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

In this video, we have a look at the naive Bayes classifier. Watch on:FAU TVYouTube

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 inte...

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

It is a great pleasure to announce an invited talk by Ivana Isgum from the University of Amsterdam on her great research in the field of Machine Learning and Medical Imaging: Title: Deep learning for automatic detection of cardiovascular disease in CT and MR exams Prof. Dr. Ivana Išgum...

In this video, we learn how to estimate the parameters of the logistic function effectively. Watch on:FAU TVYouTube

This video explains hinge loss and its relation to support vector machines. We also show why sub-gradients allow us to optimize factions that are not continuously differentiable. Furthermore, the hinge loss enables to embed optimization constraints into loss functions. Watch on:FAU TVFAU TV (no ...

This video explains how to derive L2 Loss and Cross-Entropy Loss from statistical assumptions. Highly relevant for the exam! Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we look into the actual logistic regression and how to formulate the optimization problem. Watch on:FAU TVYouTube

This video explains backpropagation at the level of layer abstraction. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science