According to a word cloud of my Facebook timeline, I use the word “zealand” a lot. Or at least often enough to make the cut. Also, according to a word cloud of my Facebook timeline, I do not use the word “new” very often at all. Which is weird, because I can’t think of a time I have ever written the word “zealand” when it wasn’t preceded by “new.”
… a more careful understanding of the data might suggest that Amazon is a lot more important than it may seem at first glance, and that the lack of benefit from “savings” in other areas can easily be understood when the data is explored more thoroughly. As a data scientist, that’s the kind of thing I need to be able to do in order to tell a story that is not only compelling, but accurate.
I left last week’s PyData Meetup with more questions than answers. Questions like “why does that neural net I just wrote perform the way it does?” So, with a couple of weeks left until the next project is due, I decided to go back and revisit the second half of the neural networks topic before moving forward.
Tonight I joined the first Southern California PyData meetup. It featured two speakers discussing how to better understand the predictions made by machine-learning models, and why it might be important to do so. I was impressed by the capabilities of the packages demonstrated and the likely importance of having such capabilities as we move forward with deep learning-based automation that could cause catastrophic results if it fails in unexpected ways.
There is little here that I could not learn on my own. But I find that it’s useful to learn along with others, and the structure that programs like this provide can be useful, so long as it isn’t too expensive. For myself, the structure and ability to discuss issues and problems with others were the key things that made this summer’s effort worth the $600.