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Carina Schipper

Carina Schipper
Carina Schipper has been an editor at Java Magazine, Business Technology and JAXenter since 2017. She studied German and European Ethnology at the Julius-Maximilians-University Würzburg.

All Posts by this author

See Markus Nutz and Thomas Pawlitzki speak at the ML Conference on 5 December 2018 in Berlin!

Too many ideas, too little data – Overcome the cold start problem

The cold start problem affects both startups as well as established companies. Nonetheless, it also provides a great opportunity to collect new data with your customer’s problem in focus. How do you solve the cold start problem and arrive at a useful data pipeline? We talked to ML Conference speakers Markus Nutz and Thomas Pawlitzki about all this and more.

See Philipp Beer speak at the ML Conference on 5 December 2018 in Berlin!

“Designing proper data collection today improves the quality of ML outcomes tomorrow”

Machine learning may have all sorts of use cases, but forecasting? In honor of the upcoming ML Conference, we talked to Philipp Beer about how data scientists can utilize ML in statistical forecasting. We talk about the advantages and disadvantages of modern vs. classical methods, how can one decide between the two, and where should they turn when they need good predictions for their business KPIs.

Interview with ML Conference speakers Tina Nord & Kathleen Jaedtke

ML at its best: How the communication between human and machines works

Machine learning enables customized conversations between man and machine that can result in buying decisions. We asked Tina Nord and Kathleen Jaedtke to explain how this can be achieved through the use of dialogue-oriented technologies. Let’s take a look at how communication between man and machines works.

Interview with Christoph Henkelmann

Machine Learning algorithms: Working with text data

Deep down ML is a pure numbers game. With very few exceptions, the actual input to an ML Model is always a collection of float values. We talked with Christoph Henkelmann about the way ML algorithms work on words and letters, the difference between image and text and how to handle textual input properly.