| coursera-nlp/assignments/assignment-1/h1-p.4.pdf |
149 KB |
| coursera-nlp/assignments/assignment-1/h1-p.zip |
1 MB |
| coursera-nlp/assignments/assignment-2/h2-p.2.pdf |
169 KB |
| coursera-nlp/assignments/assignment-2/h2.2.zip |
140 KB |
| coursera-nlp/assignments/assignment-3/h3-p.pdf |
152 KB |
| coursera-nlp/assignments/assignment-3/h3.zip |
608 KB |
| coursera-nlp/assignments/assignment-4/h4-p.pdf |
167 KB |
| coursera-nlp/assignments/assignment-4/h4.zip |
1.2 MB |
| coursera-nlp/lectures/week1-01/Natural Language Processing 0.0 Introduction (Part 1) (1117).mp4 |
14.2 MB |
| coursera-nlp/lectures/week1-01/Natural Language Processing 0.1 Introduction (Part 2) (1028).mp4 |
11.9 MB |
| coursera-nlp/lectures/week1-02/Natural Language Processing 1.0 Introduction to the Language Modeling Problem (Part 1) (617).mp4 |
7.5 MB |
| coursera-nlp/lectures/week1-02/Natural Language Processing 1.1 Introduction to the Language Modeling Problem (Part 2) (712).mp4 |
8.3 MB |
| coursera-nlp/lectures/week1-02/Natural Language Processing 1.2 Markov Processes (Part 1) (856).mp4 |
10.2 MB |
| coursera-nlp/lectures/week1-02/Natural Language Processing 1.3 Markov Processes (Part 2) (628).mp4 |
7.4 MB |
| coursera-nlp/lectures/week1-02/Natural Language Processing 1.4 Trigram Language Models (940).mp4 |
11.1 MB |
| coursera-nlp/lectures/week1-02/Natural Language Processing 1.5 Evaluating Language Models Perplexity (1236).mp4 |
14.7 MB |
| coursera-nlp/lectures/week1-03/Natural Language Processing 2.0 Linear Interpolation (Part 1) (746).mp4 |
9.1 MB |
| coursera-nlp/lectures/week1-03/Natural Language Processing 2.1 Linear Interpolation (Part 2) (1135).mp4 |
13.7 MB |
| coursera-nlp/lectures/week1-03/Natural Language Processing 2.2 Discounting Methods (Part 1) (926).mp4 |
11.2 MB |
| coursera-nlp/lectures/week1-03/Natural Language Processing 2.3 Discounting Methods (Part 2) (334).mp4 |
4.4 MB |
| coursera-nlp/lectures/week1-04/Natural Language Processing 3.0 Summary (231).mp4 |
3 MB |
| coursera-nlp/lectures/week10-01/Natural Language Processing 18.0 Introduction (102).mp4 |
1.1 MB |
| coursera-nlp/lectures/week10-01/Natural Language Processing 18.1 Recap of GLMs (740).mp4 |
9.2 MB |
| coursera-nlp/lectures/week10-01/Natural Language Processing 18.2 GLMs for Tagging (Part 1) (526).mp4 |
6.9 MB |
| coursera-nlp/lectures/week10-01/Natural Language Processing 18.3 GLMs for Tagging (Part 2) (735).mp4 |
9.3 MB |
| coursera-nlp/lectures/week10-01/Natural Language Processing 18.4 GLMs for Tagging (Part 3) (706).mp4 |
8.7 MB |
| coursera-nlp/lectures/week10-01/Natural Language Processing 18.5 GLMs for Tagging (Part 4) (600).mp4 |
7.2 MB |
| coursera-nlp/lectures/week10-02/Natural Language Processing 19.0 Introduction (037).mp4 |
700 KB |
| coursera-nlp/lectures/week10-02/Natural Language Processing 19.1 The Dependency Parsing Problem (Part 1) (521).mp4 |
6.4 MB |
| coursera-nlp/lectures/week10-02/Natural Language Processing 19.2 The Dependency Parsing Problem (Part 2) (1353).mp4 |
17.1 MB |
| coursera-nlp/lectures/week10-02/Natural Language Processing 19.3 GLMs for Dependency Parsing (Part 1) (1159).mp4 |
14.2 MB |
| coursera-nlp/lectures/week10-02/Natural Language Processing 19.4 GLMs for Dependency Parsing (Part 2) (828).mp4 |
11 MB |
| coursera-nlp/lectures/week10-02/Natural Language Processing 19.5 Experiments with GLMs for Dep. Parsing (538).mp4 |
6.9 MB |
| coursera-nlp/lectures/week10-02/Natural Language Processing 19.6 Summary (250).mp4 |
3.4 MB |
| coursera-nlp/lectures/week2-01/Natural Language Processing 4.0 The Tagging Problem (1001).mp4 |
13 MB |
| coursera-nlp/lectures/week2-01/Natural Language Processing 4.1 Generative Models for Supervised Learning (857).mp4 |
10.7 MB |
| coursera-nlp/lectures/week2-01/Natural Language Processing 4.2 Hidden Markov Models (HMMs) Basic Definitions (1200).mp4 |
14.9 MB |
| coursera-nlp/lectures/week2-01/Natural Language Processing 4.3 Parameter Estimation in HMMs (1316).mp4 |
16.3 MB |
| coursera-nlp/lectures/week2-01/Natural Language Processing 4.4 The Viterbi Algorithm for HMMs (Part 1) (1407).mp4 |
17 MB |
| coursera-nlp/lectures/week2-01/Natural Language Processing 4.5 The Viterbi Algorithm for HMMs (Part 2) (331).mp4 |
4.2 MB |
| coursera-nlp/lectures/week2-01/Natural Language Processing 4.6 The Viterbi Algorithm for HMMs (Part 3) (733).mp4 |
9.3 MB |
| coursera-nlp/lectures/week2-01/Natural Language Processing 4.7 Summary (150).mp4 |
2.2 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.0 Introduction (028).mp4 |
1017 KB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.1 Introduction to the Parsing Problem (Part 1) (1037).mp4 |
12.6 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.2 Introduction to the Parsing Problem (Part 2) (420).mp4 |
5.1 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.3 Context-Free Grammars (Part 1) (1211).mp4 |
14.5 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.4 Context-Free Grammars (Part 2) (222).mp4 |
2.8 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.5 A Simple Grammar for English (Part 1) (1032).mp4 |
12.6 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.6 A Simple Grammar for English (Part 2) (530).mp4 |
6.4 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.7 A Simple Grammar for English (Part 3) (1121).mp4 |
13.9 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.8 A Simple Grammar for English (Part 4) (220).mp4 |
2.8 MB |
| coursera-nlp/lectures/week3-01/Natural Language Processing 5.9 Examples of Ambiguity (556).mp4 |
6.7 MB |
| coursera-nlp/lectures/week3-02/Natural Language Processing 6.0 Introduction (112).mp4 |
1.3 MB |
| coursera-nlp/lectures/week3-02/Natural Language Processing 6.1 Basics of PCFGs (Part 1) (943).mp4 |
11.6 MB |
| coursera-nlp/lectures/week3-02/Natural Language Processing 6.2 Basics of PCFGs (Part 2) (826).mp4 |
10.8 MB |
| coursera-nlp/lectures/week3-02/Natural Language Processing 6.3 The CKY Parsing Algorithm (Part 1) (731).mp4 |
9.4 MB |
| coursera-nlp/lectures/week3-02/Natural Language Processing 6.4 The CKY Parsing Algorithm (Part 2) (1322).mp4 |
16.6 MB |
| coursera-nlp/lectures/week3-02/Natural Language Processing 6.5 The CKY Parsing Algorithm (Part 3) (1007).mp4 |
12.4 MB |
| coursera-nlp/lectures/week4-01/Natural Language Processing 7.0 Weaknesses of PCFGs (1459).mp4 |
17.9 MB |
| coursera-nlp/lectures/week4-02/Natural Language Processing 8.0 Introduction (0017).mp4 |
330 KB |
| coursera-nlp/lectures/week4-02/Natural Language Processing 8.1 Lexicalization of a Treebank (1044).mp4 |
12.8 MB |
| coursera-nlp/lectures/week4-02/Natural Language Processing 8.2 Lexicalized PCFGs Basic Definitions (1240).mp4 |
16 MB |
| coursera-nlp/lectures/week4-02/Natural Language Processing 8.3 Parameter Estimation in Lexicalized PCFGs (Part 1) (528).mp4 |
6.5 MB |
| coursera-nlp/lectures/week4-02/Natural Language Processing 8.4 Parameter Estimation in Lexicalized PCFGs (Part 2) (908).mp4 |
11.1 MB |
| coursera-nlp/lectures/week4-02/Natural Language Processing 8.5 Evaluation of Lexicalized PCFGs (Part 1) (932).mp4 |
12.1 MB |
| coursera-nlp/lectures/week4-02/Natural Language Processing 8.6 Evaluation of Lexicalized PCFGs (Part 2) (1128).mp4 |
14.3 MB |
| coursera-nlp/lectures/week5-01/Natural Language Processing 9.0 Opening Comments (025).mp4 |
452 KB |
| coursera-nlp/lectures/week5-01/Natural Language Processing 9.1 introduction (203).mp4 |
2.4 MB |
| coursera-nlp/lectures/week5-01/Natural Language Processing 9.2 Challenges in MT (806).mp4 |
9.4 MB |
| coursera-nlp/lectures/week5-01/Natural Language Processing 9.3 Classical Approaches to MT (Part 1) (802).mp4 |
10 MB |
| coursera-nlp/lectures/week5-01/Natural Language Processing 9.4 Classical Approaches to MT (Part 2) (556).mp4 |
7.3 MB |
| coursera-nlp/lectures/week5-01/Natural Language Processing 9.5 Introduction to Statistical MT (1231).mp4 |
15.7 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.0 Introduction (324).mp4 |
4 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.1 IBM Model 1 (Part 1) (1306).mp4 |
16.1 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.2 IBM Model 1 (Part 2) (901).mp4 |
10.9 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.3 IBM Model 2 (1127).mp4 |
13.9 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.4 The EM Algorithm for IBM Model 2 (Part 1) (509).mp4 |
6.4 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.5 The EM Algorithm for IBM Model 2 (Part 2) (837).mp4 |
11.2 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.6 The EM Algorithm for IBM Model 2 (Part 3) (928).mp4 |
11.6 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.7 The EM Algorithm for IBM Model 2 (Part 4) (452).mp4 |
6.1 MB |
| coursera-nlp/lectures/week5-02/Natural Language Processing 10.8 Summary (148).mp4 |
2.3 MB |
| coursera-nlp/lectures/week6-01/Natural Language Processing 11.0 Introduction (041).mp4 |
742 KB |
| coursera-nlp/lectures/week6-01/Natural Language Processing 11.1 Learning Phrases from Alignments (Part 1) (918).mp4 |
11.5 MB |
| coursera-nlp/lectures/week6-01/Natural Language Processing 11.2 Learning Phrases from Alignments (Part 2) (701).mp4 |
8.5 MB |
| coursera-nlp/lectures/week6-01/Natural Language Processing 11.3 Learning Phrases from Alignments (Part 3) (847).mp4 |
11.1 MB |
| coursera-nlp/lectures/week6-01/Natural Language Processing 11.4 A Sketch of Phrase-based Translation (817).mp4 |
9.9 MB |
| coursera-nlp/lectures/week6-02/Natural Language Processing 12.0 Definition of the Decoding Problem (Part 1) (912).mp4 |
11.7 MB |
| coursera-nlp/lectures/week6-02/Natural Language Processing 12.1 Definition of the Decoding Problem (Part 2) (1300).mp4 |
15.9 MB |
| coursera-nlp/lectures/week6-02/Natural Language Processing 12.2 Definition of the Decoding Problem (Part 3) (1043).mp4 |
13.5 MB |
| coursera-nlp/lectures/week6-02/Natural Language Processing 12.3 The Decoding Algorithm (Part 1) (1439).mp4 |
18 MB |
| coursera-nlp/lectures/week6-02/Natural Language Processing 12.4 The Decoding Algorithm (Part 2) (623).mp4 |
7.6 MB |
| coursera-nlp/lectures/week6-02/Natural Language Processing 12.5 The Decoding Algorithm (Part 3) (1229).mp4 |
15.9 MB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.0 Introduction (047).mp4 |
850 KB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.1 Two Example Problems (1119).mp4 |
14.1 MB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.2 Features in Log-Linear Models (Part 1) (1356).mp4 |
17.1 MB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.3 Features in Log-Linear Models (Part 2) (1013).mp4 |
12.5 MB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.4 Definition of Log-linear Models (Part 1) (1150).mp4 |
14.6 MB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.5 Definition of Log-linear Models (Part 2) (345).mp4 |
4.5 MB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.6 Parameter Estimation in Log-linear Models (Part 1) (1244).mp4 |
15.5 MB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.7 Parameter Estimation in Log-linear Models (Part 2) (413).mp4 |
5.2 MB |
| coursera-nlp/lectures/week7-01/Natural Language Processing 13.8 SmoothingRegularization in Log-linear Models (1512).mp4 |
19.3 MB |
| coursera-nlp/lectures/week8-01/Natural Language Processing 14.0 Introduction (141).mp4 |
1.9 MB |
| coursera-nlp/lectures/week8-01/Natural Language Processing 14.1 Recap of the Tagging Problem (315).mp4 |
4.3 MB |
| coursera-nlp/lectures/week8-01/Natural Language Processing 14.2 Independence Assumptions in Log-linear Taggers (832).mp4 |
10.3 MB |
| coursera-nlp/lectures/week8-01/Natural Language Processing 14.3 Features in Log-Linear Taggers (1321).mp4 |
16.3 MB |
| coursera-nlp/lectures/week8-01/Natural Language Processing 14.4 Parameters in Log-linear Models (359).mp4 |
4.8 MB |
| coursera-nlp/lectures/week8-01/Natural Language Processing 14.5 The Viterbi Algorithm for Log-linear Taggers (937).mp4 |
11.4 MB |
| coursera-nlp/lectures/week8-01/Natural Language Processing 14.6 An Example Application (928).mp4 |
11.6 MB |
| coursera-nlp/lectures/week8-01/Natural Language Processing 14.7 Summary (245).mp4 |
3.3 MB |
| coursera-nlp/lectures/week8-02/Natural Language Processing 15.0 Introduction (047).mp4 |
874 KB |
| coursera-nlp/lectures/week8-02/Natural Language Processing 15.1 Conditional History-based Models (714).mp4 |
8.7 MB |
| coursera-nlp/lectures/week8-02/Natural Language Processing 15.2 Representing Trees as Decision Sequences (Part 1) (723).mp4 |
8.7 MB |
| coursera-nlp/lectures/week8-02/Natural Language Processing 15.3 Representing Trees as Decision Sequences (Part 2) (1020).mp4 |
12 MB |
| coursera-nlp/lectures/week8-02/Natural Language Processing 15.4 Features and Beam Search (1210).mp4 |
14.6 MB |
| coursera-nlp/lectures/week8-02/Natural Language Processing 15.5 Summary (112).mp4 |
1.4 MB |
| coursera-nlp/lectures/week9-01/Natural Language Processing 16.0 Introduction (036).mp4 |
671 KB |
| coursera-nlp/lectures/week9-01/Natural Language Processing 16.1 Word Cluster Representations (836).mp4 |
11.1 MB |
| coursera-nlp/lectures/week9-01/Natural Language Processing 16.2 The Brown Clustering Algorithm (Part 1) (1150).mp4 |
14.4 MB |
| coursera-nlp/lectures/week9-01/Natural Language Processing 16.3 The Brown Clustering Algorithm (Part 2) (830).mp4 |
10.6 MB |
| coursera-nlp/lectures/week9-01/Natural Language Processing 16.4 The Brown Clustering Algorithm (Part 3) (918).mp4 |
11.7 MB |
| coursera-nlp/lectures/week9-01/Natural Language Processing 16.5 Clusters in NE Recognition (Part 1) (1133).mp4 |
15.3 MB |
| coursera-nlp/lectures/week9-01/Natural Language Processing 16.6 Clusters in NE Recognition (Part 2) (728).mp4 |
8.9 MB |
| coursera-nlp/lectures/week9-02/Natural Language Processing 17.0 Introduction (030).mp4 |
575 KB |
| coursera-nlp/lectures/week9-02/Natural Language Processing 17.1 Recap of History-based Models (711).mp4 |
9 MB |
| coursera-nlp/lectures/week9-02/Natural Language Processing 17.2 Motivation for GLMs (634).mp4 |
7.9 MB |
| coursera-nlp/lectures/week9-02/Natural Language Processing 17.3 Three Components of GLMs (1439).mp4 |
17.3 MB |
| coursera-nlp/lectures/week9-02/Natural Language Processing 17.4 GLMs for Parse Reranking (1036).mp4 |
12.8 MB |
| coursera-nlp/lectures/week9-02/Natural Language Processing 17.5 Parameter Estimation with the Perceptron Algorithm (611).mp4 |
7.3 MB |
| coursera-nlp/lectures/week9-02/Natural Language Processing 17.6 Summary (301).mp4 |
3.8 MB |
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