Saturday, December 23, 2017

A Quote That Strikes Me

“The basic project of art is always to make the world whole and comprehensible, to restore it to us in all its glory and its occasional nastiness, not through argument but through feeling, and then to close the gap between you and everything that is not you, and in this way pass from feeling to meaning. It's not something that committees can do. It's not a task achieved by groups or by movements. It's done by individuals, each person mediating in some way between a sense of history and an experience of the world.”

Robert Hughes, The Shock of the New

Saturday, February 4, 2017

Proverbial Thoughts

Current events have had proverbs rattling around in my head.

“Appear weak when you are strong, and strong when you are weak.”  - Sun Tzu

Can POTUS ever be weak?

Is this currently happening?

Is enemy of my enemy is my friend, or my enemy? - ancient proverb

So if Terror is our enemy, who are our friends?  Who is the enemy of Terror?  Peace?

“All warfare is based on deception. Hence, when we are able to attack, we must seem unable; when using our forces, we must appear inactive; when we are near, we must make the enemy believe we are far away; when far away, we must make him believe we are near.” 
― Sun Tzu, The Art of War

Are alternative facts just the deception in the war on institutions, rights, and environment?

The answers are irrelevant.

The reminder to stay vigilant in the protection of the important is the key.

Wednesday, November 9, 2016

Vote Every Day

Voting is not something that is done only a single day in November in certain years.

You vote every day with your actions and wallet.  With every action and purchase, you endorse the owners' views.  I encourage you to vote wisely every day.  Assure you align with the businesses you support both locally and nationally; especially those businesses that insert themselves into the political process.

Sunday, February 21, 2016

The Set Up - What tools will be implemented...subject to change :-)

Machine Learning is a hot topic currently.  Being a trendy there are many bright shiny tools out there to help distract from the process of getting good at my real goal: prediction.  To the end of being good at prediction the tech stack that I will implement is going to be limited.  It's far too easy to get distracted by the next new thing.  Staying focused on the end goal is key. is will be the code editor of choice.   Why?  There are not concrete reasons.  The soft reasons range from it's an opensource project that is widely used to it is customizable and as they like to boast, it's hackable.  There won't be much hacking in my future but packages have been added to support Python.

Python is my scripting language of choice.  Specifically Python 2.7.11 rather than the 3.x version.  I'm not geeked-out enough to understand the underlying differences between the two strains but in doing research on other downstream packages 2.x seemed to be a more compatible choice.

Python again is open source, stable and broadly used.  My initial working in econometrics was done in SAS, which is not open source and too expensive for a personal project.  Many use R which is also open source.   That would be a great choice too as it seems many online classes use R.

Python is implemented for many other reasons.  A few of them are that learning Python enables skills to attack other types of problems outside of machine learning and statistical analysis; it supports machine learning with pandas, numpy, ggplot, sklearn and many other modules; and finally given the timing I will attempt to use Google's Tensorflow (one shiny new toy) that comes with an easy to use Python interface.

Tensorflow can work on a Windows machine but is really linux based.  To that end I will also install Oracle VM VirtualBox with Ubuntu.  The virtual machine is pretty easy to set up and Tensorflow installs easily too.

A review of the stack above reveals a strong bias of mine.  I will default to broadly adopted open source tools.  There are good and bad points to this bias.  One bad one is that sometimes I find myself solving a simple problem that is difficult in open source and easy in a paid product.  It seems that paid products spend more resources on getting the UI/UX right.  However the good points (especially for broadly adopted tools) generally outweigh the bad.  Also if anything that gets developed turns into an income producing product the stack doesn't need to be changed to scale.

One final tool that I use extensively is Google. My coding skills are not all that advanced.  But I found that my problems are pretty common too.  Google points me to the right place, which often happens to be StackOverflow.   Get an account.  I'm mostly a consumer but hope to be able to contribute later.

Wednesday, February 17, 2016

Ginning up

It has been forever since my last post and seems like a good time to try to start writing again.  So...

I'm starting a new life project.  I am attempting to learn machine learning.  This is not a from scratch project as much of my Master's degree was very quantitative with heavy emphasis on econometrics.  Some of the basics of machine learning build on this foundation.  

Part of my problem is that I've forgotten many of the details of econometrics and statistics.  Thankfully the Internets have rushed to my rescue.  The Khan Academy has sessions on matrix algebra, calculus, statistics and history (for fun.)   Udacity and Coursera have many courses on data science, machine learning and visualizations.  Just to be a bit traditional like the old fart I am, Amazon has provided Machine Learning in Python by Michael Bowles.   There are also many new machine learning tools being open sourced by Google and others.

A reasonably large data set is also needed.  For that I'm using the Lending Club loan data which has initially provide 240Mb of data.

Wish me luck on my journey and feel free to participate.