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Deep Learning Philipp Grohs March 1st 2017

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Deep Learning

Philipp Grohs

March 1st 2017

Organisatorial Stuff...

3h lecture (MSc Level)

Monday 11:30 - 13:00 (seminar room 2nd floor) & Wednesday13:15 - 14:00 (seminar room 2nd floor)

Oral exam at the end of the semester

Exercise classes during the semester

Prerequisites: Linear algebra, Analysis

Desirable: Probability Theory, Functional Analysis, Experiencewith Python or MATLAB

I will create a website at http://mat.univie.ac.at/~grohs/DeepLearningCourse.html.

http://mat.univie.ac.at/~grohs/DeepLearningCourse.html

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Face recognition

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Face recognition

D. Trump

B. Sanders

B. Johnson

A. Merkel

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Describing the content of an image

AI generates sentences describing the content of animage [Vinyals et al., 2015 ]

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Go!

AI beats Go-champion Lee Sedol [Silver et al., 2016 ]

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Atari games

AI beats professional human Atari-players [Mnih et al., 2015 ]

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Rating Attractiveness

Swiss Dating App Blinq (developed in cooperation with ETHZ)

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Goal of This Course

How does this work???

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Syllabus

1. Machine Learning Basics

2. Neural Networks

3. Convolutional Neural Networks

Caution

This is not a pure Mathematics course!

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Some Literature

I. Goodfellow, Y. Bengio and A. Courville. Deep Learning.(2016). Available from http://www.deeplearningbook.org.

F. Cucker and S. Smale. On the Mathematical Foundations ofLearning. Bulletin of the AMS 39/1, pp 1 – 49 (2001).

M. Nielsen. Neural Networks and Deep Learning. Available fromhttp://neuralnetworksanddeeplearning.com.

MATLAB Library MatConvNet, available fromhttp://www.vlfeat.org/matconvnet.

Python Library Theano, available fromhttp://deeplearning.net/software/theano.

http://www.deeplearningbook.org

http://neuralnetworksanddeeplearning.com

http://www.vlfeat.org/matconvnet

http://deeplearning.net/software/theano

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