Companion webpage to the book "Mathematics for Machine Learning"
Companion webpage to the book "Mathematics for Machine Learning". Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press. View the Project on GitHub View On GitHub Please link to this site using https://mml-book.com. Twitter: @mpd37, @AnalogAldo, @ChengSoonOng. We wrote a book on Mathematics for Machine Learning that motivates people to
Limitations of Interpretable Machine Learning Methods
Limitations of Interpretable Machine Learning Methods 2020-10-05 Preface This book explains limitations of current methods in interpretable machine learning. The methods include partial dependence plots (PDP), Accumulated Local Effects (ALE), permutation feature importance, leave-one-covariate out (LOCO) and local interpretable model-agnostic explanations (LIME). All of those methods can be used t
AutoML: Methods, Systems, Challenges (new book) – AutoML
AutoML: Methods, Systems, Challenges (first book on AutoML) Editors: Frank Hutter, Lars Kotthoff, Joaquin Vanschoren This is an open-access book; here is an entirely free complete PDF of the book, and a bibtex entry for it. Below, you can find the individual chapters and bibtex entries for them. If you would like to purchase a hard cover, please see Springer’s website for the book, or order the bo
Designing Distributed Systems
Explore Azure Get to know Azure Discover secure, future-ready cloud solutions—on-premises, hybrid, multicloud, or at the edge Global infrastructure Learn about sustainable, trusted cloud infrastructure with more regions than any other provider Cloud economics Build your business case for the cloud with key financial and technical guidance from Azure Customer enablement Plan a clear path forward fo
CSC2541 Scalable and Flexible Models of Uncertainty (Fall 2017)
University of Toronto, Fall 2017 CSC2541: Scalable and Flexible Models of Uncertainty Instructor: Roger Grosse Email: rgrosse at cs dot toronto dot edu (put CSC2541 in the subject line) Lecture: Friday, 2-4pm, in Bahen 1220 Office Hours: Tuesday, 1-3pm, in Pratt 290F (that’s the D. L. Pratt Building, not the E. J. Pratt Library!) You need to book a time slot through a URL which will be given out d
Welcome | R for Data Science
This is the website for the first edition of “R for Data Science”, published January 2017. This book is now out-of-date and instead we recommend the 2nd edition at http://r4ds.hadley.nz/ which was published in June 2023. R4DS teaches you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this b
Programming in Haskell - 2nd Edition
Graham Hutton Cambridge University Press Paperback: ISBN 978-1316626221; Kindle: ASIN B01JGMEA3U 318 pages, 120 exercises Description Haskell is a purely functional language that allows programmers to rapidly develop software that is clear, concise and correct. This book is based on the author's experience of teaching Haskell for more than twenty years. All concepts are explained from first princi
Deep Learning
An MIT Press book Ian Goodfellow and Yoshua Bengio and Aaron Courville The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. The deep learning textbook can now be ordered on Amazon. For up to date an
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