Presented @ Google IO Extended Porto Alegre 2017

1. Deep
Learning
Applications
for fun and profit

2. Hello!I am Roberto Silveira
EE engineer, ML enthusiast
rsilveira79@gmail.com
@rsilveira79

3. “Fearing a rise of killer
robots is like worrying about
overpopulation on Mars.”
(Andrew Ng)

4. { A short intro }

5. Deep Learning (1)
Artificial Intelligence
e.g. knowledge bases
Source: Deep Learning
(Goodfellow, Bengio, Courville)
Machine Learning
e.g. logistic regression
Representation Learning
e.g. shallow autoencoders
Deep Learning
e.g. MLPs

6. Source: Deep Learning
(Goodfellow, Bengio, Courville)
Rule-based
INPUT
OUTPUT CAT
Classic ML Deep Learning
Hand
designed
program
CAT
Hand
designed
features
Mapping
from
features
CAT
CAT
Features
Mapping
from
features
Simple
Features
+ more
abstract
features
Mapping
from
features
Representation Learning
Deep Learning (2)

7. Visualizing distributed representation
Source: Convolutional networks and applications in vision
(Yan LeCun et al., 2010)

8. Visualizing distributed representation
Source: Visualizing and Understanding Convolutional Networks
(Zeiler et al., 2013)

9. Visualizing distributed representation
Source: Visualizing and Understanding Convolutional Networks
(Zeiler et al., 2013)

10. Visualizing distributed representation
Source: Visualizing and Understanding Convolutional Networks
(Zeiler et al., 2013)

11. Start of a revolution - why now?
Availability of data Processing power
(GPUs)
Improved models/
techniques
(activations, regularization,
initialization, attention)

12. { Art }

13. A Neural Algorithm of Artistic Style
Source: A Neural Algorithm of Artistic Style
(Gatys et al., 2015)
Style Content

14. Laça-Style

15. Laça-Style

16. Deep Dream
Low-level layer enhancement
High-level layer enhancement
Source: https://goo.gl/U4xhM6

17. Deep Dream

18. Laça-Dreams

19. Laça-Dreams

20. Laça-Dreams

21. Laça-Dreams

22. Laça-Dreams

23. Laça-Dreams

24. { Music, Sound }

25. WaveNet - Speech Generation
Parametric
Concatenative
WaveNet
Music #2
Music #1
Source: WaveNet: A Generative Model for Raw Audio
(Oord et al., 2016)

26. Music recommendation
Collaborative filtering vs content based
Genre
Instruments
Mood
Year
Geolocation of artist
Lyrical themes
Low level (max) - Vibrato singing
Low level (avg) - noise, distortion
Low level (avg) - Chinese pop
Similarity Playlists - Fleet Foxes
Source: Deep content-based music recommendation
(Oord et al., 2013)

27. { Image, Photos }

28. { GANs

29. Generative Adversarial Networks
Source: Thomas Paula (slideshare)
Goal: produce counterfeit money
that is as similar as real money
Goal: distinguish between real and
counterfeit money
Generated from Gaussian or
Normal distribution (usually)
Generated instance
Training image
generator discriminator

30. }

31. GANs-Image Generation
Source: Generative Visual Manipulation on the Natural Image Manifold
(Zhu et al., 2016)

32. GANs-Super Resolution
Source: Photo-Realistic Single Image Super-Resolution Using a Generative
Adversarial Network, (Zhu et al., 2016)
bicubic SRResNet SRGAN Original (HR)

33. GANs-Super Resolution
Source: Photo-Realistic Single Image Super-Resolution Using a Generative
Adversarial Network, (Zhu et al., 2016)
bicubic SRResNet SRGAN Original (HR)

34. GANs-Super Resolution
Source: Photo-Realistic Single Image Super-Resolution Using a Generative
Adversarial Network, (Zhu et al., 2016)
bicubic SRResNet SRGAN Original (HR)

35. Semantic/Instance Segmentation
Source: Mask R-CNN (He et al., 2017)

36. Semantic/Instance Segmentation
Source: Mask R-CNN (He et al., 2017)

37. Semantic/Instance Segmentation
Source: Mask R-CNN (He et al., 2017)

38. { Language, Text }

39. Language Translation
Non-monotonic
alignment
Source: Learning Phrase Representations using RNN
Encoder-Decoder for Statistical Machine Translation
(Cho et al., 2014)

40. Text Summarization
Source: A Deep Reinforced Model for Abstractive Summarization
(Paulus et al., 2017)
Extractive vs Abstractive

41. { Chatbots, Personal
Assistants }

42. Conversational Bots
Source: A Neural Conversational Model
(Vinyals et al., 2015)
Google Smart Reply

43. Visual Questioning and Answering
Sources: (1)Dynamic Memory Networks for Visual and Textual Question Answering (Xiong et al.,
2016), (2)Inferring and Executing Programs for Visual Reasoning , (Johnson et al., 2017)
2-level input mechanism (visual + text)

44. { Healthcare }

45. Biomedical image segmentation
Source: U-Net: Convolutional Networks for Biomedical Image Segmentation
(Ronneberger et al., 2015)

46. Disease prediction
Source: https://goo.gl/jGK2Jp
ML improvements:
Random forest: +1.7 %
Logistic regression: +3.2 %
Gradient boosting: +3.3 %
Neural nets: +3.6%

47. { Agriculture }

48. Cucumber “Prediction”
Source: https://goo.gl/Tvp3om

49. Crop yield prediction
Source: http://www.descarteslabs.com/
RGB image
Infrared image

50. Crop yield prediction
Source: http://www.descarteslabs.com/
Corn Prediction (State)

51. Crop yield prediction
Source: http://www.descarteslabs.com/
Soy Prediction (State)

52. Kaggle - Understanding the Amazon
Source: https://goo.gl/5eBxnr

53. Kaggle - Understanding the Amazon
Source: https://goo.gl/5eBxnr
Cloudy

54. Kaggle - Understanding the Amazon
Source: https://goo.gl/5eBxnr
Rain forest

55. Kaggle - Understanding the Amazon
Source: https://goo.gl/5eBxnr
Water (rivers & lakes)

56. Kaggle - Understanding the Amazon
Source: https://goo.gl/5eBxnr
Habitation

57. Kaggle - Understanding the Amazon
Source: https://goo.gl/5eBxnr
Agriculture

58. { Sports }

59. Ghosting with Deep Imitation
Learning
Source: Data-Driven Ghosting using Deep Imitation Learning
(Le et al., 2017)
Role and formation discovery
4-5-1

60. { Future }

61. Social AI → AI + emotions
understanding synthesis/generation
Jibo Cozmo

62. { Conclusions }

63. - Deep Learning is here to stay
- AI won’t kill humans (for the time being)
- AI will revolutionize labor market (already is)
- Nothing to do (1) → Learn Python
- Nothing to do (2) → Learn TensorFlow/Keras/PyTorch
- Nothing to do (3) → ML Andrew Ng (Coursera), Deep Learning (Udacity),
Stanford CS231n (Youtube), Stanford CS224d (Youtube), Oxford NLP
(Github)
- Nothing to do (4) → Practice @ Kaggle

64. https://www.meetup.com/Machine-Learning-Porto-Alegre/