Publications & Writing
Perspectives on data products, competitive intelligence, and product-led growth from the Mediumroast team.
Roasting Plant Coffee, here at San Francisco International Airport, employs their JavaBot (video story for the birth of the JavaBot) to deliver you a “bespoke” coffee brew delivered a cup at a time just in time. In a very real sense when you enter one of their stores you’re insid
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As a product manager, one of my ongoing tasks is to keep an eye on what competitors are doing, which often means diving into competitive market research. When I was at Georgia Tech working on my master’s thesis, I focused on using analytics and machine learning to evaluate
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In my last post, I tried to tackle the concept of maximum likelihood estimation (MLE). In this post, I want to grapple with the idea of expectation maximization but before doing so I want talk about the idea of latent variables and why they drive the need for expectation maximiza
Read more →If you’re a student of statistics or data science, you’ve probably come across the idea of maximum likelihood estimation or MLE and if you’re also like me you probably struggled with it the first time around. The goal of this post is to simply break it down to u
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In my previous roles at Hitachi, I was fortunate to work with some very smart folks in the Industrial IoT (IIoT) space. This experience sparked a serious appreciation of what it takes to manage a production IIoT environment. In working with these teams, I was able to work as both
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Simple Linear RegressionIn my last few posts, I discussed linear regression and how we find the beta estimates for ridge and lasso regression problems. In keeping with that theme and continuing my journey with PyTorch, I wanted to demonstrate how simple it is to build a linear mo
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In my last two posts, we saw how to use analytical optimization methods to find the beta coefficients for ordinary least squares and ridge regression. In this post, I want to try to tackle LASSO Regression.
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In my previous post, I showed how to find the optimal coefficients for ordinary least-squared regression using simple analytical methods. For this post, I want to show how we can do the same for Ridge Regression.
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Before beginning, I'd like to shout out to Hala Nelson and her new book Essential Math for AI for inspiring this post. I wish I had this book while taking some of my classes at Georgia Tech!
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In a couple of previous posts, I talked about how I needed to quickly learn D3.js for a course in Data and Visual Analytics. As part of that course, in addition to learning D3.js and other data visualization technologies (Tableau), we had a large group project that was effectivel
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