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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 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.

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  • 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. 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. 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. 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. 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. 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. 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. The image part with relationship ID rId14 was not found in the file. DL4J’s  Facial  Reconstructions
  • 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. The image part with relationship ID rId14 was not found in the file. Visual  Example  -­‐‑  Convolutions
  • 11. The image part with relationship ID rId14 was not found in the file. Pen  Strokes
  • 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. 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. 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. 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. The image part with relationship ID rId14 was not found in the file. Deep  Autoencoder  Architecture
  • 17. The image part with relationship ID rId14 was not found in the file. Image  Search  Results
  • 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. The image part with relationship ID rId14 was not found in the file. RNTNs  &  Scene  Composition