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Deep Learning Talk - Saverin
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Yasser Souri
May 09, 2016
Technology
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Deep Learning Talk - Saverin
Deep Learning Introduction Talk @ Saverin
Yasser Souri
May 09, 2016
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Transcript
Deep Learning Yasser Souri - Alireza Nourian http://sobhe.ir
Have you ever heard of ... Neural Networks
Have you ever heard of ... Deep Learning
Who is he?
Who is he? Jeff Dean, Google
Jeff Dean Creator of Map Reduce, Big Table, Google Crawler
Jeff Dean Creator of Map Reduce, Big Table, Google Crawler
Google Ads, Google Translator, ...
Jeff Dean Facts Compilers don't warn Jeff Dean. Jeff Dean
warns compilers.
Jeff Dean’s Calculator
Jeff Dean’s Current Role Google Brain
DeepMind In 2014, Google acquired DeepMind (a team of ~50)
for ~$ 500 million. And facebook wanted to buy them also.
What is Machine Learning? Problem 1: Given a sequence of
numbers, sort them
What is Machine Learning? Problem 1: Given a sequence of
Farsi characters, output Pinglish
What is Machine Learning? Problem 3: Give a grayscale 28x28
pixel image, identify what number it is.
What is Machine Learning? Problem 3: Give a grayscale 28x28
pixel image, identify what number it is.
What is Machine Learning? x f(x) y Classic
What is Machine Learning? x f(x) y g(x) y’ h(x)
y” Classic
How to Solve Machine Learning Problems Data = (x, y)
Classic
How to Solve Machine Learning Problems Data = (x, y)
y = f(x) Classic (x, y) f(x)
How to Solve Machine Learning Problems Data = (x, y)
y = f(x) Learn the parameters Classic (x, y) f(x; w)
How to Solve Machine Learning Problems Data = (x, y)
y = f(x) Learn the parameters Can x be the raw pixels? Classic (x, y) f(x; w) Features
How to Solve Machine Learning Problems Data = (x, y)
y = f(x) Learn the parameters Can x be the raw pixels? Classic (x, y) f(x; w) Features O(#features) ~ O(#parameters)
Machine Learning Demo http://playground.tensorflow.org/ Classic
Deep Learning Basics Learn from raw data y = f(g(h(
… (x) ))) Deep
Deep Learning Learn from raw data Number of parameters are
much larger y = f(g(h( … (x) ))) Deep
Deep Learning Learn from raw data Number of parameters are
much larger You need more data to learn y = f(g(h( … (x) ))) Deep
Problems being solved with deep learning Deep
Problems being solved with deep learning Deep
One to one: Image Classification Deep
One to one: Image Classification Deep
Problems being solved with deep learning
One to Many: Image Captioning Describing Images:
Fun With ConvNets Describing Images:
Problems being solved with deep learning
May to One: Generating Images Generating Images:
May to One: Generating Images Generating Images:
Problems being solved with deep learning
Statistical Machine Translation
End-to-End Neural Machine Translation (1) Hirschberg, J. & Manning, C.
D. Advances in natural language processing, Science, 2015, 349, 261-266
None
Learning to Execute
Deep Reinforcement Learning
Demo Videos https://www.youtube.com/watch?v=ePv0Fs9cGgU https://www.youtube.com/watch?v=Q70ulPJW3Gk
Fun With ConvNets Modifying images:
Fun With ConvNets Style transfer:
Fun With ConvNets Style transfer:
Fun With ConvNets Colorization:
Fun With ConvNets Colorization:
Fun With ConvNets Colorization:
Fun With ConvNets Colorization:
Fun With ConvNets Colorization:
Fun With ConvNets Colorization:
Fun With ConvNets Colorization:
Growing Use of Deep Learning at Google Jeff Dean &
Oriol Vinyals, “ Large Scale Distributed Systems for Training Neural Networ”, NIPS 2015.
Deep Learning Tools