Machine learning made easy

Revisiting Numerai

In this article, we revisit Numerai and their weekly data science tournament. New developments include a much larger dataset, tougher requirements for models, and bigger payouts.

It’s embarassing, really

In August, we published the first version of goodbooks-10k, a new dataset for book recommendations. By pure chance, that coincided with a proclamation of Kaggle Datasets Awards. Oh, how we hoped to get one!

Introduction to pointer networks

Pointer networks are a variation of the sequence-to-sequence model with attention. Instead of translating one sequence into another, they yield a succession of pointers to the elements of the input series. The most basic use of this is ordering the elements of a variable-length sequence or set.

Project RHUBARB: predicting mortality in England using air quality data

Once again we beat the benchmark in a Kaggle competition. The goal of the contest at hand was to forecast mortality rate in England using Copernicus Atmosphere Monitoring Service data on air quality. Specifically, to forecast mortality caused by cancer and cardiovascular diseases. The competition represents the “in class” category, because the data is publicly available somewhere on the internets. Still, the winner got a Raspberry Pi.

Tuning hyperparams fast with Hyperband

Hyperband is a relatively new method for tuning iterative algorithms. It performs random sampling and attempts to gain an edge by using time spent optimizing in the best way. We explain a few things that were not clear to us right away, and try the algorithm in practice.

Data in, predictions out

With many implementations of machine learning algorithms it is entirely unclear how to train them on one’s own data and then how to get predictions. This is an area where AI researchers have a lot of catching up to do with lessons long ago learned in computer science.

On chatbots

Chatbots seem to be all the craze these days. Why don’t we take a look at this fascinating topic. A warning, though: this article contains strong opinions.

Piping in R and in Pandas

In R community, there’s this one guy, Hadley Wickam, who by himself made R great again. One of the many, many things he came up with - so many they call it a hadleyverse - is the dplyr package, which aims to make data analysis easy and fast. It works by allowing a user to take a data frame and apply to it a pipeline of operations resulting in a desired outcome (an example in just a minute). This approach turned out to be successful. Then people have ported key pieces to Pandas.

Deep learning architecture diagrams

As a wild stream after a wet season in African savanna diverges into many smaller streams forming lakes and puddles, so deep learning has diverged into a myriad of specialized architectures. Each architecture has a diagram. Here are some of them.