codecentric mittendrin

Meetups, Stammtische, Hackathons, User Groups: Die codecentric ist weit mehr als die Summe ihrer Mitarbeiter und Projekte.

Hinter jeder erfolgreichen Software steht eine starke Community

Wissensvermittlung, Nachwuchsförderung und der fachliche Austausch auf Augenhöhe sind für uns Herzensangelegenheiten. Wir sind der Meinung: Geteilte Innovationsfreude ist doppelte Innovationsfreude.

Deshalb mischen sich codecentric-Mitarbeiter und -Mitarbeiterinnen gerne unter die Community – ob als Gastgeber, Redner oder Organisatoren diverser Veranstaltungen. Treffen Sie uns auf einem der folgenden Events!

R-Ladies London

R-Ladies London

207-211 Old Street, London, Vereinigtes Königreich 16.10.2018 | 18:00

Interpretable Deep Learning with R, Keras and LIME

R-Ladies London have a great pleasure to host Dr Shirin Glander, a data scientist at codecentric in Munster, Germany. If that wasn’t enough, she’s also an R-blogger, data enthusiast, organiser of MünsteR, animal lover, ballroom and latin dance teacher! You can read her award-winning blog here:

Shirin will talk about using R to build an interpretable imagine recognition models.


Keras is a high-level open-source deep learning framework that by default works on top of TensorFlow. Keras is minimalistic, efficient and highly flexible because it works with a modular layer system to define, compile and fit neural networks. It has been written in Python but can also be used from within R. Because the underlying backend can be changed from TensorFlow to Theano and CNTK (with more options being developed right now) it is designed to be framework-independent. Models can be trained on CPU or GPU, locally or in the cloud. Shirin will show an example of how to build an image classifier with Keras. We’ll be using a convolutional neural net to classify fruits in images. But that’s not all! We not only want to judge our black-box model based on accuracy and loss measures – we want to get a better understanding of how the model works. We will use an algorithm called LIME (local interpretable model-agnostic explanations) to find out what part of the different test images contributed most strongly to the classification that was made by our model. Shirin will introduce LIME and explain how it works. And finally, she will show how to apply LIME to the image classifier we built before, as well as to a pre-trained Imagenet model.


18.00 – 19.00: Doors open and networking.
19.00 – 19.15: Intro in the Data Science at Farfetch
19.15 – 20.30: Shirin’s talk + questions
~20.30 – ~21.00: networking


Shirin Glander