The document discusses how predictive analytics using neural networks, such as recurrent neural networks and long short term memory cells, can be applied to problems in industrial IoT, giving examples of how these techniques could be used to predict risky drivers from sensor data and to predict future customer purchases from shopping history data. It also outlines potential future directions for predictive analytics, such as using reinforcement learning approaches like Q-learning to develop intelligent agents.

1. Predictive Analytics to
the rescue of IoT
Rudradeb Mitra | https://www.linkedin.com/in/mitrar/

2. 2003, Bremen, Germany

3. 03/06/2010
2010, Bremen, Germany

4. Judge Business School, MPhil in Technology Policy 03/06/2010 4

6. 2016, Berlin, Germany

7. https://www.linkedin.com/pulse/predictive-analytics-rescue-internet-things-rudradeb-mitra/

8. Predictive Analytics

10. Industrial IoT

12. Predictive Analytics: Two cases

13. Case I:
Predicting risky drivers

15. Case II:
Predicting buying behavior

17. How?

18. Neural Networks

19. Neural Network - Supervised learning
Learning via Back propagation
Approximation
function

23. Activation function

24. How can Neural Networks be used?

25. Predicting risky drivers

26. Classification

27. Bucket1 Bucket2 Bucket3
Classification

28. Extracted
Features
Q 1
Q 2
Q 3
Q 4
h1
h2
h3
h4
h5
High
Mediu
m
Low
INPUT LAYER OUTPUT LAYER

29. In partnership with:

30. ROAD VIKINGS
Driver’s app
Record a trip Trip feedback Goals & challenges Rewards

31. Extracted
Features
Q 1
Q 2
Q 3
Q 4
h1
h2
h3
h4
h5
High
Mid
Low
INPUT LAYER OUTPUT LAYER
Score,
Medium friend score

33. Predicting next order

34. Recurrent Neural Networks

35. RNN
Y0
X0
RNN
Y1
X1
RNN
Y2
X2
RNN
Y3
X3
RNN
Y4
X4

36. Vanishing gradient problem

37. Long Short Term Memory
Gates
Picture taken from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

38. Long Short Term Memory
Cell state
Forget, Update and Output
Picture taken from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

39. Long Short Term Memory cell - Forget gate
Forgetting a past product
What to
forget?
Picture taken from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

40. Long Short Term Memory cell - Input gate
What to
update?
Update
state
Picture taken from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Adding a new future product

41. Long Short Term Memory cell - New internal state
Forgetting Adding
Picture taken from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

42. Long Short Term Memory cell - Output gate
Output
Picture taken from
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Next product to order

43. NN NN NN
f
NN
Forget
gate
Input
gate
Output
gate
Product 1
New information
Forget an old
information
Updated
information
added

44. Zolando’s approach to Prediction
Picture taken from
https://jobs.zalando.com/tech/blog/deep-learning-for-understanding-consumer-histories/?gh_src=4n3gxh1

45. Tensorflow LSTM API

46. Architecture

47. Application EngineDatabase
Learning Algorithm
Perform complex queries
Predictions
Predictive Analytics architecture

48. Future direction

50. Environment
Agent
State
Reward
Action
Reinforcement learning

51. Q-learning
• Reward function for each action in a given state
• Q(s,a)=R(s,a) + γ * max [Q(next states, all actions)]
• Initial Q is 0
• Value of γ is (0,1) depends how much you want future
actions to influence current learning.

52. Q(s,a)=R(s,a) + γ * max [Q(next states, all actions)]
If γ = 0.8
Q(s,a)=R(s,a) +
0.8 * max [Q(next states, actions)]
eg
Q(3,1)=0 + 0.8 [ max(Q(1,3),Q(1,5)) ] = 400
Example taken from
http://mnemstudio.org/path-finding-q-learning-tutorial.htm

53. Hidden
layer
Input
layer
Output
layer
0
1
2
3
4
5
State
Q value
Loss
= Σ (Qtargeti - Qi)
Qtargeti=r + γ * max(Q(s',a'))

54. Case studies?