This document provides an overview of deep learning, including a brief history, popular network architectures like CNNs and RNNs, applications such as image recognition and machine translation, and next waves in the field including self-driving cars and medical applications. Popular deep learning models are able to achieve human-level performance on tasks like image recognition, games, and Go by learning complex patterns from large datasets using neural networks with many layers. Deep learning has revolutionized fields like computer vision, natural language processing, and reinforcement learning by enabling systems to learn tasks from massive amounts of data without being explicitly programmed.

1. Deep Learning Review
Ming-Shing Su
mingshing.su@gmail.com
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2. Outline
 Artificial Intelligence, Machine Learning and
Deep Learning
 Brief History of Deep Learning
 Popular Network Architectures
 Applications
◦ MNIST
◦ ImageNet
◦ Translation
◦ Atari 2600 games
◦ AlphaGo
◦ :
 Next Waves
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3. Artificial Intelligent, Machine
Learning and Deep Learning
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4. Artificial Intelligent
 make computers think the way
humans think;
 simulate the kinds of things that
humans do;
 solve problems in a better and faster
way than humans do
 Approach examples:
◦ Expert Systems
◦ Hand-coded Inferring models
◦ Machine Learning
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5. Machine Learning
 Give data to machines and they learn
on their own how to perform the task
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Hand-coded
rules
Programs
Input
Output
(Traditional)
Learning
rules
Programs
Input
Output
(Machine Learning)
Data

6. Types of Machine Learning
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7. Machine Learning
Approaches
 Approach examples
◦ Logistic Regression
◦ Support Vector Machine (SVM)
◦ Random Forests
◦ Deep Learning
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8. Cases Example –
Predict the onset of diabetes
 P1: Number of times pregnant
 P2: Plasma glucose concentration a 2
hours in an oral glucose tolerance test
 P3: Diastolic blood pressure (mm Hg)
 P4: Triceps skin fold thickness (mm)
 P5: 2-Hour serum insulin (mu U/ml)
 P6: Body mass index (weight in
kg/(height in m)^2)
 P7: Diabetes pedigree function
 P8: Age (years)
 Class variable (0 or 1)
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9. Predict the onset of diabetes
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10. Predict the onset of diabetes
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11. Deep Learning
 using "deep” layers neural network
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12. Neural Network - Neuron
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sigmoid relu

13. Back Propagation
 8x9x9x9x4 neural networks
 Total 301parameters
◦ Layer1: 8x9+9
◦ Layer 2: 9x9+9
◦ Layer 3: 9x9+9
◦ Output layer: 9x4+4
 Backpropagation
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14. Brief History of Neural Network
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15. Why Neural Network Can Learn Now?
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 Big Data Emerging
 Computation Power Boosting
 Algorithms Improving

16. Why Deep Learning Works?
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17. Basic Neural Network Models
 MLP – MultiLayer Perceptron
 CNN – Convolution Neural Network
 RNN – Recurrent Neural Network
 LSTM – Long Short-Term Memory
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18. MLP – MultiLayer Perceptron
 Alternative names
◦ Full connected network
◦ Densely connected network
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19. CNN – Convolution Neural Network
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20. RNN – Recurrent Neural Network
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21. LSTM – Long Short-Term Memory
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22. MNIST
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 Mixed National Institute of Standards and
Technology database
 a large database of handwritten digits
 dimensions: 28x28 (768 pixels)
 60,000 training images and 10,000 testing images
 Best accuracy rate: 99.79% (2016)

23. MNIST - MLP
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From: www.cybercontrols.org

24. MNIST - CNN
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From: www.cybercontrols.org

25. MNIST - code
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26. Image Recognition
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27. Image Recognition
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28. Image Recognition
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29. ImageNet Contest Rules
 ImageNet containing the 1000
categories and 1.2 million images
 given an image an algorithm will
produce 5 class labels
 The winner achieves the minimum
average error across all test images.
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30. ImageNet Contest Error Rate
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3.57%

31. ImageNet Image Examples
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 partridge  hen  black_grous
e
 ruffed_grous
e
 prairie_chicke
n
 quails

32. AlexNet
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33. Revolution of Depth
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34. Revolution of Depth
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35. Translation
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36. Translation Systems
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(over-simplified)
I
see
…
看
到
…
我
I see …
看 到我

37. Real Google GNMT System
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38. Musical Composition
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39. Pix2Pix
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40. Pix2Pix
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demo: https://affinelayer.com/pixsrv/

41. PaintsChainer
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demo: https://paintschainer.preferred.tech/

42. PaintsChainer
 1 month reading/coding, 1 month
training
 Total 1119 lines (including space lines
and comments), Open Source
◦ Preprocessing: 156 lines
◦ Neural Networks: 276 lines
◦ Training Code: 364 lines
◦ Image Generation: 323 lines
 Training set: 770K images
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43. Neural Doodle
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Renoir’s River Bank

44. Neural Doodle
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45. QuickDraw
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https://quickdraw.withgoogle.com/

46. SuperResolution
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47. Image/Video Captioning
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48. Image/Video Captioning
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49. StoryTeller
 http://www.cs.toronto.edu/~rkiros/adv_L.html
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A boy won't be able to do it, but by the
time being a bully , he was a member of
the team. His opponent on the ball had
been a fair play. He didn't know how to
play a game. He didn't know how to tie
himself up in a flash. As far as he was
concerned , the girl in the suit kicked him
in the balls of her feet, giving her a quick
kiss on the cheek. He did not know
whether or not she was going to win the
championship, and that's how he
managed to win the championship.

50. Visual Question Answering
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51. Face Recognition
 NEC face recognition / real time
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52. Face Recognition
 FBI getting outgunned by Facebook’s facial
recognition technology
◦ FBI: returns a ranked list of 50 possibilities, and only promises an
85 percent chance
◦ Facebook: give two pictures, and it can tell you with 97 percent
accuracy whether they’re the same person
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53. VGG Face Recognition
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Face inputs
Face classification
Trainable params: 145,002,878
2622 classes
Training set: 2622 x 1000

54. Speech Recognition
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55. Text-to-Speech
 WaveNet: A Generative Model for Raw
Audio
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56. Atari 2600 Games
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57. Reinforcement Learning
 From: Mark Changhttps://www.slideshare.net/ckmarkohchang/language-understanding-for-
textbased-games-using-deep-reinforcement-learning
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58. AlphaGo
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59. AlphaGo
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 From: Mark Chang
https://www.slideshare.net/ckmarkohchang/alphago-in-
depth

60. Self-Driving
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61. Reduce Data Center Cooling
Bill
 DeepMind AI Reduces Google Data Centre
Cooling Bill by 40%
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62. Diabetic Retinopathy
Diagnoses
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63. Skip Cancer Diagnoses
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64. Healthcare
 First FDA Approval For Clinical Cloud-Based Deep Learning
In Healthcare
 Arterys Cardio DL provides automated, editable ventricle
segmentations based on conventional cardiac magnetic
resonance imaging (MRI) exams that are as accurate as
segmentations performed manually by experienced
physicians.
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65. Healthcare
 Imperial College London researchers hope to
provide automated, image-based assessments of
traumatic brain injuries
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66. Healthcare
 NYU docs are using machine learning to stop
a stealthy disease before it’s too late
 Behold.ai works to reduce the number of
incorrect diagnoses
 Enlitic analyzes medical images to identify
tumors, nearly invisible fractures, and other
medical conditions.
 Toronto University develops a “genetic
interpretation engine” that quickly identifies
cancer-causing mutations.
 Deep Genomics studies how genetic
variations lead to disease, transforming
personalized medicine and therapies.
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67. ChatBot
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 Rule-Based
 Statistic-Based (Machine Learning)

68. 68

69. 69
Q & A