TL;DR - Using a Markov Chain to model your historical sales pipeline data can help you understand the probability of eventually closing a deal based on (1) the stage the opportunity is currently in and (2) your historical data. This lets you estimate the future value of your current pipeline based on your own past data.
What is a Markov Chain? It is a mathematical model used to describe how probabilistic systems or processes evolve over time.
In the Fall of 2017, while finishing my PhD I taught ChE thermodynamics to a class of graduate and advanced undergraduate students at the University of Rhode Island. I recently discovered some of my old course materials and decided to post them publically. It was a project based class, so after the first few lectures, most of the class meetings were spent working on problems and projects.
Either way, I hope they are useful to someone!
TL;DR: Either “File –> Download” the Google Doc as a web page, or open developer tools and watch for the browser to request the image from Google’s file servers.
The deets Here are two simple ways to get an image out of a Google Doc, at least until Google (maybe inevitably?) adds a “right click-> save image as” feature.
The first way is pretty straightforward, download the Google Doc containing the image files you would like to extract as a web page (File → Download → Web page (.
TL;DR - Here’s a web app to list labelled traffic cam images from the RI DOT website. I used YOLO (You Only Look Once) to detect objects (vehicles) in the images and FastAPI to serve the results to a React app. The displays the cam images and results and uses LeafletJS to display the cameras on a map. The backend is hosted on Railway and the front end on Github Pages.
TL;DR - Here’s a list of shipwrecks plotted on an interactive map, all over the Narragansett Bay and greater New England area.
Motivation At FarSounder I work on the development software of 3D Forward Looking Sonar products. Part of that development process of course includes collecting data from a lot of different situations and running it through some processing algorithms to evaluate performance and make improvements. To that end, we’re often out on the Narragansett Bay collecting data with the system, and always looking for areas in the bay with interesting features that we can use to test and benchmark our algorithms (pilings, super steep shoals, rock piles, piers, etc).