In my last post, I described user- and item-based collaborative filtering which are some of the simplest recommendation algorithms. For someone who is used to conventional machine learning classification and regression algorithms, collaborative filtering may have felt a bit off. To me, machine learning almost always deals with some function which we are trying to maximize or minimize. In simple linear regression, we minimize the mean squared distance between our predictions and the true values. Logistic regression involves maximizing a likelihood function. However, in my post on collaborative filtering, we randomly tried a bunch of different parameters (distance function, top-k cutoff) and watched what happened to the mean squared error. This sure doesn’t feel like machine learning.

Recently I came across provocatively titled “Machine Learning in Monitoring is BS” and decided to reply but the response came out longer than typical comment so I posted it separately.

Note: The video is the interview only. The audio podcast has the intro, interview, and data science learning club activity explanation.

A recent trend in Deep Learning are Attention Mechanisms. In an interview, Ilya Sutskever, now the research director of OpenAI, mentioned that Attention Mechanisms are one of the most exciting advancements, and that they are here to stay. That sounds exciting. But what are Attention Mechanisms?

This is especially for the excel lovers! In this blog, we will see few of the new and exciting data visualization features of Excel 2016.