Into Deep...

Into Deep...

I have been busy going deep into data science. Once I have mastered the basics on Machine Learning and reviewed the main concepts on Data Analysis and Big Data through Coursera’s MooC’s I decided it was time to make one step forward. And for that, my next challenge is learning all about Deep Learning as the first step towards Artificial Intelligence. The first consequence is that I enrolled in Udacity’s Nanodegree Deep Learning Foundation. The Expected end date will be August of this year (2017).

If you ever did a course on the mentioned Platform you already know the deal, and if you did not, let me introduced a bit. Udacity was founded by a former Standford Professor, Sebastian Thrun He was completely focused on Artifical intelligence and specifically was keen on technology concerning autonomous self-driving cars. He saw the industry was growing but traditional education was too slow to adapt to professional’s market demanding. He, then, decided to start teaching the skills he thought, were needed in order to supply the market with those skilled professionals. He was so surprised by a number of people interested in what he was going to teach, that he finally give up his job and founded the school.

As a former Google employee, he partnered with them to teach app development and that attracted the attention of many people very quickly.

I came across the platform just before starting studying analytics. At that time they were not teaching anything related to data science. I enrolled to their Android development application as part of my learning curiosity to train my lateral thinking skills.

A few months ago I decided to give it a go, and with my surprise, I saw that nano degrees were on stage to stay. A Nanodegree is a medium term course that it is focused on a skill. They came in sizes from 3 months to one year. In my case, after considering Machine Learning Nanodegree I decided I should go a little forward and then choose Deep Learning as my subject.

I am right now about to start Project number 2. I will develop in following entries what I have already done and what I am up to.

But for know, I want to say that I loved what I am learning. I am struggling with the theory and the programming. Luckily for me, before starting I did their Linear Algebra Refresher MOOC and I had already started to learn some Python (I came from R programming).

So, as for know as a full-time student, I do research a lot on deep learning, maths, Python and other techniques that although I was aware of before starting I was not proficiency enough to be confident with.

I am also spending most of the time in slacks channel trying to grab any tiny URL, tip, book advice from other data science groups. I did many LinkedIn contacts from these students that allowed me to have in my feed good articles on the subject.

As for offline networking, I continued attending R ladies events in my city. OMG, these girls know a lot about the possibilities on R. I am learning every time I meet them or interact on slack. People are amazingly generous on what they know! I recommend any of you, girls, to go to any R ladies or PyLadies in your city!

As for my own, I think I need to write more often here on the blog what steps I am doing towards completing my objective which is change my career towards data science.