Lecture Videos

This video introduces the topic of recurrent neural networks and the Elman Cell. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

This video discusses learning to learn options for architecture search and first results. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we look into the concept of duality in optimization and strong duality in convex problems. Watch on:FAU TVYouTube

This video demonstrates the many uses of residual connections in deep networks from Inception-ResNet to DenseNet. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we explain the basic concept of the support vector machine. Watch on:FAU TVYouTube

This video discusses the ideas of residual connections in deep networks that allow going from 20 to more than 1000 layers. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we look at how norms alter gradient directions during optimization. Watch on:FAU TVYouTube

This video discusses the success of deeper models including Inception V2 and V3. One key technology that is introduced in V3 is label smoothing regularization. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science

In this video, we look into optimization using gradient descent and back-tracking line search. Watch on:FAU TVYouTube

This video discusses the first early architectures developed in deep learning from LeNet to GooLeNet. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science