Pyprobml Python Code For Probabilistic Machine Learning Book By Kevin

Leo Migdal

-Nov 28, 2025, 5:09 AM

pyprobml python code for probabilistic machine learning book by kevin

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. For the latest status of the code, see Book 1 dashboard and Book 2 dashboard. As of September 2022, this code is now in maintenance mode.

The notebooks needed to make all the figures are available at the following locations. Colab has most of the libraries you will need (e.g., scikit-learn, JAX) pre-installed, and gives you access to a free GPU and TPU. We have a created a colab intro notebook with more details. To run the notebooks on colab in any browser, you can go to a particular notebook on GitHub and change the domain from github.com to githubtocolab.com as suggested here. If you are using Google Chrome browser, you can use "Open in Colab" Chrome extension to do the same with a single click. We assume you have already installed JAX and Tensorflow and Torch, since the details on how to do this depend on whether you have a CPU, GPU, etc.

Python code for "Probabilistic Machine learning" book by Kevin Murphy Python 3 code to reproduce the figures in the book series Probabilistic Machine Learning by Kevin Patrick Murphy. This is work in progress, so expect rough edges. (For the latest status of the code, see Book 1 dashboard and Book 2 dashboard.) See also probml-utils for some utility code. The notebooks needed to make all the figures are available at the following locations.

Colab has most of the libraries you will need (e.g., scikit-learn, JAX) pre-installed, and gives you access to a free GPU and TPU. We have a created a colab intro notebook with more details. To run the notebooks on colab in any browser, you can go to a particular notebook on GitHub and change the domain from github.com to githubtocolab.com as suggested here. If you are using Google Chrome browser, you can use "Open in Colab" Chrome extension to do the same with a single click. The code for most figures is stored in individual files in the scripts directory. You can run these locally (on your laptop), but it's often faster to run in colab (especially for demos that use a GPU).

To do this, just type `%run foo.py`. You can also edit the file in colab, and then rerun it. Note, however, that changes to local files will not be saved beyond the current colab session. (A better, but more complex, approach is to use VScode to ssh into the colab machine, see this page for details.) There are also some inline links to code in the body of the book, labeled code.probml.ai/foo; these refer to demos that are not associated with any figure. Clicking on these links behaves in a similar way to the figure code (opening a tab for the appropriate colab cell).

In addition to the above, many chapters have supplementary code / material (for example, here). These will continue to be updated even after the book is published (contributions welcome!). Python 3 code to reproduce the figures in the book series Probabilistic Machine Learning by Kevin Patrick Murphy.This is work in progress, so expect rough edges!(Some demos use code from our companion JAX State... The scripts needed to make all the figures for each chapter are automatically combined together into a series of Jupyter notebooks, one per chapter.* Volume 1 figure notebooks* Volume 2 figure notebooks. (Note: volume 2 is not finished yet.) In addition to the automatically generated notebooks, there are a series of manually created notebooks, which create additional figures, and provide supplementary material for the book.

These are stored in the notebooks repo, since they can be quite large. Some of these notebooks use the scripts mentioned above, but others are independent of the book content. The easiest way to run these notebooks is inside Colab. This has most of the libraries you will need (e.g., scikit-learn, JAX) pre-installed, and gives you access to a free GPU and TPU. We have a created a intro to colab notebook with more details. The easiest way to run individual scripts is inside Colab.

Just cut and paste this into a code cell:pip install superimport git clone --depth 1 https://github.com/probml/pyprobml &> /dev/null # THIS CODEBASENote: The superimportlibrary will automatically install packages for any file which contains the line... 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. See every risk before it hits.

From exposed data to dark web chatter. All in one unified view. This document provides an overview of the pyprobml repository, a Python codebase designed to reproduce figures and implement algorithms from the "Probabilistic Machine Learning" book series by Kevin Murphy. This repository serves as a companion to both "Probabilistic Machine Learning: An Introduction" (Book 1) and "Probabilistic Machine Learning: Advanced Topics" (Book 2), providing practical implementations and visualizations of the concepts discussed in these... For more detailed information about contributing to this repository, see Contributing Guidelines. The pyprobml repository contains Python 3 code that implements various probabilistic machine learning algorithms using standard Python libraries (numpy, scipy, matplotlib, sklearn) and modern machine learning frameworks (JAX, TensorFlow 2, PyTorch).

The primary purpose is to provide executable examples that reproduce the figures in the book series, helping readers understand the theoretical concepts through practical implementation. As of September 2022, the repository is in maintenance mode but continues to serve as a valuable resource for students and practitioners of probabilistic machine learning. The following diagram illustrates the high-level architecture of the pyprobml repository: Python code for "Probabilistic Machine learning" book by Kevin Murphy "Probabilistic Machine Learning" - a book series by Kevin Murphy A Python package for probabilistic state space modeling with JAX

Python code for "Probabilistic Machine learning" book by Kevin Murphy "Probabilistic Machine Learning" - a book series by Kevin Murphy

Pyprobml Python Code For Probabilistic Machine Learning Book By Kevin

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