Der er noget, der er råddent i Danmark.
This semester I will be serving in OU COB Research Colloquium. We organize research presentations in college of business. As part of my duties I was tasked with creating posters. I considered my options and decided that creating new posters every other week was too much work. So I created a tex file that grabs... Continue Reading →
I am doing a discrete choice experiment design for a project that we are just starting. The idea is to present screenshots of various app's in an app store and ask the participants to pick one. For this, we need screenshots of app's with key variables changed. The problem is that, unless you are careful... Continue Reading →
Plotly presents a visually pleasing way to create 3D plots in R and Python.
Presented our paper in Hawaii, the paper was nominated for best paper. The paper is about add-on content.
In Hawaii for HICSS 50. So far so good. Have seen old friends, met new friends and am enjoying Panoply security competition. Paper being nominated as best paper does not hurt either. Hawaii
I use AWS to run the various scripts used in data collection. I have been paying about $20 each month for a t1 micro instance since my free tier and academic credits ran out. That is quite a price to pay for 1 core and about 600MB ram. Maybe AWS is not the best option... Continue Reading →
I often use data from various sources and sometimes have to get creative about transforming the data into a format that I can use easily. So far I have done this mainly with R (and with Python from time to time). The other day, Dr. Mazzolla pointed to OpenRefine, an application for data clean-up and... Continue Reading →
When I teach R, I always caution the audiences about the quirks of R programming. It is very typical for someone with a background in Java or Python to write code in R that will take forever to execute (like I used to do, and sometimes still do). I warn the students about loops and... Continue Reading →
Google open sourced TensorFlow (TF), a distributed machine learning library, in November. The basic idea is that, you build your ML process into a graph and let TF handle the running and distribution of the work between cores. Be it cores in your CPU or GPU, TF has you covered. The dataflow graph works much... Continue Reading →
Irfmeister's Adventures 