On May 21st and 22nd, I had the honor of having been chosen to attend the rOpenSci unconference 2018 in Seattle. It was a great event and I got to meet many amazing people! rOpenSci rOpenSci is a non-profit organisation that maintains a number of widely used R packages and is very active in promoting a community spirit around the R-world. Their core values are to have open and reproducible research, shared data and easy-to-use tools and to make all this accessible to a large number of people.
Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on July 5th & 6th in Münster, Germany. You can read about one participant’s experience in my last workshop: Big Data – a buzz word you can find everywhere these days, from nerdy blogs to scientific research papers and even in the news. But how does Big Data Analysis work, exactly?
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang: Sketchnotes from TWiMLAI talk: Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang You can listen to the podcast here. In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies.
When looking through the CRAN list of packages, I stumbled upon this little gem: pkgnet is an R library designed for the analysis of R libraries! The goal of the package is to build a graph representation of a package and its dependencies. And I thought it would be fun to play around with it. The little analysis I ended up doing was to compare dependencies of popular machine learning packages.
Here I am sharing the slides for a talk that my colleague Uwe Friedrichsen and I gave about Deep Learning - a Primer at the JAX conference on Tuesday, April 24th 2018 in Mainz, Germany. Slides can be found here: https://www.slideshare.net/ShirinGlander/deep-learning-a-primer-95197733 Deep Learning is one of the “hot” topics in the AI area – a lot of hype, a lot of inflated expectation, but also quite some impressive success stories.
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Reproducibility and the Philosophy of Data with Clare Gollnick: Sketchnotes from TWiMLAI talk #121: Reproducibility and the Philosophy of Data with Clare Gollnick You can listen to the podcast here. In this episode, i’m joined by Clare Gollnick, CTO of Terbium Labs, to discuss her thoughts on the “reproducibility crisis” currently haunting the scientific landscape.
Since I migrated my blog from Github Pages to blogdown and Netlify, I wanted to start migrating (most of) my old posts too - and use that opportunity to update them and make sure the code still works. Here I am updating my very first machine learning post from 27 Nov 2016: Can we predict flu deaths with Machine Learning and R?. Changes are marked as bold comments. The main changes I made are: