Explanatory Model Analysis. Interpretable Machine Learning. Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression.
Intro Stat w/Randomization & Sim. Gaussian Processes for Machine Learning: Book webpage. Carl Edward Rasmussen and Christopher K.
I. Williams The MIT Press, 2006. ISBN 0-262-18253-X. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. MEGA. Stan User’s Guide. About this user’s guide This is the official user’s guide for Stan.
It provides example models and programming techniques for coding statistical models in Stan. Part 1 gives Stan code and discussions for several important classes of models.Part 2 discusses various general Stan programming techniques that are not tied to any particular model.Part 3 introduces algorithms for calibration and model checking that requie multiple runs of Stan.The appendices provide a style guide and advice for users of BUGS and JAGS. In addition to this user’s guide, there are two reference manuals for the Stan language and algorithms. The Stan Reference Manual specifies the Stan programming language and inference algorithms. Statistical Rethinking with brms, ggplot2, and the tidyverse. I love McElreath’s Statistical Rethinking text.
It’s the entry-level textbook for applied researchers I spent years looking for. McElreath’s freely-available lectures on the book are really great, too. However, I prefer using Bürkner’s brms package when doing Bayeian regression in R. It’s just spectacular. I also prefer plotting with Wickham’s ggplot2, and coding with functions and principles from the tidyverse, which you might learn about here or here. So, this project is an attempt to reexpress the code in McElreath’s textbook. Why this? I’m not a statistician and I have no formal background in computer science. With that in mind, one of the strengths of McElreath’s text is its thorough integration with the rethinking package. In addition, McElreath’s data wrangling code is based in the base R style and he made most of his figures with base R plots. To be clear, students can get a great education in both Bayesian statistics and programming in R with McElreath’s text just the way it is.
NLTK Book. Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit Steven Bird, Ewan Klein, and Edward Loper This version of the NLTK book is updated for Python 3 and NLTK 3.
The first edition of the book, published by O'Reilly, is available at (There are currently no plans for a second edition of the book.) 0. 1. RLbook. Dimitri P.
Bertsekas Lecture slides for a course in Reinforcement Learning and Optimal Control (January 8-February 21, 2019), at Arizona State University: Slides-Lecture 1, Slides-Lecture 2, Slides-Lecture 3, Slides-Lecture 4, Slides-Lecture 5, Slides-Lecture 6, Slides-Lecture 7, Slides-Lecture 8, Slides-Lecture 9, Slides-Lecture 10, Slides-Lecture 11, Slides-Lecture 12, Slides-Lecture 13. Videos of lectures from Reinforcement Learning and Optimal Control course at Arizona State University: (Clisk around the screen to see JUST THE VIDEO, or JUST THE SLIDES, or BOTH SIMULTANEOUSLY).
Algorithms of Reinforcement Learning: A new book by Csaba Szepesvari. Why this book?
There exist a good number of really great books on Reinforcement Learning. So why a new book? I have to confess: The book arose from selfish reasons: I wanted a short book, which nevertheless contained the major ideas underlying state-of-the-art RL algorithms, a discussion of their relative strengths and weaknesses, with hints on what is known (and not known, but would be good to know) about these algorithms. If I succeeded, time will tell. Or, you can, by sending me an e-mail! Abstract. BBC Visual and Data Journalism cookbook for R graphics. Feature Engineering and Selection: A Practical Approach for Predictive Models. Notes to readers: A note to readers: this text is a work in progress.
It will eventually be published in this format as well as a more traditional physical medium by Chapman & Hall/CRC. We’ve released this initial version to get more feedback beyond what our excellent reviewers and editor have already provided. Feedback can be given at the GitHub repo Copyediting has not been done yet so read at your own risk. Right now, we are primarily interested in the quality and organization of the content but are open to all of your thoughts. Code and data will be provided but not until everything has been finalized. Thanks for taking the time to read this. The Hundred-Page Machine Learning Book [The Hundred-Page Machine Learning Book]
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To avoid buying counterfeit on Amazon, click on See All Buying Options and choose Amazon.com and not a third-party seller. Sutton & Barto Book: Reinforcement Learning: An Introduction. Second Edition (see herefor the first edition) MIT Press, Cambridge, MA, 2018 Buy from Amazon ErrataFull Pdf pdf without margins (good for ipad)New Code Old Code Solutions -- send in your solutions for a chapter, get the official ones back (currently incomplete)Teaching AidsLiterature sources cited in the book Latex Notation -- Want to use the book's notation in your own work?
Download this .sty file and this example of its use Help out! If you enjoyed the book, why not give back to the community? I am collecting a public directory with pdf files of the original sources cited in the book. Python Data Science Handbook. Recommended Books. These are the best books that I've read in terms of helping me become a better data scientist.
Purchasing books from these links helps support Data Science Bytes. Data Science and Machine Learning Data Science for Business A great description of many machine learning techniques with discussions of their application to real problems.Python for Data Analysis A thorough and well written description of the python data science stack by one of the people who made it what it is.Building Machine Learning Systems with Python This book goes in depth into the process of building machine learning systems, including feature extraction, evaluating the results and iterating on them to improve performance.
Unfortunately the code available online as a supplement for this book doesn't match well with the code in the book and doesn't always work without modification.Effective Java Effective Java is a masterpiece and a classic in the field of computer science. R for Data Science.