Chapter 10 Acknowledgements
Writing this book was (and still is) a lot of fun. But it is also a lot of work and I am very happy about the support I received.
A big thanks goes to Verena Haunschmid for writing the section about LIME explanations for images. She works in data science and I recommend following her on Twitter: @ExpectAPatronum. I also want to thank all the early readers who contributed corrections on Github!
Furthermore, I want to thank everyone who created illustrations: The cover was designed by my friend @ArbeitAmText. The graphics in the Shapley Value chapter were created by Abi Aryan, using icons made by Freepik from Flaticon. The awesome images in the chapter about the future of interpretability were designed by @TopeconHeroes. Verena Haunschmid created the graphic in the RuleFit chapter. I would like to thank all researchers who allowed me to use images from their research articles.
In at least three aspects, the way I published this book is unconventional. First, it is available both as website and as ebook/pdf. The software I used to create this book is called
bookdown, written by Yihui Xie, who created many R packages that make it easy to combine R code and text. Thanks a lot! Secondly, I self-publish the book on the platform Leanpub, instead of working with a traditional publisher. And third, I published the book as in-progress book, which has helped me enormously to get feedback and to monetize it along the way. Many thanks to leanpub for making this possible and handling the royalties fairly. I would also like to thank you, dear reader, for reading this book without having a big publisher name behind it.
I am grateful for the funding of my research on interpretable machine learning by the Bavarian State Ministry of Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B).