# Lisha

Current PhD Student at UC Berkeley Statistics

[Post in progress]

This semester I’m taking a new topics course in Deep Learning given by Joan Bruna. He has a public webiste for the course on github that includes course slides. Joan adds a lot of intuition and details on top of what is written in the slides, so I plan on blogging/taking notes about the course as a way to internalize the material.

An overview of what he covers (also found in lecture one lecture slides): 1. Mathematical models of deep convolutional networks. - Supervised and unsupervised learning using deep models. - Applications to computer vision, speech and time series. - Relationships between Deep Learning and “classic” models. - Open mathematical/statistical questions.

Deep Learning Models: “A class of parametrized non-linear representations encoding appropriate domain knowledge (invariance and stationarity) that can be (massively) optimized efficiently using stochastic gradient descent.”

## Classification, Kernals and Metrics

### High-dim Recognition Setup

Our input data $$x$$ for the neural network lives in a high-dimensional space, sometimes even infinite dimensional space. $x \in \Omega, \Omega \subset \mathbb{R}^d \text{for finite but large } d$ $x \in L^2(\mathbb{R}^m), m = 1,2,3.$

In the last case think of images in 1,2 or 3 dimensions as functions of colour on these spaces. The $$L^2$$ assumption is reasonable given our limited colour range.

Our observations are in the form $$(x_i, y_i), i = 1, …, n$$ where the $$y_i$$’s are “response” variables.

##Review: Separable Scattering Operators