Die Herausforderungen mancher Projekte sind einfach nicht lösbar – einfach weil alles, was schief gehen kann, auch schief geht. Missverständnisse, Probleme mit dem Datensatz, unangemessen hohe Erwartungen, um nur einiges zu nennen. Alles passier auf einmal, und man sieht es nicht mal kommen, bis es zu spät ist. Die Frage ist, was man in einer solchen Situation tun soll, besonders wenn man eine Reihe von Konkurrenten hat. Vladimir Rybakov beantwortet diese Frage in dieser Session von der ML Conference.
Applying Machine Learning online at Scale
by Jon Bratseth (Verizon)
Interpretable Machine Learning – Application to Finance and Banks
by Debjyoti Paul (Amazon)
Deep Learning mit TensorFlow 2
mit Jakob Karalus (Deloitte)
Alles selber lesen war gestern – wie man mit Machine Learning große Textmengen in Erkenntnisse verwandelt
mit Stephanie Fischer und Dr. Christian Winkler (datanizing)
Der Speaker: Vladimir Rybakov
Vladimir holds a master’s degree in engineering. He became interested in machine learning and data science since the first years of studying. During that time, he published several publications, participated in two grants, and wrote both his baccalaureate’s and master’s papers in the field of ML. Since 2015, Vladimir manages the ML R&D team in WaveAccess.
Vladimir holds a master’s degree in engineering. He became interested in machine learning and data science since the first years of studying. During that time, he published several publications, participated in two grants, and wrote both his baccalaureate’s and master’s papers in the field of ML. Since 2015, Vladimir manages the ML R&D team in WaveAccess.
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