Saturday, 24 September 2016

My first Shiny App: control charts

After having carefully followed the online official Shiny tutorial, I decided to make a quick try at making my very first Shiny App. I should say that I found myself very well with the explanation given and Shiny was definitely one of the libraries that took the less time for me to start using it. Of course I’m still not a master of Shiny by no means, but I feel more confident on how to use it and on what can be done with it.

Image 1

I’m working on an R project related to control charts and I was hinted to get to know Shiny, since it is very likely that the project will involve an interactive interface and Shiny fits the bill perfectly for this assignment. For this reason I took an afternoon for getting familiar with Shiny and build this App. Enough talk, let’s get to the App.

Tuesday, 13 September 2016

Some physical considerations on the dynamics of a skydiver

Recently, a friend went skydiving and, me being me, the first thing I could think about was making some physical considerations on his adventure :)

If you think of a falling object, at first you would think of it as falling with a constant acceleration of g. That is, you would neglect air drag. However, if you think of it, air drag is not exactly neglectable when describing the fall of a skydiver. If you neglect air drag, you would get an ever increasing speed which is not at all the case.

Let’s make some physical considerations:

We’ll assume that the only two forces acting on the skydiver are the force of gravity and the air drag. This is the resulting free body diagram:

Image 1

 

Monday, 12 September 2016

Getting AI smarter with Q-learning: a simple first step in Python

Yesterday I found an “old” script I wrote during a morning in the last semester. I remember being a little bored and interested in the concept of Q-learning. That was about the time Alpha-Go had beaten the world champion of Go and by reading here and there I found out that a bit of Q-learning mixed with deep learning might have been involved.

qlearning

Sunday, 11 September 2016

Building a (reusable?) deep neural network model using Tensorflow

I’ve been experimenting for more than two months with Tensorflow, and while I find it a bit more “low level” if compared to other libraries for machine learning, I like it and hopefully I am getting better at using it. During the learning process I found some minor “obstacles” so I decided to write a short tutorial on how to use this amazing deep learning library.