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Deep Learning:

and Deep Data-Science
12	
  May	
  2015
@graphific
Roelof Pieters
www.csc.kth.se/~roelof/
roelof@kth.se Graph Technologies R&D
roelof@graph-technologies.com
slides online at:

https://www.slideshare.net/roelofp/deep-learning-as-a-catdog-detector
BUT FIRST…
CAT PERSON? DOG PERSON?
are you a…
in the next few minutes
DETECTOR
we ll be making a
main Libraries
•sckikit-learn (machine learning) 

http://scikit-learn.org
•caffe (deep learning) - for training deep neural nets
(for today: loading a pre-trained one) 

http://caffe.berkeleyvision.org
•theano (efficient gpu-powered math)

http://www.deeplearning.net/software/theano/
•ipython notebook

http://ipython.org/notebook.html
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
Code is ahead, soon… 

I promise :)
“Data science is clearly a blend of the
hackers' art, statistics and machine
learning…”
—Hilary Mason & Chris Wiggins, 2010
Data Science ?
(Drew Connoway 2010)
1 feature
> Features = Awesomeness
1 feature
2 features
> Features = Awesomeness
1 feature
2 features
too few features/dimensions = overfitting
1 feature
2 features
too few features/dimensions = overfitting
3 features
> Features = Awesomeness
More Features = Awesomeness!
1 feature
2 features 3 features
++ Data Needs also grow!
(picture by Dato)
(picture by Dato)
Deep Learning?
•A host of statistical machine learning
techniques
•Enables the automatic learning of feature
hierarchies
•Generally based on artificial neural
networks
(picture by Dato)
Deep Learning
Deep Learning?
•Manually designed features are often over-specified,
incomplete and take a long time to design and validate
•Learned Features are easy to adapt, fast to learn

•Deep learning provides a very flexible, (almost?)
universal, learnable framework for representing
world, visual and linguistic information.
•Deep learning can learn unsupervised (from raw
text/audio/images/whatever content) and
supervised (with specific labels like positive/
negative)
(as summarised by Richard Socher 2014)
23
2006+ : The Deep Learning Conspirators
(chart by Clarifai)
Audio Recognition
Image Recognition
(chart by Clarifai)
Natural Langauge Processing
…
Natural Langauge Processing
… …
DL? How ?
almost at the code…
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
(picture by Dato)
Coding time!
our ingredients…
(picture by Dato)
Kaggle’s Cat vs Dog
dataset (25k dog/cat
pictures)
https://www.kaggle.com/c/dogs-vs-cats/data
Pretrained
Convolutional 

Neural Net (CNN)
(picture by Dato)
97% accuracy in < 1h
MultiLayer
Perceptron
Cooking Instructions1 Load Pretrained Net
features
image
2 Extract features for all training images
3. train MLP on those features
Cooking Instructions1 Load Pretrained Net
https://github.com/BVLC/caffe/wiki/Model-Zoo
No Free Lunch… But Free Models!
Deep Learning as a Cat/Dog Detector
# imports demo
# load pretrained deep neural net
(convnet from Krizhevsky et al.'s NIPS 2012 ImageNet classification paper)
demo
Cooking Instructions
features
image
2 Extract features for all training images
1 Load Pretrained Net
# feed image into the network and return internal feature
representation of layer fc6 demo
#extract features from images demo
(…)
#dump features as pickle file demo
Cooking Instructions1 Load Pretrained Net
features
image
2 Extract features for all training images
3. train MLP on those features
Pylearn2:
Multilayer Perceptron (MLP) on top of
extracted features
#imports demo
#load earlier extracted features and labels
#convert to input that pylearn understands demo
# create

layers of

MLP
# with 

softmax

as final layer
# trainer
initialized

with SGD, 

momentum,

dropout
demo
# train/test splits demo
#start our MLP (pylearn experiment method)
(…)
demo
already after 5 min:
90%
1 hour
50%
accuracy
97%
1 hour
94%
start at iteration #2
accuracy
(…)
Deep Learning as a Cat/Dog Detector
So are YOU more like a Dog or Cat?
DETECTOR
(I might put it up as a Flask site online, if people are
interested?)
What 

about

me?
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
Deep Learning as a Cat/Dog Detector
EASY PIEZY…
THATS ALL!
as PhD candidate KTH/CSC:
“Always interested in discussing
Machine Learning, Deep
Architectures, Graphs, and
Language Technology”
roelof@kth.se
www.csc.kth.se/~roelof/
Data Science ConsultancyAcademic/Research
roelof@graph-systems.com
www.graph-technologies.com
Gve Systems
Graph Technologies
64
In Touch!
Wanna Know More?
bit.ly/SthlmDL
• Theano - CPU/GPU symbolic expression compiler in
python (from LISA lab at University of Montreal).
http://deeplearning.net/software/theano/
• Pylearn2 - library designed to make machine learning
research easy. http://deeplearning.net/software/
pylearn2/
• Torch - Matlab-like environment for state-of-the-art
machine learning algorithms in lua (from Ronan
Collobert, Clement Farabet and Koray Kavukcuoglu)
http://torch.ch/
• more info: http://deeplearning.net/software links/
Wanna Play ?
66
Wanna Play ? General Deep Learning
• RNNLM (Mikolov)

http://rnnlm.org
• NB-SVM

https://github.com/mesnilgr/nbsvm
• Word2Vec (skipgrams/cbow)

https://code.google.com/p/word2vec/ (original)

http://radimrehurek.com/gensim/models/word2vec.html (python)
• GloVe

http://nlp.stanford.edu/projects/glove/ (original)

https://github.com/maciejkula/glove-python (python)
• Socher et al / Stanford RNN Sentiment code:

http://nlp.stanford.edu/sentiment/code.html
• Deep Learning without Magic Tutorial:

http://nlp.stanford.edu/courses/NAACL2013/
67
Wanna Play ? NLP
• cuda-convnet2 (Alex Krizhevsky, Toronto) (c++/
CUDA, optimized for GTX 580) 

https://code.google.com/p/cuda-convnet2/
• Caffe (Berkeley) (Cuda/OpenCL, Theano, Python)

http://caffe.berkeleyvision.org/
• OverFeat (NYU) 

http://cilvr.nyu.edu/doku.php?id=code:start
68
Wanna Play ? Computer Vision

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