## Predicting Limits

Multiple Outputs Make Sense.

## Naive Bias[tm] and Fairness Tooling

Just Another Dangerous Situation

## A Loop to Stop Writing.

Let's make life a whole log simpler.

## Maths as a Compiler

Once again, rephrasing is a friend.

## Oops and Optimality

GridSearch is Not Enough: Part Five

## Uncommon Contributions

Making impact without touching the core of a library.

## Mean Squared Terror

GridSearch is Not Enough: Part Four

## Sharing is Caring

Put Cookie-Monster on a Diet.

## More Descent, Less Gradient

Your regression may only need one gradient step. Really.

## Priors of Great Potential

Common Sense reduced to Priors

## Introduction to Inference

Reduce Common Sense to Calculus.

## Theoretical Dependence

Some assumptions are just plain weird.

## The Cost of Success

VeryHighAccuracy[tm] as a Problem.

## The Elephants not in the Room

The unknowns might be more interesting.

## Roman Reasoning

We should evolve beyond it.

## What Overfitting Looks Like

GridSearch is not enough: Part Three.

## Parallel Grid

An Ode to Pipes, Seeds and Simplicity

## Goodhart, Bad Metric

GridSearch is not Enough: Part Two.

## Little Victories Scale

Thoughts on automating automation itself.

## Outliers: Selection vs. Detection

Algorithms can detect outliers, but how do you select algorithms?

## Foresight for Predictions

Predictions without Foresight can be Anticipated to become Dreadful.

## Bingo Ball Pit

An Exercise in Systemic Counting

## High on Probability, Low on Confidence

GridSearch is not Enough: Part One.

## Some Geometric Algorithms

And Something Obvious about Albert Heijn.

## The Future of Data Science is Past

And it's not just because that's whats *actually* being predicted.

## Optimisation, Not Gradients

A small point to point out a difference.

## Sensors are Servers

The only servers in my stack are the sensors themselves.

## Wine Cellar Strategies

Pretending there's an optimal way to drink it.

## VI Drives me NUTS

Feel free to be a bit weary.

## Rephrasing the Billboard

A probability problem involving 40,000 sent letters.

## Gaussian Auto Embeddings

Sort of came up with an alternative to VI here.

## Amdahl's Law

The more CPU's you add, the worse it gets.

## Feed Forward Posteriors

Combine the Neural with the Normal.

## Passive Agressive Algorithms

A VeryGood[tm] name for a VeryGood[tm] Algorithm.

## Vary Very Optimally

How to search in search space.

## Bayesian Shaving Cream

A plausible (and general) method for model selection.

## Avoiding, and Preventing, Joins

Join me in preventing this.

## Hello DeepQ

Stick a Network in the Q-learning algorithm.

## Twain Learning

Never let your school get into the way of your regression.

## Switching to Sampling in Order to Switch

A simple introduction to PyMC3.

## Pokemon Recommendations, Part 2

Even more of a Sequel than SQL.

## Lego Minifigures

An investment opportunity and sampling.

## Custom Predictors in R

It's different than Python, but S3 isn't *that* bad.

## Ensemble Yourself

Scribbles as an Algorithm Service.

## Pokemon Recommendations, Part 1

An Attempt at an OptimalPortfolio[tm]

## Linear Models Solving Non-Linear Problems

XOR turns out to be a bad argument.

## Variable Selection in Machine Learning

Merely *a* argument, but one that I like.

## Digital Nomad

Some observations but also downsides.

## Vanity Metrics

How I got an A+ for measuring the wrong thing.