Here is the new edition of my abridged Twitter timeline, RSS feeds and web musings.
In case you still work with Common Lisp, the Common Lisp Standard Draft is available as a paginated PDF (1356 pages). Clearly, this is some big stuff, even for a lightweight reader such as OS X Preview, but it may replace the Hyperspec which is more about 2,300 HTML pages.
Mathematics and Computation (PDF, 300+ pages), by Avi Wigderson, is all about modern computing science with a bit of heavy math (or the other way around), including cryptography, randomized algorithms, distributed or quantum computing, information theory, artificial intelligence (yeah, it’s back to fashion!). Unrelated to this, Mathematics for Machine Learning (still work in progress), by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong., aims to provide the core mathematical concepts that are essential to understand and/or apply machine learning techniques. The final book should remain available free of charge. And if you have time and inclination, you can also read A Primer of Mathematical Writing, by Steven G. Krantz, on arXiv.
This Python notebook is a must-have companion for anyone interested in using Pandas effectively. Rather than a series of tips and tricks or an annoying list of commands, this notebook summarizes the use of many Pandas-related functions to real-world examples. I think this is a great way to start learning Pandas or to consolidate previous knowledge.
Here is a nice article published on Medium (the original article is available on the UW Interactive Data Lab): Multiple Perspectives on the Multiple Comparisons Problem in Visual Analysis. By the way, you can follow one of Jeffrey Heer’s excellent course on data visualization, for instance CSE442 Data Visualization.
In 1999, I asked David Gerrold to write a "future of computing" prediction for the magazine where I was Technology Editor. Here's what he wrote. pic.twitter.com/UAMM0Pm4W6— Esther Schindler (@estherschindler) March 28, 2018
I started learning a bit of Rust a month ago, after having read one of Evan Miller’s nice review, an O’Reilly ebook, and part of the online documentation which is awesome. I thought at that time that support for numerical computing was lacking in the current implementation and existing packages. However, there’s now a native port of Apache Arrow, which looks promising.
If you are using Emacs for GTD and stuff like that—i.e., you use Emacs for its Org support—you will likely be interested by the recent series of posts published by John Goerzen.
Fabian Pedregosa published a very enlightening tutorial on optimization algorithms, with an interactive rendering à la distill. Among the many references, one of the recommended textbook is Optimization Models, which looks pretty interesting, even if I stand by an old edition of Kenneth Lange’s Optimization book.
And, finally, here is another interactive D3 website to learn graph theory in 16 parts.
Tindersticks • Simple Pleasure