John Ramey Statistics and Machine Learning

Serverless API around Google Cloud Vision with the Serverless Framework

The Serverless Framework hit v1.0 (beta) recently after about a year of development. The framework has matured quickly to help devs build scalable applications without having to maintain servers. It aims to ease deployment via:

Installing TensorFlow on an AWS EC2 Instance with GPU Support

The following post describes how to install TensorFlow 0.6 on an Amazon EC2 Instance with GPU Support. I also created a Public AMI (ami-e191b38b) with the resulting setup. Feel free to use it.

Configuring IPython Notebook Support for PySpark

Apache Spark is a great way for performing large-scale data processing. Lately, I have begun working with PySpark, a way of interfacing with Spark through Python. After a discussion with a coworker, we were curious whether PySpark could run from within an IPython Notebook. It turns out that this is fairly straightforward by setting up an IPython profile.

Installing Python Data Science Stack on Yosemite

I was attempting to install the Python data-science stack within a fresh virtual environment on my Mac with OS X 10.10.1 (Yosemite) but encountered various frustrating errors. I logged my steps below that eventually yielded a successful installation.

MLB Rankings Using the Bradley-Terry Model

Today, I take my first shots at ranking Major League Baseball (MLB) teams. I see my efforts at prediction and ranking an ongoing process so that my models improve, the data I incorporate are more meaningful, and ultimately my predictions are largely accurate. For the first attempt, let’s rank MLB teams using the Bradley-Terry (BT) model.