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[Udemy] Natural Language Processing With Transformers in Python (06.2021)
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2022-8-16 22:44
2024-12-23 07:24
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3.29 GB
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相关链接
Udemy
Natural
Language
Processing
With
Transformers
in
Python
06
2021
文件列表
1. Introduction/1. Introduction.mp4
9.2MB
1. Introduction/2. Course Overview.mp4
34.38MB
1. Introduction/3. Environment Setup.mp4
37.25MB
1. Introduction/4. CUDA Setup.mp4
23.73MB
10. Metrics For Language/1. Q&A Performance With Exact Match (EM).mp4
18.17MB
10. Metrics For Language/2. ROUGE in Python.mp4
21.66MB
10. Metrics For Language/3. Applying ROUGE to Q&A.mp4
33.95MB
10. Metrics For Language/4. Recall, Precision and F1.mp4
21.02MB
10. Metrics For Language/5. Longest Common Subsequence (LCS).mp4
14.95MB
10. Metrics For Language/6. Q&A Performance With ROUGE.mp4
18.75MB
11. Reader-Retriever QA With Haystack/1. Intro to Retriever-Reader and Haystack.mp4
13.94MB
11. Reader-Retriever QA With Haystack/10. FAISS in Haystack.mp4
68.09MB
11. Reader-Retriever QA With Haystack/11. What is DPR.mp4
29.65MB
11. Reader-Retriever QA With Haystack/12. The DPR Architecture.mp4
14.28MB
11. Reader-Retriever QA With Haystack/13. Retriever-Reader Stack.mp4
75.25MB
11. Reader-Retriever QA With Haystack/2. What is Elasticsearch.mp4
23.54MB
11. Reader-Retriever QA With Haystack/3. Elasticsearch Setup (Windows).mp4
20.9MB
11. Reader-Retriever QA With Haystack/4. Elasticsearch Setup (Linux).mp4
20.21MB
11. Reader-Retriever QA With Haystack/5. Elasticsearch in Haystack.mp4
39.02MB
11. Reader-Retriever QA With Haystack/6. Sparse Retrievers.mp4
20.37MB
11. Reader-Retriever QA With Haystack/7. Cleaning the Index.mp4
26.45MB
11. Reader-Retriever QA With Haystack/8. Implementing a BM25 Retriever.mp4
12.55MB
11. Reader-Retriever QA With Haystack/9. What is FAISS.mp4
42.9MB
12. [Project] Open-Domain QA/1. ODQA Stack Structure.mp4
6.23MB
12. [Project] Open-Domain QA/2. Creating the Database.mp4
42.43MB
12. [Project] Open-Domain QA/3. Building the Haystack Pipeline.mp4
55.8MB
13. Similarity/1. Introduction to Similarity.mp4
28.25MB
13. Similarity/2. Extracting The Last Hidden State Tensor.mp4
29.76MB
13. Similarity/3. Sentence Vectors With Mean Pooling.mp4
32.09MB
13. Similarity/4. Using Cosine Similarity.mp4
33.86MB
13. Similarity/5. Similarity With Sentence-Transformers.mp4
23.02MB
14. Fine-Tuning Transformer Models/1. Visual Guide to BERT Pretraining.mp4
28.6MB
14. Fine-Tuning Transformer Models/10. Fine-tuning with NSP - Data Preparation.mp4
77.97MB
14. Fine-Tuning Transformer Models/11. Fine-tuning with NSP - DataLoader.mp4
14.27MB
14. Fine-Tuning Transformer Models/13. The Logic of MLM and NSP.mp4
26.25MB
14. Fine-Tuning Transformer Models/14. Fine-tuning with MLM and NSP - Data Preparation.mp4
43.62MB
14. Fine-Tuning Transformer Models/2. Introduction to BERT For Pretraining Code.mp4
29.26MB
14. Fine-Tuning Transformer Models/3. BERT Pretraining - Masked-Language Modeling (MLM).mp4
46.71MB
14. Fine-Tuning Transformer Models/4. BERT Pretraining - Next Sentence Prediction (NSP).mp4
42.08MB
14. Fine-Tuning Transformer Models/5. The Logic of MLM.mp4
79.41MB
14. Fine-Tuning Transformer Models/6. Fine-tuning with MLM - Data Preparation.mp4
76.72MB
14. Fine-Tuning Transformer Models/7. Fine-tuning with MLM - Training.mp4
69.69MB
14. Fine-Tuning Transformer Models/8. Fine-tuning with MLM - Training with Trainer.mp4
19.88MB
14. Fine-Tuning Transformer Models/9. The Logic of NSP.mp4
20.88MB
2. NLP and Transformers/1. The Three Eras of AI.mp4
22.2MB
2. NLP and Transformers/10. Transformer Heads.mp4
39.82MB
2. NLP and Transformers/2. Pros and Cons of Neural AI.mp4
32.79MB
2. NLP and Transformers/3. Word Vectors.mp4
21.73MB
2. NLP and Transformers/4. Recurrent Neural Networks.mp4
17.1MB
2. NLP and Transformers/5. Long Short-Term Memory.mp4
6.34MB
2. NLP and Transformers/6. Encoder-Decoder Attention.mp4
25.17MB
2. NLP and Transformers/7. Self-Attention.mp4
20.8MB
2. NLP and Transformers/8. Multi-head Attention.mp4
13.33MB
2. NLP and Transformers/9. Positional Encoding.mp4
55.53MB
3. Preprocessing for NLP/1. Stopwords.mp4
23.06MB
3. Preprocessing for NLP/2. Tokens Introduction.mp4
24.04MB
3. Preprocessing for NLP/3. Model-Specific Special Tokens.mp4
18.89MB
3. Preprocessing for NLP/4. Stemming.mp4
17.24MB
3. Preprocessing for NLP/5. Lemmatization.mp4
10.58MB
3. Preprocessing for NLP/6. Unicode Normalization - Canonical and Compatibility Equivalence.mp4
16.97MB
3. Preprocessing for NLP/7. Unicode Normalization - Composition and Decomposition.mp4
20.25MB
3. Preprocessing for NLP/8. Unicode Normalization - NFD and NFC.mp4
20.02MB
3. Preprocessing for NLP/9. Unicode Normalization - NFKD and NFKC.mp4
30.42MB
4. Attention/1. Attention Introduction.mp4
15.79MB
4. Attention/2. Alignment With Dot-Product.mp4
49.12MB
4. Attention/3. Dot-Product Attention.mp4
28.99MB
4. Attention/4. Self Attention.mp4
28.4MB
4. Attention/5. Bidirectional Attention.mp4
10.78MB
4. Attention/6. Multi-head and Scaled Dot-Product Attention.mp4
33.83MB
5. Language Classification/1. Introduction to Sentiment Analysis.mp4
37.53MB
5. Language Classification/2. Prebuilt Flair Models.mp4
30.71MB
5. Language Classification/3. Introduction to Sentiment Models With Transformers.mp4
26.92MB
5. Language Classification/4. Tokenization And Special Tokens For BERT.mp4
55.43MB
5. Language Classification/5. Making Predictions.mp4
25.97MB
6. [Project] Sentiment Model With TensorFlow and Transformers/1. Project Overview.mp4
12.51MB
6. [Project] Sentiment Model With TensorFlow and Transformers/2. Getting the Data (Kaggle API).mp4
35.02MB
6. [Project] Sentiment Model With TensorFlow and Transformers/3. Preprocessing.mp4
62.49MB
6. [Project] Sentiment Model With TensorFlow and Transformers/4. Building a Dataset.mp4
22.57MB
6. [Project] Sentiment Model With TensorFlow and Transformers/5. Dataset Shuffle, Batch, Split, and Save.mp4
30.17MB
6. [Project] Sentiment Model With TensorFlow and Transformers/6. Build and Save.mp4
77.01MB
6. [Project] Sentiment Model With TensorFlow and Transformers/7. Loading and Prediction.mp4
56.77MB
7. Long Text Classification With BERT/1. Classification of Long Text Using Windows.mp4
116.14MB
7. Long Text Classification With BERT/2. Window Method in PyTorch.mp4
84.94MB
8. Named Entity Recognition (NER)/1. Introduction to spaCy.mp4
51.64MB
8. Named Entity Recognition (NER)/10. NER With roBERTa.mp4
59.01MB
8. Named Entity Recognition (NER)/2. Extracting Entities.mp4
33.53MB
8. Named Entity Recognition (NER)/4. Authenticating With The Reddit API.mp4
35.63MB
8. Named Entity Recognition (NER)/5. Pulling Data With The Reddit API.mp4
88.96MB
8. Named Entity Recognition (NER)/6. Extracting ORGs From Reddit Data.mp4
28.11MB
8. Named Entity Recognition (NER)/7. Getting Entity Frequency.mp4
18.39MB
8. Named Entity Recognition (NER)/8. Entity Blacklist.mp4
20.15MB
8. Named Entity Recognition (NER)/9. NER With Sentiment.mp4
99.88MB
9. Question and Answering/1. Open Domain and Reading Comprehension.mp4
16.07MB
9. Question and Answering/2. Retrievers, Readers, and Generators.mp4
28.68MB
9. Question and Answering/3. Intro to SQuAD 2.0.mp4
25.39MB
9. Question and Answering/4. Processing SQuAD Training Data.mp4
38.42MB
9. Question and Answering/5. (Optional) Processing SQuAD Training Data with Match-Case.mp4
30.1MB
9. Question and Answering/7. Our First Q&A Model.mp4
45.71MB
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