Interactive Learning Tools

Interactive plotting tool for visualizing data and relationships

Interactive flow diagrams, timelines, and process visualizations

Visual inspection of variable states and values

Production-grade visualization of transformer attention mechanisms with real-time interactivity

Comprehensive tool for exploring different word embedding techniques and their properties

Comprehensive tool for exploring transformer architectures and their components

Comprehensive tool for exploring different tokenization techniques

Comprehensive tool for exploring different RNN architectures and their properties

Comprehensive tool for exploring various text generation and decoding methods

Comprehensive tool for exploring model training strategies and parallelism techniques

Comprehensive tool for exploring different optimization techniques for model training

2D visualization of classification decision boundaries

Interactive Python code execution environment

Visual tracking of variable states and memory addresses

Interactive visualization of Python data structures

Visualization of function call stack and scope

Step-through execution of algorithms and code

Interactive function anatomy and execution visualizer

Interactive visualization of variable scope and scope chain

Interactive dashboard for data visualization with multiple chart types

Interactive visualization of confusion matrix with metrics

Interactive playground for learning OOP concepts - create objects, call methods, see state changes in real-time

Interactive visualization of class inheritance hierarchies and method resolution order

Visual representation of memory layout and allocation

Watch AI agents move, think, and communicate in real-time - our killer feature!

Interactive visualization of the bias-variance tradeoff and model complexity effects

Train machine learning models interactively with real-time visualization

Interactive visualization of K-Means, DBSCAN, and Hierarchical clustering algorithms

Compare PCA, t-SNE, and UMAP for dimensionality reduction

Interactive anomaly detection with Isolation Forest, One-Class SVM, and statistical methods

Visualize neural network architecture, forward propagation, and training dynamics

Simulate ML model deployment with real-time latency, throughput, and load monitoring