shemsa / mlrefined

Jupyter notebooks, Python and MATLAB code examples, and demos from the textbook "Machine Learning Refined" (Cambridge University Press). See our blog https://jermwatt.github.io/mlrefined/index.html for interactive versions of many of the notebooks in this repo.

Machine Learning Refined Jupyter notebooks

This repository contains Jupyter notebooks and Python files associated with the Machine Learning Refined blog, a set of supplementary resources for the textbook Machine Learning Refined (Cambridge University Press). Visit http://www.mlrefined.com for free chapter downloads and tutorials, and our Amazon site for details regarding a hard copy of the text.

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Python 3 and a number of standard Python packages for scientific computing (numpy, matplotlib, and Jupyter being the most common) are required to use the notebooks in this repo, as well as a latex. To get these basic libraries we highly recommend downloading the Anaconda Python 3 distribution.

Many of these notebooks also require the Automatic Differentiator autograd which can be installed by typing the following command at your terminal

  pip install autograd

Note: to pull a minimial sized clone of this repo (including only the most recent commit) use a shallow pull as follows

  git clone --depth 1 https://github.com/jermwatt/mlrefined.git

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This repository is in active development by Jeremy Watt and Reza Borhani - please do not hesitate to reach out with comments, questions, typos, etc.

The material in this repository is not to be distributed, copied, or reused without written permission from the authors.