Follow along with Bishop's PRML textbook and run the Python implementation for each chapter as you read
Experiment with Bayesian models, neural networks, and kernel methods by modifying the notebook code
Study machine learning theory with executable examples using a free Amazon SageMaker Studio Lab account without a powerful local machine
Use the implementations as a reference when building your own versions of algorithms from scratch
| ctgk/prml | facebookresearch/seamless_communication | nielsrogge/transformers-tutorials | |
|---|---|---|---|
| Stars | 11,718 | 11,776 | 11,626 |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Setup difficulty | easy | hard | easy |
| Complexity | 2/5 | 4/5 | 3/5 |
| Audience | researcher | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Can run entirely for free in Amazon SageMaker Studio Lab with just an email address, no GPU or powerful local hardware required.
This repository contains Python code that implements the machine learning algorithms described in "Pattern Recognition and Machine Learning," a textbook written by Christopher Bishop. The book covers a wide range of mathematical and statistical techniques used to train computers to recognize patterns in data, and this project puts those techniques into runnable code so readers can see them in action. The code is organized as Jupyter notebooks, one per chapter of the book. There are 13 notebooks in total, covering topics like probability distributions, linear models, neural networks, kernel methods, graphical models, mixture models, and sampling methods. Each notebook corresponds directly to a chapter, so you can read a section of the book and then open the matching notebook to experiment with the actual Python implementation. To run the notebooks, you need Python 3 and a few standard scientific computing libraries: numpy and scipy for the mathematics, matplotlib for drawing charts, and sklearn if you want to load standard datasets. You can also run them for free in Amazon SageMaker Studio Lab, a cloud computing environment that requires only a free email registration, so you do not need a powerful personal computer to get started. The project is aimed at students and practitioners who are working through Bishop's textbook and want executable code alongside the theory. It is a companion to the book rather than a standalone learning resource and does not attempt to teach machine learning from scratch on its own.
Python implementations of all the machine learning algorithms from Bishop's Pattern Recognition and Machine Learning textbook, organized as 13 runnable Jupyter notebooks matched to each book chapter.
Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, NumPy.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.