If you’re just starting out in data science, machine learning, deep learning (DL), etc, then I can’t recommend Udacity enough. Years ago, after I graduated with my PhD in physics, I wanted to get in on AI research in industry, say at Google or Facebook. As a stepping stone, I took a job as a data scientist at WWE developing predictive models for various types of customer behaviors on the WWE network. Early on at this job, I serendipitously stumbled upon and enrolled in Udacity’s first offering of their DL nanodegree.

Every morning, I would wake up at 5am, get out the door by 6am, and to work by 7am. The place was quiet. My manager and coworkers wouldn’t arrive until 9:30-10:00, giving me ample time to learn and experiment with deep learning techniques. Even better, for the rest of the day, I had the opportunity to directly apply what I was learning to the various projects s I was working on at WWE.

Now I’m a research scientist at the intersection of wearables and healthcare, focused on leveraging deep learning techniques for things like human activity recognition (HAR) and detecting early signals of disease. Very cool stuff, and in part owed to what I learned during that nanodegree.

Recently, I saw that Udacity/Intel were sponsoring scholarships for a new nanodegree on deploying deep learning models directly on “edge” devices (obviously I applied). Just like the DL degree almost 3 years ago, I expect this to be a game changer: when doing HAR using wearable devices, we often develop complex data pipelines to get the data from the device, to a phone (via Bluetooth), to somewhere in the cloud. In this nanodegree, I hope to learn how to simplify this whole process by directly deploying models on wearable devices themselves.

Phase 1 of the nanodegree started today.

As per usual, I will be taking notes and documenting my experience in this blog.

Cheers!