In one of my projects to obtain more email signups for my role at ODSC, I create a downloadable PDF, the “ODSC Guide to Deep Learning,” which contained highlighted blogs and recorded ODSC talks related to machine learning, among other pieces of content. At the end, I wrote a letter from the editor with my thoughts on machine learning.
Graphic design was done by Paxtyn Merten and Ava Burcham.
Content compilation and descriptions by me.
My first exposure to the world of machine learning and deep learning was a few years ago during my time at MIT Professional Education, where I worked in marketing to promote many of their short programs. The breadth of their offerings was impressive, but it was their courses with Vivienne Sze and Regina Barzilay that intrigued me the most.
Machine learning and deep learning were both completely new to me. My only knowledge of data science was being able to do some basic sentiment analysis in R during my time as a graduate student at Boston University. Aside from that, I knew the terms, but that was it — they were terms under the AI umbrella to me. Now that I’ve been with ODSC for over a year, however, I see that “Machine Learning” and “Deep Learning” are umbrellas in their own right.
Deep learning fascinates me. As a former psychology major and almost-psychologist in the past, the concept of neural networks is compelling. Although I find that there’s an art to content curation that makes understanding the brain quite useful, such as contemplating human motivation or the repercussions of media use, my (albeit now-limited) psych knowledge is only applicable at that level. This emerging field of study, rapidly growing in breadth and depth, is called deep learning for a reason — just like the brain, AI has layers and priorities. We’re only going deeper from here!
Additionally, deep learning is incredibly reflective of how far we’ve come with technology. The amount of computational power required to run DL initiatives is impressive, and thus we need specialized computers and data centers to handle it. In my place of work, I sit near another company that uses ML and DL on a regular basis, and I’ll hear their computers from across the office when they’re running an overnight algorithm. If my laptop had emotions, it would feel inadequate in comparison.
I appreciate how involved many DL engineers are with academia — even the working data scientist still reads papers for fun. I’ve spoken to a few practitioners who will print out papers and leave them in their bags to read on public transport or during lunch breaks. I suppose it’s expected in a field that’s so research-oriented. Now, if only us marketing professionals could be so engaged in research and academia.
I hope that the resources provided in this guide prove to be helpful to you, your organization, your friends, and whoever else’s interest might be piqued. Each writer and speaker in this guide is an expert, and I’m sure that the insights they provide will enhance your knowledge and practice of deep learning. Feel free to let us know what you’re up to at work, in academia, or just for fun — we’d love to share it!