Lecture videos for STATS385, Fall 2019

Lecture01: Analyses of Deep Learning (Donoho)

Lecture03: Analyses of Deep Learning (Tengyu Ma)

Lecture04: Analyses of Deep Learning (Jeffrey Pennington)

Lecture05: Analyses of Deep Learning (Song Mei)

Lecture06: Analyses of Deep Learning (Arthur Jacot)

Lecture07: Analyses of Deep Learning (Aleksander Madry)

Lecture08: Analyses of Deep Learning (Nathan Srebro)

Lecture09: Analyses of Deep Learning (Andrew Saxe)

Lecture10: Analyses of Deep Learning (Vardan Papyan)

Lecture videos for STATS385, Fall 2017

Lecture01: Deep Learning Challenge. Is There Theory? (Donoho/Monajemi/Papyan)

Lecture02: Overview of Deep Learning From a Practical Point of View (Donoho/Monajemi/Papyan)

Lecture03: Harmonic Analysis of Deep Convolutional Neural Networks (Helmut Bolcskei)

Lecture04: Convnets from First Principles (Ankit Patel)

Lecture05: When Can Deep Networks Avoid the Curse of Dimensionality (Tomaso Poggio)

Lecture06: Views of Deep Networks from Reproducing Kernel Hilbert Spaces (Zaid Harchaoui)

Lecture07: Understanding and Improving Deep Learning With Random Matrix Theory (Jeffrey Pennington)

Lecture08: Topology and Geometry of Half-rectified Network Optimization (Joan Bruna)

Lecture09: What is missing in Deep Learning (Bruno Olshausen)

Lecture10: Convolutional Neural Networks in View of Sparse Coding and Crimes of Deep Learning (Vardan Papyan and David Donoho)