Category: Machine Learning

Using Tensorboard with Multiple Model Runs
I tend to use Keras when doing deep learning, with tensorflow as the backend. This allows use of tensorboard, a web interface that will chart loss and other metrics by training iteration, as well as visualize the computation graph. I noticed tensorboard has an area of the interface for showing different runs, but wasn’t able […]

StirTrek 2018 Talk: Machine Learning in R
I had the chance to speak at StirTrek 2018 about Machine Learning in R. I have been to StirTrek before, but it’s been a few years. The conference has really grown, as there are over 2000 attendees now. I was in the 3:30 timeslot. I talked in a full theater and they broadcast the talk […]

Installing pymc on OS X using homebrew
I’ve been working through the following book on Bayesian methods with an emphasis on the pymc library: However, pymc installation on OS X can be a bit of a pain. The issues comes down to fortran… I know. The version of gfortran in newer gcc implementations doesn’t work well with the pymc build, you need […]

The Math of Machine Learning
(hover for CC attribution) One of the challenges of data science in general is that it is a multidisciplinary field. For any given problem, you may need skills in data extraction, data transformation, data cleaning, math, statistics, software engineering, data visualization, and the domain. And that list likely isn’t inclusive. One of the first questions […]

An Overview of Machine Learning in R
I presented at the Cleveland SciPy/Julia/R Data Science Group on 6/14. The talk is a fairly highlevel introduction to some of the machine learning methods and packages available in R. Here is the video: Here are the slides. Here are the notebooks.