What is this?
A collection of Python implementations of the algorithms described in Christopher Bishop's book 'Pattern Recognition and Machine Learning' (PRML), organized as Jupyter notebooks by chapter.
A collection of Python implementations of the algorithms described in Christopher Bishop's book 'Pattern Recognition and Machine Learning' (PRML), organized as Jupyter notebooks by chapter.Is this a library or a set of tutorial notebooks?It's a set of Jupyter notebooks, one per chapter, meant to be read and run alongside the book rather than imported as a package.
It's a set of Jupyter notebooks, one per chapter, meant to be read and run alongside the book rather than imported as a package.How do I view or run the notebooks?Open the chapter notebooks via nbviewer or another notebook viewer, or run them live using Amazon SageMaker Studio Lab, a free AWS-hosted environment that needs email registration.
Open the chapter notebooks via nbviewer or another notebook viewer, or run them live using Amazon SageMaker Studio Lab, a free AWS-hosted environment that needs email registration.What do I need installed to run the code myself?Python 3 with numpy and scipy are required; jupyter and matplotlib are optional for running/plotting notebooks, and sklearn is optional for fetching datasets.
Python 3 with numpy and scipy are required; jupyter and matplotlib are optional for running/plotting notebooks, and sklearn is optional for fetching datasets.Do I need to know the book to make sense of this repo?Yes, the notebooks implement specific algorithms from the book and are organized chapter by chapter (ch1 through ch13), so they're most useful alongside a copy of PRML.
Yes, the notebooks implement specific algorithms from the book and are organized chapter by chapter (ch1 through ch13), so they're most useful alongside a copy of PRML.How is the content organized?By the 13 chapters of the PRML book, from Introduction through Sequential Data, each with its own notebook covering that chapter's algorithms.
By the 13 chapters of the PRML book, from Introduction through Sequential Data, each with its own notebook covering that chapter's algorithms.Can I run this without installing anything locally?Yes, Amazon SageMaker Studio Lab provides a free hosted environment where you can open and run the notebooks after registering with an email address.
Yes, Amazon SageMaker Studio Lab provides a free hosted environment where you can open and run the notebooks after registering with an email address.What are similar projects?Probabilistic-Programming-and-Bayesian-Methods-for-Hackers, Book4_Power-of-Matrix, homemade-machine-learning, Kalman-and-Bayesian-Filters-in-Python, CV, 100-Days-Of-ML-Code, d2l-en, mml-book.github.io
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers, Book4_Power-of-Matrix, homemade-machine-learning, Kalman-and-Bayesian-Filters-in-Python, CV, 100-Days-Of-ML-Code, d2l-en, mml-book.github.ioWhich topics is it in?notebook, jupyter, jupyter-notebook, statistics, python3, academic, algorithms, jupyter notebook, data-science, book, learning-resources, learning
notebook, jupyter, jupyter-notebook, statistics, python3, academic, algorithms, jupyter notebook, data-science, book, learning-resources, learning