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1
Introduction
2
Motivation
3
Symmetry
4
Transformations
5
Time Series
6
Continuous Domain
7
Geometric Stability
8
Convolutional Network
9
Other Models
10
Spatial Support
11
Convolutional Kernel Networks
12
Reproducing Kernels
13
Scattering Representation
Description:
Explore advanced concepts in deep learning through this comprehensive lecture by Joan Bruna from NYU, covering topics such as symmetry, transformations, time series analysis, continuous domain applications, geometric stability, convolutional networks, and scattering representations. Delve into the intricacies of reproducing kernels, convolutional kernel networks, and spatial support while gaining insights into various deep learning models and their applications.

Deep Learning II - Joan Bruna NYU

Paul G. Allen School
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