Overview on Mateusz Opala Reproducible Machine Learning Pydata Warsaw 2019
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DataSphere: Reproducible Machine Learning by Matthew Opala
Mateusz Opala: Debugging Machine Learning - 5 największych pułapek, z którymi musiałem się zmierzyć
Pawel Cyrta: Sound Modelling - parametric methods and deep learning... | PyData Warsaw 2019
Boguszewski & Jankowska: In the service of the history. AI in archivistics. | PyData Warsaw 2019
AI Monday Berlin - Mateusz Opal - Netguru - 28.01.2019
Robert Kostrzewski: Modern Machine Learning flow with Quilt and Polyaxon | PyData Warsaw 2019
PyData Warsaw Conference 2019 - Showreel
Martyna Urbanek-Trzeciak: ML model from an idea to production with the help... | PyData Warsaw 2019
Keynote - Romeo Kienzler: Trusted AI – Building Reproducible, Unbiased and... | PyData Warsaw 2019
Zuzanna Kunik: Command line language – where NLP and cyber security meets | PyData Warsaw 2019
Prabhant Singh: Reproducible machine learning and science with python
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Last Updated: October 27, 2026
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Summary
In the last couple of years there've been a lot of buzz generated around performance of deep Debugowanie algorytmów uczenia maszynowego jest trudnym zadaniem. Pomimo tego, że jest potrzebną umiejętnością w ... Full title - Pawel Cyrta: Sound Modelling - parametric methods and deep Full title - Adrian Boguszewski and Natalia Ziemba-Jankowska: In the service of the history. AI in archivistics. A trillions of old ... Presentation of development flow dedicated to Full title - Martyna Urbanek-Trzeciak: ML model from an idea to production with the help of Python We will talk about how Python ... Full title - Romeo Kienzler: Trusted AI – Building In the cyber security world, analysis of command line logs is important for breach detection, but it is one of the most challenging ... Speaker:: Prabhant Singh Track:
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