Python 3 code to reproduce the figures in the books Probabilistic Machine Learning: An Introduction (aka "book 1") and Probabilistic Machine Learning: Advanced Topics (aka "book 2"). The code uses the standard Python libraries, such as numpy, scipy, matplotlib, sklearn, etc. Some of the code (especially in book 2) also uses JAX, and in some parts of book 1, we also use Tensorflow 2 and a little bit of Torch. See also probml-utils for some utility code that is shared across multiple notebooks.

Features

  • Python code for "Probabilistic Machine learning" book by Kevin Murphy
  • Run notebooks in colab
  • Documentation available
  • Examples available
  • Run the notebooks locally
  • Cloud computing

Project Samples

Project Activity

See All Activity >

Categories

Machine Learning

License

MIT License

Follow pyprobml

pyprobml Web Site

Other Useful Business Software
Build Securely on AWS with Proven Frameworks Icon
Build Securely on AWS with Proven Frameworks

Lay a foundation for success with Tested Reference Architectures developed by Fortinet’s experts. Learn more in this white paper.

Moving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
Download Now
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of pyprobml!

Additional Project Details

Operating Systems

Linux, Mac, Windows

Registered

2024-08-01