Five Things I Learned while Prototyping ML Papers
As data scientists, building on published research allows us to stay on the cutting edge and not reinvent the wheel. However, in most cases, this transfer is not trivial. My talk will provide you with a five-step workflow that has made this transfer easier for me.
What will the audience learn from this talk?
How can we access the gold mine that is published research in ML, stats, and related fields, and turn it into nuggets and even refined gold jewelry useful for us? In other words, how we can evaluate the relevance, quality and reproducibility of research papers for our specific work situation.
Does it feature code examples and/or live coding?
Prerequisite attendee experience level: