Blog Archives

I’m a developer, why should I care about matrices or calculus? (talk at MLConference 2017)

December 6, 2017
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Yesterday at ML Conference, which took place this year for the first time, I had a talk on cool bits of calculus and linear algebra that are useful and fun to know if you’re writing code for deep learning and/or machine learning. Originally, the title was something like “What every interested ML/DL developer should know … Continue reading I’m...

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

November 15, 2017
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Dynamic forecasts – with Bayesian linear models and neural networks (talk at Predictive Analytics World Berlin)

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 … Continue reading Dynamic...

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Deep Learning with Keras – using R (talk)

November 11, 2017
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This week in Kassel, 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 … Continue reading Deep...

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Time series shootout: ARIMA vs. LSTM (talk)

October 7, 2017
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Yesterday, the Munich datageeks Data Day took place. It was a totally fun event – great to see how much is going on, data-science-wise, in and around Munich, and how many people are interested in the topic! (By the way, I think that more than half the talks were about deep learning!) I also had … Continue reading Time...

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Automatic Crack Detection – with Deep Learning

September 23, 2017
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Automatic Crack Detection – with Deep Learning

On Friday at DOAG Big Data Days, I presented one possible application of deep learning: using deep learning for automatic crack detection – with some background theory, a Keras model trained from scratch, and the use of VGG16 pretrained on Imagenet. The amount of input data really was minimal, and the resulting accuracy, under these … Continue reading Automatic...

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Deep Learning, deeplearning4j and Outlier Detection: Talks at Trivadis Tech Event

September 18, 2017
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Last weekend, another edition of Trivadis Tech Event took place. As usual, it was great fun and a great source of inspiration. I had the occasion to talk about deep learning twice: One talk was an intro to DL4J (deeplearning4j), zooming in on a few aspects I’ve found especially nice and useful while trying to … Continue reading Deep...

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Haskell, R, and HaskellR: Combining the best of two worlds (talk at UseR! 2017)

July 7, 2017
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Earlier today, I presented at UseR! 2017 about HaskellR: a great piece of software, developed by Tweag I/O, that allows to seemlessly use R from Haskell. It was my first UseR!, it was a great experience, and if I had the time I’d like to write a separate blog post about it, as there were … Continue reading Haskell,...

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Time series prediction – with deep learning

May 26, 2017
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Time series prediction – with deep learning

More and more often, and in more and more different areas, deep learning is making its appearance in the world around us. Many small and medium businesses, however, will probably still think – Deep Learning, that’s for Google, Facebook & co., for the guys with big data and even bigger computing power (barely resisting the … Continue reading Time...

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R 4 hackers

March 20, 2017
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R 4 hackers

Yesterday at Trivadis Tech Event, I talked about R for Hackers. It was the first session slot on Sunday morning, it was a crazy, nerdy topic, and yet there were, like, 30 people attending! An emphatic thank you to everyone who came! R a crazy, nerdy topic, – why that, you’ll be asking? What’s so … Continue reading R...

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