Udemy Natural Language Processing With Transformers in Python 06 2021

Udemy Natural Language Processing With Transformers in Python 06 2021

Name	[Udemy] Natural Language Processing With Transformers in Python (06.2021)	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	3.5 GB
Total Files	288
Hash	15A0A25219359A8870CB3F3844C0E975A3826772

/1. Introduction/
1. Introduction.mp4	9.6 MB
1. Introduction.srt	3.2 KB
2. Course Overview.mp4	36.0 MB
2. Course Overview.srt	8.3 KB
2.1 GitHub Repo.html	0.1 KB
3. Environment Setup.mp4	39.1 MB
3. Environment Setup.srt	7.5 KB
3.1 Installation Instructions.html	0.1 KB
4. CUDA Setup.mp4	24.9 MB
4. CUDA Setup.srt	3.6 KB
4.1 Installation Instructions.html	0.1 KB
/10. Metrics For Language/
1. Q&A Performance With Exact Match (EM).mp4	19.0 MB
1. Q&A Performance With Exact Match (EM).srt	5.7 KB
1.1 Notebook.html	0.2 KB
2. ROUGE in Python.mp4	22.7 MB
2. ROUGE in Python.srt	4.6 KB
2.1 Notebook.html	0.2 KB
3. Applying ROUGE to Q&A.mp4	35.6 MB
3. Applying ROUGE to Q&A.srt	8.8 KB
3.1 Notebook.html	0.2 KB
4. Recall, Precision and F1.mp4	22.0 MB
4. Recall, Precision and F1.srt	5.6 KB
4.1 Notebook.html	0.2 KB
5. Longest Common Subsequence (LCS).mp4	15.7 MB
5. Longest Common Subsequence (LCS).srt	3.1 KB
5.1 Notebook.html	0.2 KB
6. Q&A Performance With ROUGE.mp4	19.7 MB
6. Q&A Performance With ROUGE.srt	4.2 KB
6.1 Notebook.html	0.2 KB
/11. Reader-Retriever QA With Haystack/
1. Intro to Retriever-Reader and Haystack.mp4	14.6 MB
1. Intro to Retriever-Reader and Haystack.srt	3.9 KB
1.1 Notebook.html	0.2 KB
10. FAISS in Haystack.mp4	71.4 MB
10. FAISS in Haystack.srt	13.7 KB
10.1 Notebook.html	0.2 KB
11. What is DPR.mp4	31.1 MB
11. What is DPR.srt	8.7 KB
11.1 Article.html	0.2 KB
11.2 Notebook.html	0.2 KB
12. The DPR Architecture.mp4	15.0 MB
12. The DPR Architecture.srt	2.3 KB
12.1 Article.html	0.2 KB
12.2 Notebook.html	0.2 KB
13. Retriever-Reader Stack.mp4	78.9 MB
13. Retriever-Reader Stack.srt	11.4 KB
13.1 Notebook.html	0.2 KB
2. What is Elasticsearch.mp4	24.7 MB
2. What is Elasticsearch.srt	7.4 KB
2.1 Elasticsearch (Cloud) Introduction Article.html	0.2 KB
3. Elasticsearch Setup (Windows).mp4	21.9 MB
3. Elasticsearch Setup (Windows).srt	2.1 KB
4. Elasticsearch Setup (Linux).mp4	21.2 MB
4. Elasticsearch Setup (Linux).srt	2.1 KB
5. Elasticsearch in Haystack.mp4	40.9 MB
5. Elasticsearch in Haystack.srt	8.9 KB
5.1 Notebook.html	0.2 KB
6. Sparse Retrievers.mp4	21.4 MB
6. Sparse Retrievers.srt	4.3 KB
6.1 Notebook.html	0.2 KB
7. Cleaning the Index.mp4	27.7 MB
7. Cleaning the Index.srt	5.4 KB
7.1 Notebook.html	0.2 KB
8. Implementing a BM25 Retriever.mp4	13.2 MB
8. Implementing a BM25 Retriever.srt	2.6 KB
8.1 Notebook.html	0.2 KB
9. What is FAISS.mp4	45.0 MB
9. What is FAISS.srt	10.1 KB
9.1 Article.html	0.2 KB
9.2 Notebook.html	0.2 KB
/12. [Project] Open-Domain QA/
1. ODQA Stack Structure.mp4	6.5 MB
1. ODQA Stack Structure.srt	2.0 KB
2. Creating the Database.mp4	44.5 MB
2. Creating the Database.srt	7.9 KB
2.1 Data.html	0.1 KB
2.2 Notebook.html	0.2 KB
3. Building the Haystack Pipeline.mp4	58.5 MB
3. Building the Haystack Pipeline.srt	9.2 KB
3.1 Notebook.html	0.2 KB
/13. Similarity/
1. Introduction to Similarity.mp4	29.6 MB
1. Introduction to Similarity.srt	8.0 KB
2. Extracting The Last Hidden State Tensor.mp4	31.2 MB
2. Extracting The Last Hidden State Tensor.srt	5.8 KB
3. Sentence Vectors With Mean Pooling.mp4	33.6 MB
3. Sentence Vectors With Mean Pooling.srt	8.2 KB
4. Using Cosine Similarity.mp4	35.5 MB
4. Using Cosine Similarity.srt	6.0 KB
5. Similarity With Sentence-Transformers.mp4	24.1 MB
5. Similarity With Sentence-Transformers.srt	4.2 KB
/14. Fine-Tuning Transformer Models/
1. Visual Guide to BERT Pretraining.mp4	30.0 MB
1. Visual Guide to BERT Pretraining.srt	9.9 KB
10. Fine-tuning with NSP - Data Preparation.mp4	81.8 MB
10. Fine-tuning with NSP - Data Preparation.srt	15.0 KB
10.1 Notebook.html	0.2 KB
11. Fine-tuning with NSP - DataLoader.mp4	15.0 MB
11. Fine-tuning with NSP - DataLoader.srt	3.4 KB
11.1 Notebook.html	0.2 KB
12. Setup the NSP Fine-tuning Training Loop.html	0.1 KB
13. The Logic of MLM and NSP.mp4	27.5 MB
13. The Logic of MLM and NSP.srt	5.6 KB
13.1 Notebook.html	0.2 KB
14. Fine-tuning with MLM and NSP - Data Preparation.mp4	45.7 MB
14. Fine-tuning with MLM and NSP - Data Preparation.srt	9.2 KB
14.1 Notebook.html	0.2 KB
15. Setup DataLoader and Model Fine-tuning For MLM and NSP.html	0.1 KB
2. Introduction to BERT For Pretraining Code.mp4	30.7 MB
2. Introduction to BERT For Pretraining Code.srt	5.3 KB
2.1 Notebook.html	0.1 KB
3. BERT Pretraining - Masked-Language Modeling (MLM).mp4	49.0 MB
3. BERT Pretraining - Masked-Language Modeling (MLM).srt	9.6 KB
3.1 Notebook.html	0.1 KB
4. BERT Pretraining - Next Sentence Prediction (NSP).mp4	44.1 MB
4. BERT Pretraining - Next Sentence Prediction (NSP).srt	7.1 KB
4.1 Notebook.html	0.1 KB
5. The Logic of MLM.mp4	83.3 MB
5. The Logic of MLM.srt	13.6 KB
5.1 Notebook.html	0.2 KB
6. Fine-tuning with MLM - Data Preparation.mp4	80.4 MB
6. Fine-tuning with MLM - Data Preparation.srt	13.8 KB
6.1 Notebook.html	0.2 KB
7. Fine-tuning with MLM - Training.mp4	73.1 MB
7. Fine-tuning with MLM - Training.srt	14.0 KB
7.1 Notebook.html	0.2 KB
8. Fine-tuning with MLM - Training with Trainer.mp4	20.8 MB
8. Fine-tuning with MLM - Training with Trainer.srt	3.4 KB
8.1 Notebook.html	0.2 KB
9. The Logic of NSP.mp4	21.9 MB
9. The Logic of NSP.srt	4.7 KB
9.1 Notebook.html	0.2 KB
/2. NLP and Transformers/
1. The Three Eras of AI.mp4	23.3 MB
1. The Three Eras of AI.srt	7.9 KB
10. Transformer Heads.mp4	41.8 MB
10. Transformer Heads.srt	10.9 KB
2. Pros and Cons of Neural AI.mp4	34.4 MB
2. Pros and Cons of Neural AI.srt	5.6 KB
2.1 2010 Flash Crash.html	0.2 KB
2.2 Amazon AI Recruitment Bias.html	0.1 KB
2.3 Self-Driving Limitations.html	0.2 KB
3. Word Vectors.mp4	22.8 MB
3. Word Vectors.srt	5.2 KB
4. Recurrent Neural Networks.mp4	17.9 MB
4. Recurrent Neural Networks.srt	4.6 KB
5. Long Short-Term Memory.mp4	6.7 MB
5. Long Short-Term Memory.srt	2.2 KB
6. Encoder-Decoder Attention.mp4	26.4 MB
6. Encoder-Decoder Attention.srt	6.2 KB
7. Self-Attention.mp4	21.8 MB
7. Self-Attention.srt	4.7 KB
8. Multi-head Attention.mp4	14.0 MB
8. Multi-head Attention.srt	3.3 KB
9. Positional Encoding.mp4	58.2 MB
9. Positional Encoding.srt	9.9 KB
/3. Preprocessing for NLP/
1. Stopwords.mp4	24.2 MB
1. Stopwords.srt	6.4 KB
1.1 Notebook.html	0.2 KB
2. Tokens Introduction.mp4	25.2 MB
2. Tokens Introduction.srt	8.6 KB
2.1 Notebook.html	0.2 KB
3. Model-Specific Special Tokens.mp4	19.8 MB
3. Model-Specific Special Tokens.srt	7.3 KB
3.1 Notebook.html	0.2 KB
4. Stemming.mp4	18.1 MB
4. Stemming.srt	6.6 KB
4.1 Notebook.html	0.2 KB
5. Lemmatization.mp4	11.1 MB
5. Lemmatization.srt	4.3 KB
5.1 Notebook.html	0.2 KB
6. Unicode Normalization - Canonical and Compatibility Equivalence.mp4	17.8 MB
6. Unicode Normalization - Canonical and Compatibility Equivalence.srt	6.7 KB
6.1 Notebook.html	0.2 KB
7. Unicode Normalization - Composition and Decomposition.mp4	21.2 MB
7. Unicode Normalization - Composition and Decomposition.srt	5.8 KB
7.1 Notebook.html	0.2 KB
8. Unicode Normalization - NFD and NFC.mp4	21.0 MB
8. Unicode Normalization - NFD and NFC.srt	6.4 KB
8.1 Notebook.html	0.2 KB
9. Unicode Normalization - NFKD and NFKC.mp4	31.9 MB
9. Unicode Normalization - NFKD and NFKC.srt	8.9 KB
9.1 Notebook.html	0.2 KB
/4. Attention/
1. Attention Introduction.mp4	16.6 MB
1. Attention Introduction.srt	2.8 KB
1.1 Notebook.html	0.1 KB
2. Alignment With Dot-Product.mp4	51.5 MB
2. Alignment With Dot-Product.srt	14.1 KB
2.1 Notebook.html	0.2 KB
3. Dot-Product Attention.mp4	30.4 MB
3. Dot-Product Attention.srt	5.6 KB
3.1 Notebook.html	0.2 KB
4. Self Attention.mp4	29.8 MB
4. Self Attention.srt	6.4 KB
4.1 Notebook.html	0.2 KB
5. Bidirectional Attention.mp4	11.3 MB
5. Bidirectional Attention.srt	3.0 KB
5.1 Notebook.html	0.2 KB
6. Multi-head and Scaled Dot-Product Attention.mp4	35.5 MB
6. Multi-head and Scaled Dot-Product Attention.srt	7.3 KB
6.1 Notebook.html	0.2 KB
/5. Language Classification/
1. Introduction to Sentiment Analysis.mp4	39.3 MB
1. Introduction to Sentiment Analysis.srt	10.3 KB
1.1 Notebook.html	0.2 KB
2. Prebuilt Flair Models.mp4	32.2 MB
2. Prebuilt Flair Models.srt	9.6 KB
2.1 Notebook.html	0.2 KB
3. Introduction to Sentiment Models With Transformers.mp4	28.2 MB
3. Introduction to Sentiment Models With Transformers.srt	7.3 KB
3.1 Notebook.html	0.2 KB
4. Tokenization And Special Tokens For BERT.mp4	58.1 MB
4. Tokenization And Special Tokens For BERT.srt	8.6 KB
4.1 Notebook.html	0.2 KB
5. Making Predictions.mp4	27.2 MB
5. Making Predictions.srt	7.0 KB
5.1 Notebook.html	0.2 KB
/6. [Project] Sentiment Model With TensorFlow and Transformers/
1. Project Overview.mp4	13.1 MB
1. Project Overview.srt	3.5 KB
2. Getting the Data (Kaggle API).mp4	36.7 MB
2. Getting the Data (Kaggle API).srt	8.6 KB
2.1 Notebook.html	0.2 KB
3. Preprocessing.mp4	65.5 MB
3. Preprocessing.srt	15.5 KB
3.1 Notebook.html	0.2 KB
4. Building a Dataset.mp4	23.7 MB
4. Building a Dataset.srt	6.2 KB
4.1 Notebook.html	0.2 KB
5. Dataset Shuffle, Batch, Split, and Save.mp4	31.6 MB
5. Dataset Shuffle, Batch, Split, and Save.srt	7.8 KB
5.1 Notebook.html	0.2 KB
6. Build and Save.mp4	80.8 MB
6. Build and Save.srt	14.4 KB
6.1 Notebook.html	0.2 KB
7. Loading and Prediction.mp4	59.5 MB
7. Loading and Prediction.srt	12.0 KB
7.1 Notebook.html	0.2 KB
/7. Long Text Classification With BERT/
1. Classification of Long Text Using Windows.mp4	121.8 MB
1. Classification of Long Text Using Windows.srt	24.8 KB
1.1 Article.html	0.2 KB
1.2 Notebook.html	0.2 KB
2. Window Method in PyTorch.mp4	89.1 MB
2. Window Method in PyTorch.srt	16.7 KB
2.1 Notebook.html	0.2 KB
/8. Named Entity Recognition (NER)/
1. Introduction to spaCy.mp4	54.2 MB
1. Introduction to spaCy.srt	9.6 KB
1.1 Notebook.html	0.2 KB
1.2 spaCy Model Docs.html	0.1 KB
10. NER With roBERTa.mp4	61.9 MB
10. NER With roBERTa.srt	10.6 KB
10.1 Notebook.html	0.2 KB
2. Extracting Entities.mp4	35.2 MB
2. Extracting Entities.srt	6.9 KB
2.1 Notebook.html	0.2 KB
3. NER Walkthrough.html	0.1 KB
4. Authenticating With The Reddit API.mp4	37.4 MB
4. Authenticating With The Reddit API.srt	8.0 KB
4.1 Notebook.html	0.2 KB
5. Pulling Data With The Reddit API.mp4	93.3 MB
5. Pulling Data With The Reddit API.srt	13.2 KB
5.1 Notebook.html	0.2 KB
6. Extracting ORGs From Reddit Data.mp4	29.5 MB
6. Extracting ORGs From Reddit Data.srt	6.9 KB
6.1 Data.html	0.2 KB
6.2 Notebook.html	0.2 KB
7. Getting Entity Frequency.mp4	19.3 MB
7. Getting Entity Frequency.srt	4.0 KB
7.1 Notebook.html	0.2 KB
8. Entity Blacklist.mp4	21.1 MB
8. Entity Blacklist.srt	4.1 KB
8.1 Notebook.html	0.2 KB
9. NER With Sentiment.mp4	104.7 MB
9. NER With Sentiment.srt	20.1 KB
9.1 Notebook.html	0.2 KB
/9. Question and Answering/
1. Open Domain and Reading Comprehension.mp4	16.9 MB
1. Open Domain and Reading Comprehension.srt	3.6 KB
1.1 Notebook.html	0.2 KB
2. Retrievers, Readers, and Generators.mp4	30.1 MB
2. Retrievers, Readers, and Generators.srt	7.3 KB
2.1 Notebook.html	0.2 KB
3. Intro to SQuAD 2.0.mp4	26.6 MB
3. Intro to SQuAD 2.0.srt	6.8 KB
3.1 Notebook.html	0.2 KB
4. Processing SQuAD Training Data.mp4	40.3 MB
4. Processing SQuAD Training Data.srt	7.2 KB
4.1 Notebook.html	0.2 KB
5. (Optional) Processing SQuAD Training Data with Match-Case.mp4	31.6 MB
5. (Optional) Processing SQuAD Training Data with Match-Case.srt	5.2 KB
5.1 Notebook.html	0.2 KB
5.2 Pattern Matching Article.html	0.2 KB
6. Processing SQuAD Dev Data.html	0.1 KB
7. Our First Q&A Model.mp4	47.9 MB
7. Our First Q&A Model.srt	9.2 KB
7.1 Notebook.html	0.2 KB
Total files 288