Next time you’re at King’s Cross station, take a moment to think about this. Just yards from where you’re standing, the world’s most advanced artificial intelligence (AI) technology is being developed — by a London company called DeepMind.
Nowadays, I spend more time in Bash shell, typing longer commands. One of my new year resolutions for this year is to stop using left/right arrow keys to move around in the command line. I learned a few shortcuts a while ago.
HTTP headers are an important way of controlling how caches and browsers process your web content. But many are used incorrectly or pointlessly, which adds overhead at a critical time in the loading of your page, and may not work as you intended.
Data science, also known as data-driven decision, is an interdisciplinery field about scientific methods, process and systems to extract knowledge from data in various forms, and take descision based on this knowledge. A data scientist should not only be evaluated only on his/her knowledge on mahine learning, but he/she should also have good expertise on statistics. I will try to start from very basics of data science and then slowly move to expert level. So let’s get started.
Do you think of yourself as a Python programmer, or a Ruby programmer? Are you a front-end programmer, a back-end programmer? Emacs, vim, Sublime, or Visual Studio? Linux or macOS? If you think of yourself as a Python programmer, if you identify yourself as an Emacs user, if you know you’re better than those vim-loving Ruby programmers: you’re doing yourself a disservice. You’re a worse programmer for it, and you’re harming your career. Why? Because you are not your tools, and your tools shouldn’t define your skillset.
I have a major pet peeve that I need to confess. I go insane when I hear programmers talking about statistics like they know shit when it’s clearly obvious they do not. I’ve been studying it for years and years and still don’t think I know anything. This article is my call for all programmers…
In December 2017, researchers at Google and MIT published a provocative research paper about their efforts into “learned index structures”. The research is quite exciting, as the authors state in the…
D. Karger, and D. Quan. Web Semantics: Science, Services and Agents on the World Wide Web, Selcted Papers from the International Semantic Web Conference, 2004 - ISWC, 2004, Hiroshima, Japan, 07-11 November 2004, 3 (2-3):
147-157(October 2005)