Data Analytics Workouts

As part of reading Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms by Nikhil Buduma , I was expecting to work through some of the code examples with my own data, and for the examples, 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.

I was exploring Jupyter notebooks , that combines live code, markdown and data, through Microsoft's implementation, known as MS Azure Notebooks , putting together a small library of R and F# notebooks . As Microsoft's FAQ for the service describes it as : ...a multi-lingual REPL on steroids. This is a free service that provides Jupyter notebooks along with supporting packages for R, Python and F# as a service. This means you can just login and get going since no installation/setup is necessary. Typical usage includes schools/instruction, giving webinars, learning languages, sharing ideas, etc. Feel free to clone and comment... In R Azure Workbook for R - Memoisation and Vectorization Charting Correlation Matrices in R In F# Charnownes Constant in FSharp.ipynb Project Euler - Problems 18 and 67 - FSharp using Dynamic Programming