Ruhr.PY - Introducing Deep Learning with Keras and Python

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Transparente und Verantwortungsbewusste KI: Was bedeutet Explainable und Responsible AI und warum sollten wir uns damit beschäftigen? In dieser Session widmen wir uns den aktuellen Themen Explainable AI (XAI) und Responsible AI (RAI), bei denen es um die Entwicklung von Künstlicher Intelligenz geht, die transparent, erklärbar und ethisch verantwortlich agiert. Wir wollen ein breites Publikum ansprechen, von KI-Experten und Forschern bis hin zu Vertretern aus Wirtschaft, Politik und der Zivilgesellschaft. Ziel ist es, ein Verständnis für die Bedeutung von Explainable und Responsible AI zu schaffen, mögliche Herausforderungen zu diskutieren und Lösungsansätze für eine verantwortungsvolle KI-Nutzung zu entwickeln.

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Vortrag auf der M3 Online-Konferenz am 16.06.2020 (https://online.m3-konferenz.de/lecture.php?id=12337&source=0) Mit Machine Learning getroffene Entscheidungen sind inhärent schwierig – wenn nicht gar unmöglich – nachzuvollziehen. Ein scheinbar gutes Ergebnis mit Hilfe von maschinellen Lernverfahren ist oft schnell erzielt oder wird von anderen als bahnbrechend verkauft. Die Komplexität einiger der besten Modelle wie neuronaler Netze ist genau das, was sie so erfolgreich macht. Aber es macht sie gleichzeitig zu einer Black Box. Das kann problematisch sein, denn Geschäftsführer oder Vorstände werden weniger geneigt sein, einer Entscheidung zu vertrauen und nach ihr zu handeln, wenn sie sie nicht verstehen. Shapley Values, Local Interpretable Model-Agnostic Explanations (LIME) und Anchors sind Ansätze, diese komplexen Modelle zumindest teilweise nachvollziehbar zu machen. In diesem Vortrag erkläre ich, wie diese Ansätze funktionieren, und zeige Anwendungsbeispiele. LERNZIELE * Die Teilnehmer erhalten Einblick in Möglichkeit, die komplexe Modelle erklärbar machen. * Sie lernen, Datensätze kritisch zu hinterfragen und angemessen aufzuteilen. * Und sie erfahren, unter welchen Bedingungen sie Entscheidungen durch Machine Learning vertrauen können.

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These are the slides from workshop: Introduction to Machine Learning with R which I gave at the University of Heidelberg, Germany on June 28th 2018. The accompanying code to generate all plots in these slides (plus additional code) can be found on my blog: https://shirinsplayground.netlify.com/2018/06/intro_to_ml_workshop_heidelberg/ The workshop covered the basics of machine learning. With an example dataset I went through a standard machine learning workflow in R with the packages caret and h2o: - reading in data - exploratory data analysis - missingness - feature engineering - training and test split - model training with Random Forests, Gradient Boosting, Neural Nets, etc. - hyperparameter tuning

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This is a slide deck from a presentation, that my colleague Uwe Friedrichsen (https://www.slideshare.net/ufried/) and I did together. As we created our respective parts of the presentation on our own, it is quite easy to figure out who did which part of the presentation as the two slide decks look quite different ... :) For the sake of simplicity and completeness, Uwe copied the two slide decks together. As he did the "surrounding" part, he added my part at the place where I took over and then added concluding slides at the end. Well, I'm sure, you will figure it out easily ... ;) The presentation was intended to be an introduction to deep learning (DL) for people who are new to the topic. It starts with some DL success stories as motivation. Then a quick classification and a bit of history follows before the "how" part starts. The first part of the "how" is some theory of DL, to demystify the topic and explain and connect some of the most important terms on the one hand, but also to give an idea of the broadness of the topic on the other hand. After that the second part dives deeper into the question how to actually implement DL networks. This part starts with coding it all on your own and then moves on to less coding step by step, depending on where you want to start. The presentation ends with some pitfalls and challenges that you should have in mind if you want to dive deeper into DL - plus the invitation to become part of it. As always the voice track of the presentation is missing. I hope that the slides are of some use for you, though.

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