Interactive Mathematics for Machine Learning
Master the mathematical foundations of AI through interactive visualizations and hands-on experiments. Explore gradient descent, neural networks, optimization algorithms, and deep learning concepts with real-time animations and step-by-step explanations.
✨ Key Features
🎯 Interactive Visualizations
See mathematical concepts come alive with D3.js animations and real-time parameter adjustments.
📐 Step-by-Step Math
Detailed mathematical derivations with LaTeX equations and manual calculations for every algorithm.
🧪 Hands-On Experiments
Adjust hyperparameters and see immediate effects on optimization paths and convergence behavior.
🎓 From Basics to Advanced
Progressive learning path from fundamental calculus to state-of-the-art deep learning techniques.
📚 Explore Topics
Gradient Descent
Understand how optimization algorithms navigate loss landscapes. Compare vanilla GD, Momentum, Adagrad, RMSprop, and Adam.
Backpropagation
Visualize how gradients flow backward through neural networks using the chain rule. See the computational graph in action.
Activation Functions
Explore sigmoid, tanh, ReLU, and modern variants. Understand their derivatives and effects on gradient flow.
Linear Transformations
Visualize matrix operations, eigenvalues, and vector spaces. See how linear algebra powers neural networks.
Convolution Operation
Watch kernels slide across images, extracting features. Understand stride, padding, and receptive fields.
Neural Network Architecture
Build and visualize custom neural networks. See forward propagation and activation patterns in real-time.
Probability Distributions
Explore Gaussian, Bernoulli, and categorical distributions. Understand maximum likelihood and Bayesian inference.
Attention Mechanism
Visualize self-attention, query-key-value operations, and attention weights. See how transformers focus on relevant information.
Dimensionality Reduction
Explore PCA, t-SNE, and UMAP. Watch high-dimensional data project into 2D/3D space while preserving structure.
Regularization
Understand L1, L2 regularization, and dropout. See how they prevent overfitting and improve generalization.
Loss Landscapes
Navigate 3D loss surfaces. Understand local minima, saddle points, and the geometry of optimization.
Batch Normalization
See how normalization stabilizes training. Visualize activation distributions before and after batch norm.