Andreas Maier

265 to 189 of 189

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
Read more about Watch now: Pattern Recognition: Episode 12 – Discriminant Analysis – Properties (WS 20/21)
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
Deep Learning WS 20/21
Watch now: Deep Learning: Loss and Optimization – Part 3 (WS 20/21)
This video discusses details on optimization and different options in gradient descent procedure such as momentum and ADAM. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science
Read more about Watch now: Deep Learning: Loss and Optimization – Part 3 (WS 20/21)