Udemy Natural Language Processing With Transformers in Python 06 2021

1. 1. Introduction/1. Introduction.mp49.2MB
2. 1. Introduction/2. Course Overview.mp434.38MB
3. 1. Introduction/3. Environment Setup.mp437.25MB
4. 1. Introduction/4. CUDA Setup.mp423.73MB
5. 10. Metrics For Language/1. Q&A Performance With Exact Match (EM).mp418.17MB
6. 10. Metrics For Language/2. ROUGE in Python.mp421.66MB
7. 10. Metrics For Language/3. Applying ROUGE to Q&A.mp433.95MB
8. 10. Metrics For Language/4. Recall, Precision and F1.mp421.02MB
9. 10. Metrics For Language/5. Longest Common Subsequence (LCS).mp414.95MB
10. 10. Metrics For Language/6. Q&A Performance With ROUGE.mp418.75MB
11. 11. Reader-Retriever QA With Haystack/1. Intro to Retriever-Reader and Haystack.mp413.94MB
12. 11. Reader-Retriever QA With Haystack/10. FAISS in Haystack.mp468.09MB
13. 11. Reader-Retriever QA With Haystack/11. What is DPR.mp429.65MB
14. 11. Reader-Retriever QA With Haystack/12. The DPR Architecture.mp414.28MB
15. 11. Reader-Retriever QA With Haystack/13. Retriever-Reader Stack.mp475.25MB
16. 11. Reader-Retriever QA With Haystack/2. What is Elasticsearch.mp423.54MB
17. 11. Reader-Retriever QA With Haystack/3. Elasticsearch Setup (Windows).mp420.9MB
18. 11. Reader-Retriever QA With Haystack/4. Elasticsearch Setup (Linux).mp420.21MB
19. 11. Reader-Retriever QA With Haystack/5. Elasticsearch in Haystack.mp439.02MB
20. 11. Reader-Retriever QA With Haystack/6. Sparse Retrievers.mp420.37MB
21. 11. Reader-Retriever QA With Haystack/7. Cleaning the Index.mp426.45MB
22. 11. Reader-Retriever QA With Haystack/8. Implementing a BM25 Retriever.mp412.55MB
23. 11. Reader-Retriever QA With Haystack/9. What is FAISS.mp442.9MB
24. 12. [Project] Open-Domain QA/1. ODQA Stack Structure.mp46.23MB
25. 12. [Project] Open-Domain QA/2. Creating the Database.mp442.43MB
26. 12. [Project] Open-Domain QA/3. Building the Haystack Pipeline.mp455.8MB
27. 13. Similarity/1. Introduction to Similarity.mp428.25MB
28. 13. Similarity/2. Extracting The Last Hidden State Tensor.mp429.76MB
29. 13. Similarity/3. Sentence Vectors With Mean Pooling.mp432.09MB
30. 13. Similarity/4. Using Cosine Similarity.mp433.86MB
31. 13. Similarity/5. Similarity With Sentence-Transformers.mp423.02MB
32. 14. Fine-Tuning Transformer Models/1. Visual Guide to BERT Pretraining.mp428.6MB
33. 14. Fine-Tuning Transformer Models/10. Fine-tuning with NSP - Data Preparation.mp477.97MB
34. 14. Fine-Tuning Transformer Models/11. Fine-tuning with NSP - DataLoader.mp414.27MB
35. 14. Fine-Tuning Transformer Models/13. The Logic of MLM and NSP.mp426.25MB
36. 14. Fine-Tuning Transformer Models/14. Fine-tuning with MLM and NSP - Data Preparation.mp443.62MB
37. 14. Fine-Tuning Transformer Models/2. Introduction to BERT For Pretraining Code.mp429.26MB
38. 14. Fine-Tuning Transformer Models/3. BERT Pretraining - Masked-Language Modeling (MLM).mp446.71MB
39. 14. Fine-Tuning Transformer Models/4. BERT Pretraining - Next Sentence Prediction (NSP).mp442.08MB
40. 14. Fine-Tuning Transformer Models/5. The Logic of MLM.mp479.41MB
41. 14. Fine-Tuning Transformer Models/6. Fine-tuning with MLM - Data Preparation.mp476.72MB
42. 14. Fine-Tuning Transformer Models/7. Fine-tuning with MLM - Training.mp469.69MB
43. 14. Fine-Tuning Transformer Models/8. Fine-tuning with MLM - Training with Trainer.mp419.88MB
44. 14. Fine-Tuning Transformer Models/9. The Logic of NSP.mp420.88MB
45. 2. NLP and Transformers/1. The Three Eras of AI.mp422.2MB
46. 2. NLP and Transformers/10. Transformer Heads.mp439.82MB
47. 2. NLP and Transformers/2. Pros and Cons of Neural AI.mp432.79MB
48. 2. NLP and Transformers/3. Word Vectors.mp421.73MB
49. 2. NLP and Transformers/4. Recurrent Neural Networks.mp417.1MB
50. 2. NLP and Transformers/5. Long Short-Term Memory.mp46.34MB
51. 2. NLP and Transformers/6. Encoder-Decoder Attention.mp425.17MB
52. 2. NLP and Transformers/7. Self-Attention.mp420.8MB
53. 2. NLP and Transformers/8. Multi-head Attention.mp413.33MB
54. 2. NLP and Transformers/9. Positional Encoding.mp455.53MB
55. 3. Preprocessing for NLP/1. Stopwords.mp423.06MB
56. 3. Preprocessing for NLP/2. Tokens Introduction.mp424.04MB
57. 3. Preprocessing for NLP/3. Model-Specific Special Tokens.mp418.89MB
58. 3. Preprocessing for NLP/4. Stemming.mp417.24MB
59. 3. Preprocessing for NLP/5. Lemmatization.mp410.58MB
60. 3. Preprocessing for NLP/6. Unicode Normalization - Canonical and Compatibility Equivalence.mp416.97MB
61. 3. Preprocessing for NLP/7. Unicode Normalization - Composition and Decomposition.mp420.25MB
62. 3. Preprocessing for NLP/8. Unicode Normalization - NFD and NFC.mp420.02MB
63. 3. Preprocessing for NLP/9. Unicode Normalization - NFKD and NFKC.mp430.42MB
64. 4. Attention/1. Attention Introduction.mp415.79MB
65. 4. Attention/2. Alignment With Dot-Product.mp449.12MB
66. 4. Attention/3. Dot-Product Attention.mp428.99MB
67. 4. Attention/4. Self Attention.mp428.4MB
68. 4. Attention/5. Bidirectional Attention.mp410.78MB
69. 4. Attention/6. Multi-head and Scaled Dot-Product Attention.mp433.83MB
70. 5. Language Classification/1. Introduction to Sentiment Analysis.mp437.53MB
71. 5. Language Classification/2. Prebuilt Flair Models.mp430.71MB
72. 5. Language Classification/3. Introduction to Sentiment Models With Transformers.mp426.92MB
73. 5. Language Classification/4. Tokenization And Special Tokens For BERT.mp455.43MB
74. 5. Language Classification/5. Making Predictions.mp425.97MB
75. 6. [Project] Sentiment Model With TensorFlow and Transformers/1. Project Overview.mp412.51MB
76. 6. [Project] Sentiment Model With TensorFlow and Transformers/2. Getting the Data (Kaggle API).mp435.02MB
77. 6. [Project] Sentiment Model With TensorFlow and Transformers/3. Preprocessing.mp462.49MB
78. 6. [Project] Sentiment Model With TensorFlow and Transformers/4. Building a Dataset.mp422.57MB
79. 6. [Project] Sentiment Model With TensorFlow and Transformers/5. Dataset Shuffle, Batch, Split, and Save.mp430.17MB
80. 6. [Project] Sentiment Model With TensorFlow and Transformers/6. Build and Save.mp477.01MB
81. 6. [Project] Sentiment Model With TensorFlow and Transformers/7. Loading and Prediction.mp456.77MB
82. 7. Long Text Classification With BERT/1. Classification of Long Text Using Windows.mp4116.14MB
83. 7. Long Text Classification With BERT/2. Window Method in PyTorch.mp484.94MB
84. 8. Named Entity Recognition (NER)/1. Introduction to spaCy.mp451.64MB
85. 8. Named Entity Recognition (NER)/10. NER With roBERTa.mp459.01MB
86. 8. Named Entity Recognition (NER)/2. Extracting Entities.mp433.53MB
87. 8. Named Entity Recognition (NER)/4. Authenticating With The Reddit API.mp435.63MB
88. 8. Named Entity Recognition (NER)/5. Pulling Data With The Reddit API.mp488.96MB
89. 8. Named Entity Recognition (NER)/6. Extracting ORGs From Reddit Data.mp428.11MB
90. 8. Named Entity Recognition (NER)/7. Getting Entity Frequency.mp418.39MB
91. 8. Named Entity Recognition (NER)/8. Entity Blacklist.mp420.15MB
92. 8. Named Entity Recognition (NER)/9. NER With Sentiment.mp499.88MB
93. 9. Question and Answering/1. Open Domain and Reading Comprehension.mp416.07MB
94. 9. Question and Answering/2. Retrievers, Readers, and Generators.mp428.68MB
95. 9. Question and Answering/3. Intro to SQuAD 2.0.mp425.39MB
96. 9. Question and Answering/4. Processing SQuAD Training Data.mp438.42MB
97. 9. Question and Answering/5. (Optional) Processing SQuAD Training Data with Match-Case.mp430.1MB
98. 9. Question and Answering/7. Our First Q&A Model.mp445.71MB