Laurent did a wonderful job of listing slides and videos at the same location, you should go to his page to check videos and slides of the STAT385 at Stanford taught by Dave Donoho in part and with some illustrous speakers. The link is here.
- 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: Generative Models, Dynamic Programming & EM (Ankit Patel)
- Lecture05: When Can Deep Networks Avoid the Curse of Dimensionality and Other Theoretical Puzzles (Tomaso Poggio)
- Lecture06: Views of Deep Networksfrom 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’s Missing from Deep Learning? (Bruno Olshausen)
- Lecture10: Convolutional Neural Networks in View of Sparse Coding (Vardan Papyan)
Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.