Publications by Brian Lee Yung Rowe
A primer on universal function approximation with deep learning (in Torch and R)
Arthur C. Clarke famously stated that “any sufficiently advanced technology is indistinguishable from magic.” No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people’s hearts. This primer sheds some light on how neural ...
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A simple workflow for deep learning
As a follow-up to my Primer On Universal Function Approximation with Deep Learning, I’ve created a project on Github that provides a working example of building, training, and evaluating a neural network. Included are helper functions in Lua that I wrote to simplify creating the data and using some functional programming techniques. The basic w...
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What you need to know about data augmentation for machine learning
Plentiful high-quality data is the key to great machine learning models. But good data doesn’t grow on trees, and that scarcity can impede the development of a model. One way to get around a lack of data is to augment your dataset. Smart approaches to programmatic data augmentation can increase the size of your training set 10-fold or more. Eve...
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Modeling data with functional programming, Part I
The latest draft of my book is available. This will be my last pre-publication update, as I’m in the process of editing the remainder of the book. That said, the first part is packed with examples and provides a solid foundation on its own. I’m including the preface below to whet people’s appetite for the complete book when it is published....
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Data-driven unit testing for data scientists and quant developers alike
Often overlooked, testing is a critical process that saves time over the long term and enables building complex systems. Unit tests for model (descriptive, predictive, or prescriptive analytics) systems differ from standard software. Effective unit testing of computational systems requires a particular emphasis on the data used in the tests. Belo...
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How to call bullshit on AI companies (aka a short lesson on recall)
Now that software ate the world, what’s for dessert? Those in the know know that it’s AI. It seems everyone took Kevin Kelly’s recommendation to “take X and add AI” to heart. Fast forward to 2018 and all startups tout adding AI to X. There’s a fine line between hype and bullshit, so how do you separate the substance from the snake oil...
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Preview my new book: Introduction to Reproducible Science in R
I’m pleased to share Part I of my new book “Introduction to Reproducible Science in R“. The purpose of this book is to approach model development and software development holistically to help make science and research more reproducible. The need for such a book arose from observing some of the challenges that I’ve seen teaching graduate c...
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Request for comments on planned features for futile.logger 1.5
I will be pushing a new version of futile.logger (version 1.5) to CRAN in January. This version introduces a number of enhancements and fixes some bugs. It will also contain at least one breaking change. I am making the release process public, since the package is now used in a number of other packages. If you use futile.logger, this is your oppo...
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Lies, damned lies, and rankings: the problem with Bloomberg’s COVID resilience ranking
Every ranking creates winners and losers. In the case of Bloomberg’s Covid Resilience Ranking, the Philippines is a loser: dead last and called the worst place to be during the pandemic. A damning judgment that the country’s vaccine czar, Carlito Galvez, Jr., says isn’t fair due to a biased scoring methodology. Is Bloomberg’s ranking bias...
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