Machine Learning

Gradient Descent

Finding the minimum of a cost function.

Perceptron

Visualize the Perceptron algorithm learning a linear decision boundary.

K-Means Clustering

Partitioning data into clusters by iteratively assigning points and updating centroids.

Linear Regression

Fitting a line to data by minimizing the sum of squared residuals.

Attention Mechanism

Attention Mechanism

Decision Boundaries

Visualize how model complexity and regularization shape the separation between classes.

Decision Tree

A non-parametric supervised learning method for classification.

Neural Network Learning

Understanding forward propagation, backpropagation, and weight updates in artificial neural networks.

Support Vector Machine

Visualize how a linear SVM learns a maximum-margin decision boundary using gradient descent on hinge loss.

Backpropagation

Visualize the gradient flow and weight updates in a neural network.

Bias-Variance Tradeoff

Visualize how model complexity affects the tradeoff between bias (underfitting) and variance (overfitting).

Convolutional Filter

Visualize how a kernel slides over an input image to produce a feature map via the convolution operation.

Naive Bayes Classifier

Interactively explore how a Naive Bayes classifier learns probabilities and makes predictions.

Recurrent Neural Network

Recurrent Neural Network.

PCA (Dimensionality Reduction)

Find the principal axes that maximize variance and project data into a lower-dimensional space.

Generative Adversarial Network

Visualize the generator mapping latent noise to a target distribution while the discriminator learns to separate real from fake.

Optimization Landscape

Visualize gradient descent path finding minimums on 2D contour loss surfaces.

Word Embedding (Word2Vec)

Simulate training 2D word vectors using the Skip-gram architecture with Negative Sampling.

Activation Functions

Visualize and compare common neural network activation functions.

Batch Normalization

Visualize the effects of batch normalization on neural network activations.

Confusion Matrix & Metrics

Adjust predictions and true values to see how accuracy, precision, recall, and F1 score react.

Cross-Validation

Visualize K-Fold cross-validation, model fitting, and training versus validation error.

Dropout Regularization

Visualize how randomly disabling neurons during training prevents neural network overfitting.

Ensemble Methods

Combine multiple weak learners to create a single strong model with lower error and greater generalization.

Learning Rate Schedules

Visualize how learning rate decay schedules impact optimization convergence and stability.

Mini-Batch Gradient Descent

Optimize parameters efficiently by estimating gradients on small data batches.

Reinforcement Learning (Q-Learning)

Visualize an agent learning to navigate a grid world using the Q-Learning algorithm.

Transformer Architecture

Visualize the core components and connectivity of a Transformer encoder block.

Autoencoder

Visualize the encoding and decoding process of data through a bottleneck layer.

Data Augmentation Effects

Visualize how image transformations like rotation, scaling, and noise artificially expand training datasets.

Feature Importance (SHAP)

Visualize SHAP values to explain machine learning model predictions based on feature contributions.

Gradient Clipping & Exploding Gradients

Visualize how exploding gradients occur during backpropagation and how gradient clipping mitigates them.

Normalization Techniques

Compare and visualize various data normalization and scaling techniques used in machine learning.

Residual Connections

Visualize how skip connections help mitigate the vanishing gradient problem in deep networks.

t-SNE Visualization

Reduce high-dimensional data into a low-dimensional map by matching pairwise probability distributions.

Contrastive Learning

Visualize how contrastive learning pulls similar examples together and pushes dissimilar ones apart in embedding space.

Knowledge Distillation

Transfer knowledge from a large teacher model to a smaller student model using temperature scaling.

LSTM & Gated Units

Visualize the internal workings of an LSTM cell and its gating mechanisms.

Mixture of Experts

Visualize how a gating network routes inputs to specialized expert networks to form a combined prediction.

Variational Autoencoder

Visualize the encoding of data into a probabilistic latent space and its reconstruction.