پردازش زبان طبیعی(NLP)

1.1. Course introduction

10

2.1. Linguistics Knowledge

11

3.1. NLP Challenges

24

4.1. Zipf’s Law

11

5.1. Probabilistic Language Modeling: Motivation and Estimation

40

6.1. An Intro to Google Colab

13

7.1. Unigram & Bigram Language Models

17

1.2. Probabilistic Language Modeling: Smoothing1

15

2.2. Probabilistic Language Modeling: Smoothing2

17

3.2. Probabilistic Language Modeling: Evaluation

31

4.2. Neural Language Modeling

40

5.2. Neural Language Model

33

1.3. Sparse Word Representation 1

36

2.3. Sparse Word Representation 2

30

3.3. Dense Word Representation 1

22

4.3. Dense Word Representation 2

29

5.3. Dense Word Representation 3

29

6.3. TF-IDF representation and Word2Vec by gensim

19

1.4. Text Preprocessing

36

2.4. Spell Correction

27

3.4. Persian Text Processing

31

4.4. Text Processing Using NLTK Library

13

1.5. Sequence Processing Recurrent Neural Networks I

27

2.5. Sequence Processing Recurrent Neural Networks II

16

3.5. Sequence Processing Transformers

28

4.5. Text Classification with RNNs

27

1.6. Dialogue Systems: Introduction

16

2.6. Dialogue Systems: Properties of Human Conversation

32

3.6. Dialogue Systems: Rule-based Conversational Chatbots

19

4.6. Dialogue Systems: Corpus-based Conversational Chatbots I

24

5.6. Dialogue Systems: Corpus-based Conversational Chatbots II

11

6.6. Dialogue Systems: Corpus-based Conversational Chatbots III

15

7.6. Seq-to-seq model with Transformer

36

1.7. Contextualized Representation I

12

2.7. Contextualized Representation II

25

3.7. Contextualized Representation for Corpus-Based Chatbots I

26

4.7. Contextualized Representation for Corpus-Based Chatbots II

15

5.7. Use BERT for Classification

16

6.7. Chatbot with GPT-2

22

1.8. Dialogue Systems: GUS, Simple Frame-based Dialogue Systems

27

2.8. Dialogue Systems: Frame-based Dialogue-state Architecture I

19

3.8. Dialogue Systems: Frame-based Dialogue-state Architecture II

27

4.8. Dialogue Systems: Evaluation

23

5.8. Summary

11

6.8. Slot Filling using RNNs

21

1.9. جلسه اول پرسش و پاسخ

33

2.9. جلسه چهارم پرسش و پاسخ

44

3.9. جلسه ششم پرسش و پاسخ

12

1.10. تمرین هفته اول

19

2.10. تمرین هفته دوم

21

3.10. تمرین هفته سوم

33

4.10. تمرین هفته چهارم

41

5.10. تمرین هفته پنجم

51

6.10. تمرین هفته ششم

13

7.10. تمرین هفته هفتم

35