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Explore cutting-edge machine learning algorithms through interactive demos
Note: All these interactive demos were created independently by me. The source code can be found by inspecting your browser. Feel free to explore how they work!
Classification Algorithms
A statistical method for analyzing a dataset with one or more independent variables that determine an outcome.
Computing systems inspired by the biological neural networks that constitute animal brains.
A supervised learning model that analyzes data for classification and regression analysis.
A non-parametric method used for classification and regression that predicts based on closest training examples.
Regression Algorithms
A linear approach to modeling the relationship between a dependent variable and one or more independent variables.
A form of regression analysis in which the relationship is modeled as an nth degree polynomial.
An ensemble learning method for regression that operates by constructing multiple decision trees.
Clustering Algorithms
A method of vector quantization that aims to partition observations into k clusters.
A method of cluster analysis which seeks to build a hierarchy of clusters.
A density-based clustering algorithm that groups together points that are closely packed together.
Dimensionality Reduction
Principal Component Analysis - A technique used to emphasize variation and bring out strong patterns in a dataset.
Independent Component Analysis - A computational method for separating a multivariate signal into additive subcomponents.
t-Distributed Stochastic Neighbor Embedding - A machine learning algorithm for dimensionality reduction.
Uniform Manifold Approximation and Projection - A dimension reduction technique that preserves more of the global structure.
Reinforcement Learning
A model-free reinforcement learning algorithm to learn the value of an action in a particular state.
A combination of Q-learning with deep neural networks to handle high-dimensional observation spaces.
A family of reinforcement learning techniques that rely on optimizing parametrized policies with respect to the expected return.
Search Algorithms
Explore various search algorithms including BFS, DFS, A*, Dijkstra's, and more in one interactive environment.
Tailored AI
Explore how we can use semantic chunking, data manipulation and clever retrieval technique to ground our LLM outputs.
Learn how to teach our LLMs to give us outputs we want them to by rewarding or punishing them accordingly.