This document provides an overview of deep learning concepts including linear regression, neural networks, convolutional neural networks, transfer learning, and generative adversarial networks. It discusses techniques such as data augmentation, dropout, and pretrained models. It also covers visualizing networks, one shot learning, and using cognitive services for computer vision tasks. The goal is to provide practical guidance on deep learning topics and code examples.

1. A PRACTICAL GUIDE TO DEEP LEARNING
Tess Ferrandez – Microsoft - @TessFerrandez

4. from @teenybiscuit

5. FROM ML TO DEEP LEARNING
Predicting the price of a house

7. int EstimatePrice(...){
price = 10000 +
6700 * area_in_sqm +
20000 * has_pool +
10000 * new_kitchen +
5000 * neighborhood_quality;
return price;
}
Price = b + w1*area_in_sqm + w2*has_pool + ...

8. Price = b + w1*area_in_sqm
[LINEAR REGRESSION]
[GRADIENT DESCENT]

9. int EstimatePrice(...){
price = 10000 +
6700 * area_in_sqm +
20000 * has_pool +
10000 * new_kitchen +
5000 * neighborhood_quality;
return price;
}
Price = b + w1*area_in_sqm + w2*has_pool + ...
[LINEAR REGRESSION]

10. [NEURAL NET]

11. [NEURAL NET]

13. UNSTRUCTURED DATA
Machine Learning on Images

14. [HISTOGRAMS]

15. [PURE PIXELS]

16. F. Chollet – Creator of Keras

18. [DENSLEY CONECTED] [CONVOLUTIONAL] [RECURRENT]
NETWORK ARCHITECTURES

20. CONVOLUTIONAL NEURAL NETWORKS
The basics

22. http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution

23. https://en.wikipedia.org/wiki/Kernel_(image_processing)

24. 0*1+0*1+0*1 + 0*0+0*0+0*0 + 0*-1+0*-1+0*-1 = 01*1+1*1+1*1 + 1*0+1*0+1*0 + 0*-1+0*-1+0*-1 = 3

27. https://www.quora.com/How-can-l-explain-the-dimensionality-reduction-in-convolutional-neural-network-CNN-from-this-image

29. 0 0
2 3

30. layer 1 layer 2 layer 3 layer 4 layer 5
https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf

32. CNNs IN PRACTICE
Finally time for some code

33. 1 PREPARE DATA
CREATE MODEL
TRAIN MODEL (UNTIL OVERFIT)
GET MORE DATA OR ADD DROPOUT
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

34. 1 PREPARE DATA
CREATE MODEL
TRAIN MODEL (UNTIL OVERFIT)
GET MORE DATA OR ADD DROPOUT
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

38. 1 PREPARE DATA
CREATE MODEL
TRAIN MODEL (UNTIL OVERFIT)
GET MORE DATA OR ADD DROPOUT
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

42. 1 PREPARE DATA
CREATE MODEL
TRAIN MODEL (UNTIL OVERFIT)
GET MORE DATA OR ADD DROPOUT
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

43. 1 EPOCH = 1 pass through the training data

45. Time for the Epoch
Training data
Validation data

46. MODEL LOSS ACCURACY
BASIC 0.2507 91.05%

48. OOPSIE DOOPSIE!
We’re overfitting

49. 1 PREPARE DATA
CREATE MODEL
TRAIN MODEL (UNTIL OVERFIT)
GET MORE DATA OR ADD DROPOUT
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

50. [DATA AUGMENATION]

51. Chihuahua the movie

52. [DROPOUT]
http://jmlr.org/papers/v15/srivastava14a.html

54. 1 PREPARE DATA
CREATE MODEL
TRAIN MODEL (UNTIL OVERFIT)
GET MORE DATA OR ADD DROPOUT
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

56. 1 PREPARE DATA
CREATE MODEL
TRAIN MODEL (UNTIL OVERFIT)
GET MORE DATA OR ADD DROPOUT
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

59. PREDICTED
Chihuahua Muffin
TRUE
ChihuahuaMuffin

60. MODEL LOSS ACCURACY
BASIC 0.2507 91.05%
AUGMENTATION 0.1988 93.68%

61. 1 PREPARE DATA
CREATE MODEL
TRAIN MODEL (UNTIL OVERFIT)
GET MORE DATA OR ADD DROPOUT
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

62. TRAINING ON PRETRAINED MODELS
Feature Extraction and Transfer Learning

63. F. Chollet – Deep Learning with Python

64. 1 EXTRACT FEATURES FROM A PRE-TRAINED
MODEL
CREATE A SHALLOW NETWORK TO PREDICT2

65. 1 EXTRACT FEATURES FROM A PRE-TRAINED
MODEL
CREATE A SHALLOW NETWORK TO PREDICT2

68. 1 EXTRACT FEATURES FROM A PRE-TRAINED
MODEL
CREATE A SHALLOW NETWORK TO PREDICT2

69. 1 EXTRACT FEATURES FROM A PRE-TRAINED
MODEL
CREATE A SHALLOW NETWORK TO PREDICT2

73. MODEL LOSS ACCURACY
BASIC 0.2507 91.05%
AUGMENTATION 0.1988 93.68%
FEATURE EXTR. 0.01253 99.47%

75. 1 ADD DENSE LAYERS ON TOP OF CONV. BASE
FREEZE THE CONV. BASE
TRAIN MODEL
UNFREEZE SOME LAYERS
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

76. 1 ADD DENSE LAYERS ON TOP OF CONV. BASE
FREEZE THE CONV. BASE
TRAIN MODEL
UNFREEZE SOME LAYERS
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

78. 1 ADD DENSE LAYERS ON TOP OF CONV. BASE
FREEZE THE CONV. BASE
TRAIN MODEL
UNFREEZE SOME LAYERS
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

80. MODEL LOSS ACCURACY
BASIC 0.2507 91.05%
AUGMENTATION 0.1988 93.68%
FEATURE EXTR. 0.01253 99.47%
TRANSFER LEARNING 0.01842 100%

81. 1 ADD DENSE LAYERS ON TOP OF CONV. BASE
FREEZE THE CONV. BASE
TRAIN MODEL
UNFREEZE SOME LAYERS
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

83. MODEL LOSS ACCURACY
BASIC 0.2507 91.05%
AUGMENTATION 0.1988 93.68%
FEATURE EXTR. 0.01253 99.47%
TRANSFER LEARNING 0.01842 100%
TRANSFER UNFREEEZE 0.01081 99.47%

84. 1 ADD DENSE LAYERS ON TOP OF CONV. BASE
FREEZE THE CONV. BASE
TRAIN MODEL
UNFREEZE SOME LAYERS
TRAIN MODEL
PREDICT ON TEST DATA
2
3
4
5
6

85. VISUALIZE THE NETWORK
Understanding what it learns

86. [Grad-CAM Heatmaps]

87. layer 1 layer 2 layer 3 layer 4 layer 5
https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf

88. ADVANCED TOPICS
Extra Extra!

89. GrabCut
OpenCV
http://www.australiandoglover.com/2016/05/chihuahua-breed-profile.html

90. [GENERATOR (forger)]
NETWORK ARCHITECTURES
[ORIGINALS]
[DETECTOR (detective)]GENERATIVE ADVERSARIAL NETWORK
Generating images

91. Alec Radford – DCGAN paper - https://arxiv.org/pdf/1511.06434.pdf

92. https://deepart.io/

93. Ian Goodfellow: Adversarial Examples

94. DeepFace from Facebook
Image: Daily Mirror
ONE SHOT LEARNING
DeepFace and FaceNet

95. FaceNet from Google
Image: https://omoindrot.github.io/triplet-loss

96. http://slideshare.net/Tess
@TessFerrandez

97. QUICK, PRE-FAB AND EASY
Cognitive Services

98. COMPUTER VISION
Azure Cognitive Services

99. CUSTOM VISION
Azure Cognitive Services

102. A PRACTICAL GUIDE TO DEEP LEARNING
Tess Ferrandez – Microsoft - @TessFerrandez