probml/pyprobml

Features

• Machine Learning Implementations - Implements a comprehensive collection of probabilistic machine learning models and algorithms using Python.
• Figure Recreation - Executes code to reproduce specific figures and visualizations presented throughout a technical text.
• Bayesian Model Prototyping - Implements Bayesian models and sampling methods to explore and prototype probabilistic machine learning theories.
• Research Figure Generation - Produces publication-quality graphics by executing code that maps mathematical concepts to visual plots.
• Educational Code Notebooks - Encapsulates mathematical models and algorithms within executable notebooks for reproducible research and education.
• Figure Reproductions - Executes models and algorithms specifically to reproduce the scientific figures presented in a technical text.
• Machine Learning Tutorials - Provides pedagogical notebooks that guide users through the implementation of machine learning models using scientific libraries.
• Instructional Code Demos - Provides standalone code examples and supplementary materials to demonstrate probabilistic concepts.
• Numerical Library Integrations - Leverages standard numerical computing and data analysis libraries to implement complex probabilistic models.
• Educational Tutorials - Builds pedagogical notebooks that demonstrate how to use specific scientific computing tools and libraries.
• Machine Learning Tutorials - Provides educational resources and implementation patterns for probabilistic machine learning and numerical computing.
• Scientific Figure Generation - Programmatically generates high-quality scientific plots and figures from machine learning code for publications.
• Vectorized Array Operations - Uses array-based operations to implement high-performance probabilistic algorithms and numerical models.
• Notebook Execution Testing - Provides automated notebook verification and execution testing to ensure code correctness and style consistency.

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