In software engineering, the famous quote by Phil Karlton, extended by Martin Fowler goes something like: “There are two hard things in computer science: cache invalidation, naming things, and off-by-one errors.” In data science, there’s one hard thing that towers over all other hard things: deployment.
Read moreI have two outstanding tasks from the previous notebooks. The first is that I haven’t iterated over all countries.
Read moreThis post builds upon the exponential model created in a previous post. The main issue was that there an exponential model does not include a limit. A logistic model introduces this limit. I also perform some very basic backtesting and future prediction.
Read moreThis notebook builds upon the exponential bayesian model to implement simple backtesting. The idea here is to hold out data, train a model, and see how well the model is able to predict those results.
Read moreThe purposes of this notebook is to provide initial experience with the pymc3 library for the purpose of modeling and forecasting COVID-19 virus summary statistics. This model is very simple, and therefore not very accurate, but serves as a good introduction to the topic.
Over the next couple of weeks I will be using Bayesian analysis to model the spread of COVID-19. Inspired by Alex Stage who started the Athena Project, I have committed Winder.AI to helping Athena reach its goals.
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Data is an essential asset of modern business. It empowers companies by surfacing unique insights about their customers and creates actionable products. The more data you possess, the better you meet and exceed your customers’ expectations.
Read moreSlides Abstract The Internet is full of examples of how to train models. But the reality is that industrial projects spend the majority of the time working with data. The largest improvements in performance can often be found through improving the underlying data. Bad data is costing the US economy an estimated 3.1 trillion Dollars and approximately 27% of data is flawed in the world’s top companies. Bad data also contributes to the failure of many Data Science projects.
Read moreTime-series (TS) filters are often used in digital signal processing for distributed acoustic sensing (DAS). The goal is to remove a subset of frequencies from a digitised TS signal. To filter a signal you must touch all of the data and perform a convolution. This is a slow process when you have a large amount of data. The purpose of this post is to investigate which filters are fastest in Python.
Read moreReinforcement Learning (RL) frameworks help engineers by creating higher level abstractions of the core components of an RL algorithm. This makes code easier to develop, easier to read and improves efficiency.
But choosing a framework introduces some amount of lock in. An investment in learning and using a framework can make it hard to break away. This is just like when you decide which pub to visit. It’s very difficult not to buy a beer, no matter how bad the place is.
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