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[Udemy] Natural Language Processing With Transformers in Python (06.2021)
magnet:?xt=urn:btih:15a0a25219359a8870cb3f3844c0e975a3826772&dn=[Udemy] Natural Language Processing With Transformers in Python (06.2021)
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文件列表详情
15a0a25219359a8870cb3f3844c0e975a3826772
infohash:
98
文件数量
3.29 GB
文件大小
2022-8-16 22:44
创建日期
2024-6-30 01:29
最后访问
相关分词
Udemy
Natural
Language
Processing
With
Transformers
in
Python
06
2021
1. Introduction/1. Introduction.mp4 9.2 MB
1. Introduction/2. Course Overview.mp4 34.38 MB
1. Introduction/3. Environment Setup.mp4 37.25 MB
1. Introduction/4. CUDA Setup.mp4 23.73 MB
10. Metrics For Language/1. Q&A Performance With Exact Match (EM).mp4 18.17 MB
10. Metrics For Language/2. ROUGE in Python.mp4 21.66 MB
10. Metrics For Language/3. Applying ROUGE to Q&A.mp4 33.95 MB
10. Metrics For Language/4. Recall, Precision and F1.mp4 21.02 MB
10. Metrics For Language/5. Longest Common Subsequence (LCS).mp4 14.95 MB
10. Metrics For Language/6. Q&A Performance With ROUGE.mp4 18.75 MB
11. Reader-Retriever QA With Haystack/1. Intro to Retriever-Reader and Haystack.mp4 13.94 MB
11. Reader-Retriever QA With Haystack/10. FAISS in Haystack.mp4 68.09 MB
11. Reader-Retriever QA With Haystack/11. What is DPR.mp4 29.65 MB
11. Reader-Retriever QA With Haystack/12. The DPR Architecture.mp4 14.28 MB
11. Reader-Retriever QA With Haystack/13. Retriever-Reader Stack.mp4 75.25 MB
11. Reader-Retriever QA With Haystack/2. What is Elasticsearch.mp4 23.54 MB
11. Reader-Retriever QA With Haystack/3. Elasticsearch Setup (Windows).mp4 20.9 MB
11. Reader-Retriever QA With Haystack/4. Elasticsearch Setup (Linux).mp4 20.21 MB
11. Reader-Retriever QA With Haystack/5. Elasticsearch in Haystack.mp4 39.02 MB
11. Reader-Retriever QA With Haystack/6. Sparse Retrievers.mp4 20.37 MB
11. Reader-Retriever QA With Haystack/7. Cleaning the Index.mp4 26.45 MB
11. Reader-Retriever QA With Haystack/8. Implementing a BM25 Retriever.mp4 12.55 MB
11. Reader-Retriever QA With Haystack/9. What is FAISS.mp4 42.9 MB
12. [Project] Open-Domain QA/1. ODQA Stack Structure.mp4 6.23 MB
12. [Project] Open-Domain QA/2. Creating the Database.mp4 42.43 MB
12. [Project] Open-Domain QA/3. Building the Haystack Pipeline.mp4 55.8 MB
13. Similarity/1. Introduction to Similarity.mp4 28.25 MB
13. Similarity/2. Extracting The Last Hidden State Tensor.mp4 29.76 MB
13. Similarity/3. Sentence Vectors With Mean Pooling.mp4 32.09 MB
13. Similarity/4. Using Cosine Similarity.mp4 33.86 MB
13. Similarity/5. Similarity With Sentence-Transformers.mp4 23.02 MB
14. Fine-Tuning Transformer Models/1. Visual Guide to BERT Pretraining.mp4 28.6 MB
14. Fine-Tuning Transformer Models/10. Fine-tuning with NSP - Data Preparation.mp4 77.97 MB
14. Fine-Tuning Transformer Models/11. Fine-tuning with NSP - DataLoader.mp4 14.27 MB
14. Fine-Tuning Transformer Models/13. The Logic of MLM and NSP.mp4 26.25 MB
14. Fine-Tuning Transformer Models/14. Fine-tuning with MLM and NSP - Data Preparation.mp4 43.62 MB
14. Fine-Tuning Transformer Models/2. Introduction to BERT For Pretraining Code.mp4 29.26 MB
14. Fine-Tuning Transformer Models/3. BERT Pretraining - Masked-Language Modeling (MLM).mp4 46.71 MB
14. Fine-Tuning Transformer Models/4. BERT Pretraining - Next Sentence Prediction (NSP).mp4 42.08 MB
14. Fine-Tuning Transformer Models/5. The Logic of MLM.mp4 79.41 MB
14. Fine-Tuning Transformer Models/6. Fine-tuning with MLM - Data Preparation.mp4 76.72 MB
14. Fine-Tuning Transformer Models/7. Fine-tuning with MLM - Training.mp4 69.69 MB
14. Fine-Tuning Transformer Models/8. Fine-tuning with MLM - Training with Trainer.mp4 19.88 MB
14. Fine-Tuning Transformer Models/9. The Logic of NSP.mp4 20.88 MB
2. NLP and Transformers/1. The Three Eras of AI.mp4 22.2 MB
2. NLP and Transformers/10. Transformer Heads.mp4 39.82 MB
2. NLP and Transformers/2. Pros and Cons of Neural AI.mp4 32.79 MB
2. NLP and Transformers/3. Word Vectors.mp4 21.73 MB
2. NLP and Transformers/4. Recurrent Neural Networks.mp4 17.1 MB
2. NLP and Transformers/5. Long Short-Term Memory.mp4 6.34 MB
2. NLP and Transformers/6. Encoder-Decoder Attention.mp4 25.17 MB
2. NLP and Transformers/7. Self-Attention.mp4 20.8 MB
2. NLP and Transformers/8. Multi-head Attention.mp4 13.33 MB
2. NLP and Transformers/9. Positional Encoding.mp4 55.53 MB
3. Preprocessing for NLP/1. Stopwords.mp4 23.06 MB
3. Preprocessing for NLP/2. Tokens Introduction.mp4 24.04 MB
3. Preprocessing for NLP/3. Model-Specific Special Tokens.mp4 18.89 MB
3. Preprocessing for NLP/4. Stemming.mp4 17.24 MB
3. Preprocessing for NLP/5. Lemmatization.mp4 10.58 MB
3. Preprocessing for NLP/6. Unicode Normalization - Canonical and Compatibility Equivalence.mp4 16.97 MB
3. Preprocessing for NLP/7. Unicode Normalization - Composition and Decomposition.mp4 20.25 MB
3. Preprocessing for NLP/8. Unicode Normalization - NFD and NFC.mp4 20.02 MB
3. Preprocessing for NLP/9. Unicode Normalization - NFKD and NFKC.mp4 30.42 MB
4. Attention/1. Attention Introduction.mp4 15.79 MB
4. Attention/2. Alignment With Dot-Product.mp4 49.12 MB
4. Attention/3. Dot-Product Attention.mp4 28.99 MB
4. Attention/4. Self Attention.mp4 28.4 MB
4. Attention/5. Bidirectional Attention.mp4 10.78 MB
4. Attention/6. Multi-head and Scaled Dot-Product Attention.mp4 33.83 MB
5. Language Classification/1. Introduction to Sentiment Analysis.mp4 37.53 MB
5. Language Classification/2. Prebuilt Flair Models.mp4 30.71 MB
5. Language Classification/3. Introduction to Sentiment Models With Transformers.mp4 26.92 MB
5. Language Classification/4. Tokenization And Special Tokens For BERT.mp4 55.43 MB
5. Language Classification/5. Making Predictions.mp4 25.97 MB
6. [Project] Sentiment Model With TensorFlow and Transformers/1. Project Overview.mp4 12.51 MB
6. [Project] Sentiment Model With TensorFlow and Transformers/2. Getting the Data (Kaggle API).mp4 35.02 MB
6. [Project] Sentiment Model With TensorFlow and Transformers/3. Preprocessing.mp4 62.49 MB
6. [Project] Sentiment Model With TensorFlow and Transformers/4. Building a Dataset.mp4 22.57 MB
6. [Project] Sentiment Model With TensorFlow and Transformers/5. Dataset Shuffle, Batch, Split, and Save.mp4 30.17 MB
6. [Project] Sentiment Model With TensorFlow and Transformers/6. Build and Save.mp4 77.01 MB
6. [Project] Sentiment Model With TensorFlow and Transformers/7. Loading and Prediction.mp4 56.77 MB
7. Long Text Classification With BERT/1. Classification of Long Text Using Windows.mp4 116.14 MB
7. Long Text Classification With BERT/2. Window Method in PyTorch.mp4 84.94 MB
8. Named Entity Recognition (NER)/1. Introduction to spaCy.mp4 51.64 MB
8. Named Entity Recognition (NER)/10. NER With roBERTa.mp4 59.01 MB
8. Named Entity Recognition (NER)/2. Extracting Entities.mp4 33.53 MB
8. Named Entity Recognition (NER)/4. Authenticating With The Reddit API.mp4 35.63 MB
8. Named Entity Recognition (NER)/5. Pulling Data With The Reddit API.mp4 88.96 MB
8. Named Entity Recognition (NER)/6. Extracting ORGs From Reddit Data.mp4 28.11 MB
8. Named Entity Recognition (NER)/7. Getting Entity Frequency.mp4 18.39 MB
8. Named Entity Recognition (NER)/8. Entity Blacklist.mp4 20.15 MB
8. Named Entity Recognition (NER)/9. NER With Sentiment.mp4 99.88 MB
9. Question and Answering/1. Open Domain and Reading Comprehension.mp4 16.07 MB
9. Question and Answering/2. Retrievers, Readers, and Generators.mp4 28.68 MB
9. Question and Answering/3. Intro to SQuAD 2.0.mp4 25.39 MB
9. Question and Answering/4. Processing SQuAD Training Data.mp4 38.42 MB
9. Question and Answering/5. (Optional) Processing SQuAD Training Data with Match-Case.mp4 30.1 MB
9. Question and Answering/7. Our First Q&A Model.mp4 45.71 MB
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