Title: It’s Time Deep Learning Learned from Software Engineering
Date: Monday November 2, 1:00 PM ET/10:00 AM PT
Duration: 1 hr
Speaker: Jeremy Howard, Founding Researcher, fast.ai; Distinguished Research Scientist, University of San Francisco
This topic is very relevant for me, since as a data analytics graduate student at UCF, I recently used a software build automation tool on one of my projects, and I have to say the experience was not pleasant. I was asked to execute a few commands to setup the project, which didn’t go well. Moreover, I had to keep in mind that before committing any changes to the project I had to run other commands to ensure compliance. Honestly, I had a hard time keeping up with these instructions, I found it hard to understand what was going on, or whether I’d done something very wrong that there was no way back, and why were so many new files being created, while other being deleted? What I am trying to say is this, for someone who is looking to start a career as a data scientist, dealing with the different algorithmic families and their implementations, while enjoyable, is often hard and time consuming, so what would be your advise on how to incorporate best practices from software engineering, without being totally consumed by them? Thanks, Mina
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