This 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.
Read moreI’m excited to announce that I have agreed with O’Reilly Media to write a new book on Reinforcement Learning. The contracts have just been signed and I’ve started the writing process. It is likely to take around a year to be released so I’m hoping that it will be ready around Summer 2020.
Read moreAt their Cloud’s Next 19 conference, Google has announced the launch of an expanded AI platform. For a number of years Google has been expanding it’s portfolio to compete with AI products from Azure and AWS. But this is the first time that the platform can be considered “end-to-end”.
Read moreI’ve seen a few posts recently about the emergence of a new field that is kind of like DevOps, but not quite, because it involves too much data. Verbally, about two years ago and in blog form about a year ago, I used the word DataDevOps, because that’s what I did. I develop and operate Data Science platforms, products and services. But more recently I have read of the emergence of DataOps.
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