Archives: Thesis

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19NOV 2020
Pattern Recognition WS 20/21
Watch now: Pattern Recognition: Episode 12 – Discriminant Analysis – Properties (WS 20/21)
In this video, we look into some useful properties of discriminant analyses. Watch on:FAU TVYouTube
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19NOV 2020
Deep Learning WS 20/21
Watch now: Deep Learning: Activations, Convolutions, and Pooling – Part 2 (WS 20/21)
This video presents a variety of modern activation functions including the quest for finding new ones. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science
Read more about Watch now: Deep Learning: Activations, Convolutions, and Pooling – Part 2 (WS 20/21)
18NOV 2020
Pattern Recognition WS 20/21
Watch now: Pattern Recognition: Episode 11 – Discriminant Analysis – Concept (WS 20/21)
In this video, we start introducing discriminant transforms and look at their basic concept. Watch on:FAU TVYouTube
Read more about Watch now: Pattern Recognition: Episode 11 – Discriminant Analysis – Concept (WS 20/21)
18NOV 2020
Deep Learning WS 20/21
Watch now: Deep Learning: Activations, Convolutions, and Pooling – Part 1 (WS 20/21)
This video presents the biological background of activation functions and the classical choices that were used for neural networks. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science
Read more about Watch now: Deep Learning: Activations, Convolutions, and Pooling – Part 1 (WS 20/21)
17NOV 2020
Pattern Recognition WS 20/21
Watch now: Pattern Recognition: Episode 10 – Naive Bayes Classifier (WS 20/21)
In this video, we have a look at the naive Bayes classifier. Watch on:FAU TVYouTube
Read more about Watch now: Pattern Recognition: Episode 10 – Naive Bayes Classifier (WS 20/21)
17NOV 2020
News
New PhD student
After her Master’s studies in Computer Science, Nora Gourmelon joined the Pattern Recognition Lab mid of November 2020 as a PhD candidate supervised by Prof. Andreas Maier. She will be working in the Emerging Field Initiative Project “Tapping the potential of Earth Observation”. Nora’s research interests lie in the application of machine and deep learning […]
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