Maxpowerwastaken.github.io Website Review

Tue, Jul 18, 2017 Introduction The MNIST digits dataset is a famous dataset of handwritten digit images. You can read more about it at wikipedia or Yann LeCun’s page. It’s a useful dataset because it provides an example of a pretty simple, straightforward image processing task, for which we know exactly what state of the art accuracy is. I plan to use this dataset for a couple upcoming machine learning blog posts, and since the first step of pretty much any ML task is ‘explore your data,’ I figured I would post this first, to Mon, Jun 19, 2017 Introduction Python’s Pandas library for data processing is great for all sorts of data-processing tasks. However, one thing it doesn’t support out of the box is parallel processing across multiple cores. I’ve been wanting a simple way to process Pandas DataFrames in parallel, and recently I found this truly awesome blog post. It shows how to apply an arbitrary Python function to each Tue, Jun 13, 2017 Tom Augspurger, one of the maintainers of Python’s Pandas library for data an*lysis, has an awesome series of blog posts on writing idiomatic Pandas code. In fact you should probably leave this site now and go read one of those blog posts, they’re really good. His post on Performance has an especially interesting tip: “You rarely want to use DataFrame.apply and almost Sun, Jan 8, 2017 My new favorite dataset is the trove of NFL play-by-play data do*nloadable in R now through nflScrapR. However, in my previous post ‘Exploring NFL Play by Play Data with NFLScrapR’, I noticed something that didn’t look right with the GoalToGo variable, based the plot below. The problem here is that the probability distribution in the right sub-plot says that for GoalToGo situations (i.e. ‘3rd and Goal’), a play never gained more than 10 yards. Fri, Jan 6, 2017 In this post I’d like to dive into NFL yards-per-play (ypp) outcomes, looking at ypp distributions under different conditions. I’ll be using NFL 2009-2015 play-by-play data that I’ve do*nloaded using the awesome R library nflscrapR For an