Year in Review

It's been a really interesting year.. I moved to the Bay Area. It's one thing to read about Silicon Valley or visit briefly. It's another to actually live out here and experience all it has to offer. This is the center of this data revolution everyone seems to be talking about. Obviously, if you can manage the ridiculously expensive housing out here and how much more expensive everything is out here, then you should be fine.

To wrap up the year, here is Jeff Leeks' Non-comprehensive list of awesome things other people did in 2014 . It has an rlang slant since he's a statistician.

I had more blog posts and traffic this year than each of the previous 3 year combined. Hoping this trend continues. Just looking at my traffic, it does appear there is a lot more interest in Data Science Education and immersive experiences like boot camps.

Going forward, I plan to do more tutorial style posts showing side projects or other interesting tech I encounter. 

I do want to spend more time delving into Deep Learning. Starting with the nuts and bolts and then moving to available libraries / implementations and sharing some of what I learn along the way... stay tuned 

Some more interesting links-4, Machine Intelligence, TDA, ipython notebooks

Most Topological Data Analysis tools are either stuck in academic research papers or Company intellectual property. DataRefiner might help to change that

Python for Exploratory Computing : Collection of ipython notebook showing python basics, statistics and advanced python topics

A collection of ipython notebooks on hacking security data 

This is the future of education Open Loop University, where your education is spread over several years. You'll have periods of work with schooling interlaced inbetween

Detailed infograph showing major players in the Machine Intelligence space

You should look at this if you're interested in the Quantified Self space

I've been looking for something like this. Instant temporary ipython notebooks hosted in the cloud

An extensive Deep Learning Reading list

Nice reading on Generative vs Discriminative Algorithms (Naive Bayes - Logistic Regression)