پردازش زبان طبیعی(NLP)

1.1. Sparse Word Representation 1

36:00

2.1. TF-IDF representation and Word2Vec by gensim

19:16

3.1. Sparse Word Representation 2

30:06

4.1. Dense Word Representation 1

22:04

5.1. Dense Word Representation 2

29:29

6.1. Dense Word Representation 3

29:30

1.2. Contextualized Representation I

12:47

2.2. Contextualized Representation II

25:57

3.2. Contextualized Representation for Corpus-Based Chatbots I

26:14

4.2. Contextualized Representation for Corpus-Based Chatbots II

15:35

5.2. Use BERT for Classification

16:08

6.2. Chatbot with GPT-2

22:24

1.3. تمرین هفته اول

19:41

2.3. تمرین هفته دوم

21:56

3.3. تمرین هفته سوم

33:16

4.3. تمرین هفته چهارم

41:34

5.3. تمرین هفته پنجم

51:16

6.3. تمرین هفته ششم

13:32

7.3. تمرین هفته هفتم

35:11

1.4. Text Preprocessing

36:01

2.4. Spell Correction

27:58

3.4. Persian Text Processing

31:27

4.4. Text Processing Using NLTK Library

13:35

1.5. جلسه اول پرسش و پاسخ

33:09

2.5. جلسه چهارم پرسش و پاسخ

44:03

3.5. جلسه ششم پرسش و پاسخ

12:21

1.6. Dialogue Systems: Properties of Human Conversation

32:17

2.6. Dialogue Systems: Corpus-based Conversational Chatbots II

11:53

3.6. Dialogue Systems: Corpus-based Conversational Chatbots III

15:06

4.6. Dialogue Systems: Rule-based Conversational Chatbots

19:23

5.6. Dialogue Systems: Corpus-based Conversational Chatbots I

24:24

6.6. Seq-to-seq model with Transformer

36:36

7.6. Dialogue Systems: Introduction

16:31

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

27:03

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

19:26

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

27:27

4.7. Dialogue Systems: Evaluation

23:21

5.7. Summary

11:15

6.7. Slot Filling using RNNs

21:24

1.8. Sequence Processing Recurrent Neural Networks I

27:55

2.8. Sequence Processing Recurrent Neural Networks II

16:39

3.8. Sequence Processing Transformers

28:35

4.8. Text Classification with RNNs

27:25

1.9. Probabilistic Language Modeling: Evaluation

31:46

2.9. Neural Language Model

33:43

3.9. Probabilistic Language Modeling: Smoothing1

15:41

4.9. Neural Language Modeling

40:11

5.9. Probabilistic Language Modeling: Smoothing2

17:01

1.10. Course introduction

10:55

2.10. Linguistics Knowledge

11:48

3.10. NLP Challenges

24:39

4.10. Zipf’s Law

11:09

5.10. Probabilistic Language Modeling: Motivation and Estimation

40:59

6.10. An Intro to Google Colab

13:20

7.10. Unigram & Bigram Language Models

17:18