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probml/pyprobml

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Pyprobml

pyprobml is a collection of notebook-based implementations of probabilistic machine learning models and algorithms. It uses scientific computing and data analysis libraries to execute mathematical concepts and theories for practical application and research.

The project focuses on the programmatic generation of scientific figures and visualizations to recreate results from a technical text. It employs a system of branch-based asset storage to isolate these generated images from the source code.

The repository covers a wide range of probabilistic modeling and machine learning tasks, including Bayesian model prototyping, time series segmentation with hidden Markov models, and sampling via Gibbs sampling. It also includes educational tutorials for numerical computing and image classification.

The project utilizes automated notebook verification and execution testing to ensure code correctness and style consistency.

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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.
7,096 stars·1,623 forks·Jupyter Notebook·MIT·28 views

Star history

Star history chart for probml/pyprobmlStar history chart for probml/pyprobml

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does probml/pyprobml do?

pyprobml is a collection of notebook-based implementations of probabilistic machine learning models and algorithms. It uses scientific computing and data analysis libraries to execute mathematical concepts and theories for practical application and research.

What are the main features of probml/pyprobml?

The main features of probml/pyprobml are: Machine Learning Implementations, Figure Recreation, Bayesian Model Prototyping, Research Figure Generation, Educational Code Notebooks, Figure Reproductions, Machine Learning Tutorials, Instructional Code Demos.

Which projects share features with probml/pyprobml?

Projects with overlapping indexed features include: donnemartin/data-science-ipython-notebooks — This project is a collection of interactive Python notebooks and educational resources designed for mastering data… ageron/handson-ml — This is a machine learning educational repository consisting of a collection of notebooks and code examples. It… tangyudi/ai-learn — Ai-Learn is an educational repository and technical reference designed to facilitate the mastery of artificial… nlintz/tensorflow-tutorials — This repository is a collection of guided tutorials for building and training machine learning models using the… dod-o/statistical-learning-method_code — This project is a reference collection of statistical learning algorithms built from scratch using NumPy for linear… ctgk/prml — PRML is a Python machine learning library and statistical learning toolkit. It provides code implementations of…

Projects sharing features with Pyprobml

These projects share indexed features with Pyprobml. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    ageron/handson-ml

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    tangyudi/Ai-Learn

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  • nlintz/tensorflow-tutorialsnlintz avatar

    nlintz/TensorFlow-Tutorials

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    This repository is a collection of guided tutorials for building and training machine learning models using the TensorFlow framework. It provides practical walkthroughs and examples for implementing a variety of model architectures to solve data prediction and analysis problems. The guides cover the construction of feedforward, convolutional, and recurrent neural networks to analyze complex data patterns. It includes specific tutorials for unsupervised learning, such as denoising autoencoders and word-to-vec embeddings, as well as examples for training generative adversarial networks to synth

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