RegressionOpen
Interpolasi
Atur titik data, pilih derajat polinom, lalu bandingkan prediksi di dalam rentang data dengan prediksi di luar rentang data.
y_hat = f(x); x in range = interpolation, x outside range = extrapolation
ClassificationOpen
Logistic Regression
Learn how the sigmoid function converts a linear score into class probability.
p(y=1) = 1 / (1 + exp(-z))
ClassificationOpen
Decision Tree
Prepare a dataset, run training, then compare data boundaries with tree rules and Gini calculations.
Gain = Gini(parent) - sum w_i Gini(child_i)
EnsembleOpen
Random Forest
Compare voting across many trees and see how an ensemble boundary becomes more stable.
y = mode(h_1(x), ..., h_T(x))
ClassificationOpen
SVM
Visualize the hyperplane, margin, and support vectors that define the decision boundary.
min 1/2 ||w||^2 + C sum hinge loss
Deep LearningOpen
Neural Network
Configure hidden layers, train an XOR network, and inspect neuron activations inside the architecture.
a = sigma(Wx + b)
Deep LearningOpen
CNN Convolution
Edit input pixels, choose a kernel, and watch feature maps produced by convolution.
Y[i,j] = sum_m sum_n X[i+m,j+n] K[m,n] + b
Deep LearningOpen
Transformer
Enter your own sentence and inspect Q, K, V, scaled dot-product, softmax, and the attention matrix.
Attention(Q,K,V) = softmax(QK^T / sqrt(d))V
Model Evaluation
Read prediction quality beyond raw accuracy.
Confusion Matrix
Edit multi-class matrix cells and calculate accuracy, precision, recall, specificity, and F1 score.
Accuracy = sum(diagonal) / total
Segmentation MetricsOpen
IoU + Dice
Move actual and predicted masks on a pixel grid and compare IoU, Jaccard, Dice, and DSC in real time.
IoU = intersection / union; Dice = 2 intersection / (A + B)
Image Processing
Extract color and image features before modeling.
RGB Channels
Click image pixels and inspect real Red, Green, and Blue channel values separately.
I(x,y) = [R(x,y), G(x,y), B(x,y)]
Color TransformationOpen
RGB to HSV
Click image pixels and follow the real-time conversion from normalized RGB to Hue, Saturation, and Value.
H = f(R,G,B); S = delta / max; V = max
Remote SensingOpen
Spectral Indices: NDVI, NDWI, SAVI
Slide the Band 4 and Band 8 blend into an index map, click any pixel, and follow the real formula.
NDVI = (B8 - B4) / (B8 + B4)
Signal Processing
Inspect signals in time, frequency, and component views.
Signal Filter
Enter a signal or choose a sample, set the cutoff frequency, and compare input with low-pass or high-pass output.
y[n] = alpha x[n] + (1-alpha)y[n-1]
Frequency DomainOpen
FFT Spectrum
Enter a signal or choose a sample, then map the waveform into an FFT spectrum up to the Nyquist limit.
X[k] = sum x[n] exp(-j 2 pi kn / N)
DecompositionOpen
Signal Decomposition
Enter an observed signal and decompose it into trend, seasonality, and residual components on the same time axis.
observed = trend + seasonal + residual