TLDR: The most important thing about communicating uncertainty is that you’re doing it.

Want all the formulae? presentation, github

🙂

Skip to content
# recurrent null

## Data Science, Machine Learning, & diverse IT stuff

#
Month: November 2017

# Plus/minus what? Let’s talk about uncertainty (talk)

# Dynamic forecasts – with Bayesian linear models and neural networks (talk at Predictive Analytics World Berlin)

# Deep Learning with Keras – using R (talk)

Last week at DOAG 2017, I had two talks, one about deep learning with DL4J (slides here) and one about how to communicate uncertainty (or rather: how to construct prediction intervals for various methods / in various frameworks ranging from simple linear regression over Bayesian statistics to neural networks).

TLDR: The most important thing about communicating uncertainty is that you’re doing it.

Want all the formulae? presentation, github

🙂

Advertisements

I really wish I had the time to write an article about the conference, instead of just posting the slides!

Predictive Analytics World was super inspiring, not just in a technical way but also as to the broader picture of today’s data science / AI explosion, including its political, sociological and personal implications.

As I really don’t have the time, I’m not even gonna try, so let me just point you to my talk, which was about time series forecasting using two under-employed (as yet) methods: Dynamic Linear Models (think: Kalman filter) and Recurrent Neural Networks (LSTMs, to be precise).

So, here are the slides, and as usual, here’s the link to the github repo, containing some more example code.

For me, experimentation with time series forecasting seems to form a time series in itself – I’m sure there’s pretty much still to be explored 🙂

Thanks for reading!

This week in Kassel, [R]Kenntnistage 2017 took place, organised by EODA. It was all about Data Science (with R, mostly, as you could guess): Speakers presented interesting applications in industry, manufacturing, ecology, journalism and other fields, including use cases such as predictive maintenance, forecasting and risk analysis.

I had the honour to have a talk too (thanks guys!), combining two of my favorite topics – deep learning and R. The slides are on RPubs as usual, and the source code (including complete examples) can be found on github.

Last not least, it’s great to see data science, and R, gaining momentum like that (this is Europe, so I can still write such a sentence ;-))

If you allow me to include an advertisement here – if you’re wondering what insight might come out of *your* data: At Trivadis, we’re a (yet) smallish but super motivated team of data scientists and machine learning practitioners happy to help!