- Having read up a little on Bayesian inference as part of a statistics & probability course I’ve been working through, I found this Towards Data Science guide pretty useful. Covering the same ground as the stats & probability material, but providing additional context as to how it’s applied when developing machine learning features – my main purpose for studying the material in the first place.
- On the subject of machine learning (and AI), here’s a huge and handily curated reading list to get you started if you’re interested in learning more.
- Since I seem to spend a lot of time facilitating, leading or just giving presentations and webinars, I’m always open to ways in which I can improve my presentation skills. This post on using Children’s Book Storytelling Techniques seemed insightful, and I’ll likely try to build some of the learnings into my next slide deck.
- I loved reading this post on different modelling techniques for complex systems also. I’m working on the designs for some fairly major TestRail improvements currently, so identifying some better tools for thinking about and communicating the systems those designs are intended to deliver will be very helpful. I expect to return to this post and the links and book citations therein, frequently. (While thinking about how to model some of my behaviours, I made a note of this site also: http://agilemodeling.com/)