YOU ARE DOWNLOADING DOCUMENT

Please tick the box to continue:

Transcript
Page 1: Donner - Deep Learning - Overview and practical aspects

Medical Data Retrieval [email protected]

René Donner

Deep Learning

Page 2: Donner - Deep Learning - Overview and practical aspects
Page 3: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Overview

3

The (amazing) things Deep Learning can do

How does it work?

How can you start with DL?

Page 4: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Roughly …

4

Deep learning finds patternsin data corresponding tohigh-level, abstract concepts

Page 5: Donner - Deep Learning - Overview and practical aspects

What can it do?

Page 6: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

What it can be used for

6

Image recognition

Text understanding, translation

Voice recognition

Playing video games

Driving cars

Page 7: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Image recognition

7

Page 8: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Scene labeling

8

http://www.purdue.edu/newsroom/releases/2014/Q1/smartphone-to-become-smarter-with-deep-learning-innovation.html

Page 9: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Text recognition

9

http://www.pyimagesearch.com/2014/09/22/getting-started-deep-learning-python/

Large-Scale Deep Learning for Intelligent Computer Systems, Jeff Dean, Google, BayLearn 2015

Page 10: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Text understanding

10

2013 Glove: Global Vectors for Word Representation, Jeffrey Pennington, Richard Socher and Christopher D. Manning

Page 11: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Word embeddings

11

Page 12: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Information extraction / Reasoning

12

MetaMind

Page 13: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Some well know research groups

13

Stanford / BaiduAndrew Ng

NYU / FacebookYann LeCun

UToronto / GoogleGeoffrey Hinton

Page 14: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

NVIDIA

14

brand new: M40(same as Geforce GTX Titan X)

Images: NVIDIA website

Page 15: Donner - Deep Learning - Overview and practical aspects

How does it work?

Page 16: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Difference to classic ML

16

http://rinuboney.github.io/2015/10/18/theoretical-motivations-deep-learning.html

Page 17: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Deep learning

17

http://theanalyticsstore.ie/deep-learning/

Page 18: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Visualization

18

1. Layer

higher Layers

Emergence of Object-Selective Features in Unsupervised Feature Learning, Adam Coates, NIPS 2012

Page 19: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Deep learning

19

How does it work?

http://theanalyticsstore.ie/deep-learning/ http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma

Page 20: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Optimization

20

Stochastic gradient descent

Automatic differentiation

blog.datumbox.com

Page 21: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Local minima

21

Less problematic than thought - saddle points

https://ganguli-gang.stanford.edu/figures/14.Saddlepoint.jpg

Page 22: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Deep learning

22

Low level features of color images

https://www.coursera.org/course/neuralnets

Page 23: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Deep learning

23

http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf

Page 24: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

ImageNet topologies

24

ImageNet Classification with Deep Convolutional Neural Networks", Alex Krizhevsky

“Inception” deep neural network architecture. Source: Christian Szegedy et. al. Going deeper with convolutions. CVPR 2015

Page 25: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

MNIST - Demo

25

Page 26: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

MNIST

26

http://deeplearning4j.org/rbm-mnist-tutorial.html

Page 27: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Deep learning - why does it work?

27

Can cope with huge amounts of data

Learns small invariances

Overcomplete, sparse, representations

Learn Embedding

Lots of data

Recent advance: it is actually computable!

Page 28: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Deep learning - pros

28

Not-domain specific

Supervised / Semi-supervised / Unsupervised

Classification / regression in last layer

Simple math

Hip

Page 29: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Deep learning - cons

29

Lots of meta-parameters

Needs a lot of data

Very compute intensive

Hip

Page 30: Donner - Deep Learning - Overview and practical aspects

Getting started with DL

Page 31: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Frameworks

31

Many different DL toolboxes

Efficiency important (GPU)

Attention to numerical issues

Page 32: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Frameworks

32

Caffehttp://caffe.berkeleyvision.org/Plain text filesFastest CNN, GPU

Kerashttps://github.com/fchollet/kerasPython, on top of Theano

TensorFlowhttp://tensorflow.org/Python, by Google

MXNethttps://github.com/dmlc/mxnetPython, R, Julia

Slid

e fro

m c

affe

tuto

rial

Page 33: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Tensorflow

33

General gradient descent library

Page 34: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Tutorials

34

Stanford tutorialhttps://deeplearning.stanford.edu/wiki/index.php/UFLDL_TutorialMatlab code snippets

videolectures.nethttp://videolectures.net/deeplearning2015_montreal/

courserahttps://www.coursera.org/course/neuralnets

Page 35: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Practical hints

35

Bengio ArxivPractical Recommendations for Gradient-Based Training of Deep Architectureshttp://arxiv.org/abs/1206.5533http://rinuboney.github.io/2015/10/18/theoretical-motivations-deep-learning.html

Kaggle http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challengehttp://benanne.github.io/2014/04/05/galaxy-zoo.html

Relevant conferences NIPS (https://sites.google.com/site/deeplearningworkshopnips2013/accepted-papers)CVPR, ICMLMany interesting papers on arxiv.org

Page 36: Donner - Deep Learning - Overview and practical aspects

René Donner Deep Learning

Current research topics

36

Parallelization

What is deep learning, actually?

Alternative, faster, simpler methods

Multi-domain, transfer learning

Page 37: Donner - Deep Learning - Overview and practical aspects

Medical Data Retrieval [email protected]

René Donner

Deep Learning


Related Documents