CodeDotNet

This site is often under construction as it is used to expand my skills by exploring ideas and techniques for .NET, combining ASP.NET, Silverlight applications, web services, and a VB/C# desktop client to access selfsame web services.

Updates and Announcements

2017-11-14

Pinterest as a Publication Channel for Data Analytics

More as an experiment, rather an attempt at sharing code and ideas, I created a Pinterest board devoted to my personal data analytics work, done with Python, R, or F#, as well as reviews of books, and was quite surprised with the result.

The graphics could do with optimization, but otherwise...

2017-11-09

Deep Learning and Toolkits


Bookcover image for Fundamentals of Deep Learning As part of reading Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms by Nikhil Buduma, I was expecting to work through the code examples with my own data, and for the library, it recommended TensorFlow, which brings up competing alternatives, a primary one being Microsoft Cognitive Toolkit. Over the next few weeks, I will start exploring both in Python, as well as publishing some of the related work.

A minor note, the darksigma/Fundamentals-of-Deep-Learning-Book: Code companion to the O'Reilly "Fundamentals of Deep Learning" book is available on GitHub.

2017-10-12

Principal Component Analysis (PCA) on Stock Returns in R

Principal Component Analysis is a statistical process that distills measurement variation into vectors with greater ability to predict outcomes utilizing a process of scaling, covariance, and eigendecomposition.

MS Azure Notebook

The work for this is done in the following notebook, Principal Component Analysis (PCA) on Stock Returns in R, with detailed code, output, and charts. An outline of the notebook contents are below.

Overview of Demonstration

  • Supporting Material
  • Load Data: Format Data & Sort
  • Prep Data: Create Returns
  • Eigen Decomposition and Scree Plot
  • Create Principal Components
  • FVX using PCA versus Logistic Regression
  • Alternative Libraries: Psych for the Social Sciences