So here I am after trying for a long time to not learn Python learning Python. It just seems like I might get a hit or two more on my blog with some Python content. Well whats the first thing I need to figure out aside from getting it up and running in my environment and installing some libraries… Thats right, find a numerical computing library and see how it ticks.
Lets just start with my environment, because I painstakingly chose one.
In this article we are going to cover a simple version of Gradient Descent. It is important to note that this version of gradient descent is using Sum of Squares as its cost function to reduce. This implementation utilizes vectorized algorithms. Lets start off with…
So I wrote an article earlier “Linear Regression From Scratch”. Many folks have pointed out that this is in fact not the optimal approach. Now being the perfectionist I decided to re-implement. Not to mention it works great in my own libraries. The following article discussing converting the original code into code that uses linear algebra. Beyond this, it still works in PCL for xamarin, Hoo-Rah Xamarin!
So today we will do a quick conversion from mathematical notations of Algebra into a real algorithm that can be executed. Note we will not be covering gradient descent, but rather only cost functions, errors and execution of these to provide the framework for gradient descent. Gradient descent has so many flavors that it deserves its own article.
So I’ve been working on building some interesting visualizations with open data. Today I get to show off a really interesting one, not only will we discuss the visualization in depth, but also dive into how I built it. And here it is, the top 10 bookings in Miami where the legend is in descending order for most common bookings holistically.
Here is a recorded version of an in-person training I have been doing. Enjoy. I end up coming back to this myself even for reference.
This episode is all about performing data manipulation to derive raw insights from your data using the R programming language. Data manipulation is the core to anything and everything you do in business intelligence and machine learning. This episode sets the base for all R based intelligence sessions from here on out.
This article is a video tutorial on introduction to the very bare basics of R. Its a bit dry, but it is the underlying components of everything covered in the interesting stuff. Can’t do cool stuff without understanding the basics first.
Ever wonder the difference between R and Microsoft R? Considering learning R as a programming language? You should probably watch this video. It is the first in a 4 part series to give you the jump start you need to becoming a professional data scientist with R.
These days I need to make videos instead of written articles, so I am going to post a few of those here.
In this video we will do an initial exploratory analysis on a water flow data set that came from a prototype that I built. The prototype consists of a water pump, a valve and a flow meter. The data set exists in SQL Azure. We will use R and R Studio to perform the analysis from an Azure virtual machine.