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Deep  Learning
And  Its  Applications:  Computer  Vi...
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•  Object  Recognition
•  Image  Categorization
•  Sce...
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•  OpenCV  
•  SIFT
•  Filters/Edge  Detection
•  Feat...
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•  Representation  Learning  
•  More  precise  than  ...
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•  Representation  Learning
•  Position  Invariance  w...
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•  Normal  pixels  –  0-­‐‑255  –  
normalization
•  S...
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•  Faces  =  a  collection  of  images.
•  With  persi...
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DL4J’s  Facial  Reconstructions
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•  Slices  of  a  feature  space  (Max  pooling)	
•  L...
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Visual  Example  -­‐‑  Convolutions
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Pen  Strokes
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•  Facebook  uses  facial  recognition  to  make  
its...
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•  2  layers  of  neuron-­‐‑like  nodes.	
•  The  1st ...
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•  A  stack  of  RBMs.	
•  Each  RBM’s  hidden  layer ...
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•  2  DBNs.
•  1st  DBN  *encodes*  data  into  vector...
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Deep  Autoencoder  Architecture
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Image  Search  Results
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•  Top-­‐‑down  &  hierarchical  rather  than  feed-­‐...
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RNTNs  &  Scene  Composition
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Deep Learning and its Applications - Computer Vision

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Deep Learning and its Applications - Computer Vision Zipfian Academy Meetup

Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.

The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.

Published in: Engineering
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Deep Learning and its Applications - Computer Vision

  1. 1. The image part with relationship ID rId14 was not found in the file. { Deep  Learning And  Its  Applications:  Computer  Vision Adam  Gibson {  deeplearning4j.org  //  skymind.io  //  zipfian  academy
  2. 2. The image part with relationship ID rId14 was not found in the file. •  Object  Recognition •  Image  Categorization •  Scene  Parsing •  Face  Recognition Computer  Vision:  A  Primer
  3. 3. The image part with relationship ID rId14 was not found in the file. •  OpenCV   •  SIFT •  Filters/Edge  Detection •  Feature  Extraction What’s  currently  done?
  4. 4. The image part with relationship ID rId14 was not found in the file. •  Representation  Learning   •  More  precise  than  hand-­‐‑done   features •  Non-­‐‑linearities  and  higher-­‐‑order   trends •  Pretrain  and  Hessian  Free This  is  manual!
  5. 5. The image part with relationship ID rId14 was not found in the file. •  Representation  Learning •  Position  Invariance  with  convolutions •  Semantic  Hashing   Deep  Learning  and  Images
  6. 6. The image part with relationship ID rId14 was not found in the file. •  Normal  pixels  –  0-­‐‑255  –   normalization •  Sparse  –  binarization  (depending  on   pixel  presence) Different  kinds  of  images
  7. 7. The image part with relationship ID rId14 was not found in the file. •  Faces  =  a  collection  of  images. •  With  persistent  pa_erns  of  pixels. •  Pixel  pa_erns  =  features. •  Nets  learn  to  identify  features  in  data,  to   classify  faces  as  faces  and  label  them:  John  or   Sarah. •  Nets  train  by  reconstructing  faces  from  features   many  times. •  Measuring  their  work  against  a  benchmark. Facial  recognition
  8. 8. The image part with relationship ID rId14 was not found in the file. DL4J’s  Facial  Reconstructions
  9. 9. The image part with relationship ID rId14 was not found in the file. •  Slices  of  a  feature  space  (Max  pooling) •  Learns  different  portions  for  easily  scalable   and  robust  feature  engineering. Position  Invariance  -­‐‑  Convolutions
  10. 10. The image part with relationship ID rId14 was not found in the file. Visual  Example  -­‐‑  Convolutions
  11. 11. The image part with relationship ID rId14 was not found in the file. Pen  Strokes
  12. 12. The image part with relationship ID rId14 was not found in the file. •  Facebook  uses  facial  recognition  to  make   itself  stickier  and  know  more  about  us. •  Government  agencies  use  it  to  secure   national  borders. •  Video  game  makers  use  it  to  construct  more   realistic  worlds. •  Stores  use  it  to  identify  customers  and  track   behavior. What  are  faces  for?
  13. 13. The image part with relationship ID rId14 was not found in the file. •  2  layers  of  neuron-­‐‑like  nodes. •  The  1st  is  the  visible,  or  input,  layer •  The  2nd  is  “hidden.”  It  identifies  features  in  input •  Symmetrically  connected. •  “Restricted”  =  no  visible-­‐‑visible  or  hidden-­‐‑hidden   ties •  All  connections  happen  between  layers. Restricted  Bolgmann   Machines  (RBMs)
  14. 14. The image part with relationship ID rId14 was not found in the file. •  A  stack  of  RBMs. •  Each  RBM’s  hidden  layer  à  Next  RBM’s  visible/input   layer.   •  DBNs  learn  more  &  more  complex  features •  Example:   •  1)  Pixels  =  input;   •  2)  H1  learns  an  edge  or  line;   •  3)  H2  learns  a  corner  or  set  of  lines;   •  4)  H3  learns  two  groups  of  lines  forming  an  object   -­‐‑-­‐‑  a  face! •  Final  layer  classifies  feature  groups:  sunset,  elephant,   flower,  John,  Sarah. Deep-­‐‑Belief  Net  (DBN)
  15. 15. The image part with relationship ID rId14 was not found in the file. •  2  DBNs. •  1st  DBN  *encodes*  data  into  vector  of  10-­‐‑30   numbers  =  Pre-­‐‑training. •  2nd  DBN  decodes  data  into  original  state. •  Backprop  only  happens  on  2nd  DBN •  2nd  is  the  fine-­‐‑tuning  stage  (reconstruction  entropy). •  Reduces  documents  or  images  to  compact  vectors  . •  Useful  in  search,  QA  and  information  retrieval. Deep  Autoencoder
  16. 16. The image part with relationship ID rId14 was not found in the file. Deep  Autoencoder  Architecture
  17. 17. The image part with relationship ID rId14 was not found in the file. Image  Search  Results
  18. 18. The image part with relationship ID rId14 was not found in the file. •  Top-­‐‑down  &  hierarchical  rather  than  feed-­‐‑forward  (DBNs). •  Handles  sequence-­‐‑based  classification,  windows  of  several   events,  entire  scenes  (multiple  objects). •  Features  themselves  are  vectors.   •  A  tensor  =  a  multi-­‐‑dimensional  matrix,  or  multiple  matrices  of   the  same  size. Recursive  Neural  Tensor  Net
  19. 19. The image part with relationship ID rId14 was not found in the file. RNTNs  &  Scene  Composition

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