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!

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# Category: Machine Learning

## Machine Learning & Gradient Boosting w/xgboost

## Installing TensorFlow on CentOS

## Presentation on Linear Algebra in R

Google released TensorFlow as open source for community use and improvement. From the site: “TensorFlowâ„¢ 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 github gist. Feel free to comment if you find something I missed:

** Updated 8/18/2016 for TensorFlow 0.10*

** Updated gist 10/18/2016 to correct typo in epel-release*

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 the techniques in R.

In order to limit the scope of the talk, I focused on matrices, vectors and basic operations with them. There is a practical example that uses a machine learning algorithm, but it’s just to show how R handles a more involved equation with matrices. The talk is not an attempt to teach machine learning.

The slides are available here, and comments or suggestions are welcome.