Tim’s Blog
-
The Math of Machine Learning
(hover for CC attribution) One of the challenges of data science in general is that it is a multi-disciplinary 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 high-level 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.
-
Machine Learning & Gradient Boosting w/xgboost
I presented at the Cleveland R User Group on using xgboost in R. Slides are available here. Code (jupyter notebooks) are here. Feedback welcome. Enjoy!
-
Installing TensorFlow on CentOS
Google released TensorFlow as open source for community use and improvement. From the site: “TensorFlow™ is an open source software library for numerical computation using data flow graphs.” The instructions on tensorflow.org are aimed at Ubuntu and OS X. I had a need to install it on CentOS so I documented the steps in a […]
-
Presentation on Linear Algebra in R
At our January meeting, I presented on Linear Algebra basics in R. I have been taking the Andrew Ng’s Stanford Machine Learning course. That course primarily uses Matlab (or Octave, and open source equivalent), and machine learning involves manipulating and calculating with matrices. Naturally, being an R person, I have been working with some of […]
Got any book recommendations?