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
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"Designing Data-Intensive Applications"は濃密すぎる一冊だったので2018年の自分にも読んでもらいたい
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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
Fundamentals of Data Visualization
Welcome This is the website for the book “Fundamentals of Data Visualization,” published by O’Reilly Media, Inc. The website contains the complete author manuscript before final copy-editing and other quality control. If you would like to order an official hardcopy or ebook, you can do so at various resellers, including Amazon, Barnes and Noble, Google Play, or Powells. The book is meant as a guid
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
The Feynman Lectures on Physics
Caltech's Division of Physics, Mathematics and Astronomy and The Feynman Lectures Website are pleased to present this online edition of Now, anyone with internet access and a web browser can enjoy reading2 a high quality up-to-date copy of Feynman's legendary lectures. This edition has been designed for ease of reading on devices of any size or shape; text, figures and equations can all be zoomed
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Deep Learning