We describe how the RNN and LSTM works in Deep Learning.Also, we summarize previous section content about NLP. Tokenization, lemmation, stemm are ;

1. Deep Learning in NLP
微光國際資訊有限公司 2018/06/09

2. About this Section
• Preprocessing data
• Work with NLTK + word2vec
• Introduce Recurrent Neural Networks(RNN)
• Introduce Long Short-Term Memory
Networks(LSTM)

3. Preprocessing Data(Classic)
3

4. Preprocessing Data(NN)
4

5. Difference between Classic and NN
5
Classic NN

6. 這叫做 Feature Engineering

7. 這叫做 Feature Engineering
Bag-of-word
N-gram
例句：Bob went to the market to buy some flowers.
[1, 1, 2, 1, 1, 1, 1, 1, 0, 0, 0]
["Bo", "ob", "b ", " w", "we", "en", ..., "me", "e "," f", "fl", "lo", "ow", "we", "er", "rs"]

8. 這叫做 Feature Engineering
Bag-of-word
N-gram
例句：Bob went to the Market to buy some flowers.
[1, 1, 2, 1, 1, 1, 1, 1, 0, 0, 0]
["Bo", "ob", "b ", " w", "we", "en", ..., "me", "e "," f", "fl", "lo", "ow", "we", "er", "rs"]
缺點在於
1. 太過消耗資源
2.需要專家且經驗不易複製

9. Load Data
9

10. Import NLTK & use tokenize
10

11. use word2vec and store it
11

12. word2vec - distance
12

13. word2vec - similar
13

14. word2vec - calculate distance
14
target = father + woman - man

15. Img that we want u to remember
15

16. 開始NN之前先回想些東西

17. 回想三個東西
• Activation Function
• Loss Function
• Optimizer

18. Activation Function
18

19. Loss Function
19

20. Optimizer
20

21. About this Section
• Preprocessing data
• Work with NLTK + word2vec
• Introduce Recurrent Neural Networks(RNN)
• Introduce Long Short-Term Memory
Networks(LSTM)

22. The problem with forward NN
22
已知這些關係
文字預測 James has cat and it like to drink ___ .
圖像表現

23. The problem with forward NN
23
改變這些關係
文字預測 James has cat and it like to drink ___ .
圖像表現

24. Begin with RNN
24
已知這些關係
可推導出 Y2
可推導出 Y4
圖像表現

25. Application in RNN
25

26. Application in RNN (Img)
26

27. Application in RNN (Table)
27

28. Have problem in backpropagation
28
想找到 Error 與 weight 的關係
展開之後

29. Recall previous video on youtube
29

30. Have problem in backpropagation with RNN
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一樣想找到關係
展開之後卻發現...
h 產生 recursive

31. Limit recursive times.
31
Backpropagation Through Time (BPTT)
有效但會使梯度
消失
爆炸

32. Another problem in Long-Term Dependence
32
John lives in France. 以下省略五千字…..
這邊省略一點點字..... John speak ___.
距離太遙遠導致記憶消失

33. LSTM 就這樣出世

34. About this Section
• Preprocessing data
• Work with NLTK + word2vec
• Introduce Recurrent Neural Networks(RNN)
• Introduce Long Short-Term Memory
Networks(LSTM)

35. How LSTM differ from RNN ?
35
標準型 RNN ＬＳＴＭ 最小單位

36. How LSTM differ from RNN ?
36
LSTM
可以決定哪些記憶要忘記，哪些要留下

37. Application in LSTM
37

38. What is LSTM ?
38
LSTM 有五個值，四個動作，三個門
• Cell state: This is the internal cell state (that is, memory) of an LSTM cell
• Hidden state: This is the external hidden state used to calculate predictions
• Input gate: This determines how much of the current input is read into the cell state
• Forget gate: This determines how much of the previous cell state is sent into the current cell state
• Output gate: This determines how much of the cell state is output into the hidden state
表過去經驗
表預測結果
• Discard old information(that, forget something…)
• Store which new information in the cell state
• Updated the old cell state Ct-1, into the new cell state Ct
• Output information

39. How does LSTM works ?
39
• Input gate: This determines how much of the current input is read into the cell state (0~1)
• Forget gate: This determines how much of the previous cell state is sent into the current cell state (0~1
• Output gate: This determines how much of the cell state is output into the hidden state (0~1)

40. Classical article in LSTM
40

41. Material in “Understanding LSTM Networks”
41

42. Discard old information(that, forget something…)
42
• Forget gate: This determines how much of the previous cell state is sent into the current cell state (0~

43. Store which new information in the cell state
43
• Input gate: This determines how much of the current input is read into the cell state (0~1)

44. Updated the old cell state Ct-1, into the new cell state Ct
44

45. Output information
45
• Output gate: This determines how much of the cell state is output into the hidden state

46. Practice it by yourself

47. Use these functions to build LSTM cell
47
Input Gate
Output Gate
Forget Gate