AI6 Cycle 1 Learning Materials   Stanford STAT385 Theories of Deep Learning


About the Stanford STAT385 Theories of Deep Learning category (1)
Lagrangian formulation of backpropagation (9)
Wiki: Lecture 2 – Overview of Deep Learning from a Practical Point of View (3)
Wiki: Discussion on STAT385 Lecture 1 Video and Readings (1)
Wiki: Lecture 10 – CNNs in view of Sparse Coding (1)
Wiki: Lecture 9 – What's Missing in Deep Learning (1)
Wiki: Lecture 8 – Topology and Geometry of Half-rectified Network Optimization (1)
Wiki: Lecture 7 – Understanding and Improving Deep Learning With Random Matrix Theory (1)
Wiki: Lecture 6 – Views of Deep Networks from Reproducing Kernel Hilbert Spaces (1)
Wiki: Lecture 5 – When Can Deep Networks Avoid the Curse of Dimensionality (1)
Wiki: Lecture 4 – Covnets from First Principles (1)
Wiki: Lecture 3 – Harmonic Analysis of Deep Convolutional Neural Networks (1)