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001 Attention Introduction.mp4
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MP4
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15.8 MB
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001 Classification of Long Text Using Windows.mp4
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MP4
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116.1 MB
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001 Intro to Retriever-Reader and Haystack.mp4
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MP4
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13.9 MB
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001 Introduction to Sentiment Analysis.mp4
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MP4
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37.5 MB
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001 Introduction to Similarity.mp4
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MP4
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28.2 MB
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001 Introduction to spaCy.mp4
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MP4
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51.6 MB
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001 Introduction.mp4
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MP4
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9.2 MB
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001 ODQA Stack Structure.mp4
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MP4
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6.2 MB
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001 Open Domain and Reading Comprehension.mp4
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MP4
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16.1 MB
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001 Project Overview.mp4
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MP4
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12.5 MB
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001 Q&A Performance With Exact Match (EM).mp4
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MP4
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18.2 MB
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001 Stopwords.mp4
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MP4
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23 MB
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001 The Three Eras of AI.mp4
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MP4
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22.2 MB
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001 Visual Guide to BERT Pretraining.mp4
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MP4
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28.6 MB
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002 Alignment With Dot-Product.mp4
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MP4
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49.1 MB
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002 Course Overview.mp4
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MP4
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34.4 MB
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002 Creating the Database.mp4
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MP4
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42.4 MB
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002 Extracting Entities.mp4
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MP4
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33.5 MB
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002 Extracting The Last Hidden State Tensor.mp4
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MP4
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29.7 MB
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002 Getting the Data (Kaggle API).mp4
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MP4
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35 MB
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002 Introduction to BERT For Pretraining Code.mp4
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MP4
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29.3 MB
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002 Prebuilt Flair Models.mp4
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MP4
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30.7 MB
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002 Pros and Cons of Neural AI.mp4
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MP4
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32.8 MB
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002 ROUGE in Python.mp4
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MP4
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21.7 MB
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002 Retrievers, Readers, and Generators.mp4
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MP4
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28.7 MB
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002 Tokens Introduction.mp4
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MP4
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24 MB
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002 What is Elasticsearch_.mp4
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MP4
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23.5 MB
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002 Window Method in PyTorch.mp4
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MP4
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84.9 MB
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003 Applying ROUGE to Q&A.mp4
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MP4
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33.9 MB
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003 Authenticating With The Reddit API.mp4
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MP4
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35.6 MB
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003 BERT Pretraining - Masked-Language Modeling (MLM).mp4
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MP4
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46.7 MB
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003 Building the Haystack Pipeline.mp4
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MP4
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55.8 MB
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003 Dot-Product Attention.mp4
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MP4
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29 MB
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003 Elasticsearch Setup (Windows).mp4
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MP4
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20.9 MB
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003 Environment Setup.mp4
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MP4
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37.3 MB
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003 Intro to SQuAD 2.0.mp4
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MP4
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25.4 MB
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003 Introduction to Sentiment Models With Transformers.mp4
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MP4
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26.9 MB
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003 Model-Specific Special Tokens.mp4
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MP4
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18.9 MB
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003 Preprocessing.mp4
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MP4
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62.5 MB
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003 Sentence Vectors With Mean Pooling.mp4
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MP4
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32.1 MB
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003 Word Vectors.mp4
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MP4
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21.7 MB
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004 Alternative Setup.html
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HTML
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2.8 KB
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004 BERT Pretraining - Next Sentence Prediction (NSP).mp4
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MP4
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42.1 MB
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004 Building a Dataset.mp4
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MP4
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22.6 MB
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004 Elasticsearch Setup (Linux).mp4
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MP4
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20.2 MB
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004 Processing SQuAD Training Data.mp4
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MP4
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38.4 MB
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004 Pulling Data With The Reddit API.mp4
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MP4
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88.9 MB
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004 Recall, Precision and F1.mp4
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MP4
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21 MB
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004 Recurrent Neural Networks.mp4
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MP4
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17.1 MB
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004 Self Attention.mp4
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MP4
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28.4 MB
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004 Stemming.mp4
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MP4
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17.2 MB
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004 Tokenization And Special Tokens For BERT.mp4
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MP4
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55.4 MB
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004 Using Cosine Similarity.mp4
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MP4
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33.9 MB
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005 (Optional) Processing SQuAD Training Data with Match-Case.mp4
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MP4
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30.1 MB
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005 Bidirectional Attention.mp4
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MP4
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10.8 MB
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005 CUDA Setup.mp4
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MP4
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23.7 MB
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005 Dataset Shuffle, Batch, Split, and Save.mp4
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MP4
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30.2 MB
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005 Elasticsearch in Haystack.mp4
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MP4
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39 MB
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005 Extracting ORGs From Reddit Data.mp4
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MP4
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28.1 MB
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005 Lemmatization.mp4
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MP4
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10.6 MB
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005 Long Short-Term Memory.mp4
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MP4
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6.3 MB
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005 Longest Common Subsequence (LCS).mp4
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MP4
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15 MB
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005 Making Predictions.mp4
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MP4
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26 MB
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005 Similarity With Sentence-Transformers.mp4
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MP4
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23 MB
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005 The Logic of MLM.mp4
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MP4
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79.4 MB
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006 Build and Save.mp4
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MP4
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77 MB
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006 Encoder-Decoder Attention.mp4
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MP4
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25.2 MB
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006 Fine-tuning with MLM - Data Preparation.mp4
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MP4
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76.7 MB
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006 Getting Entity Frequency.mp4
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MP4
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18.4 MB
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006 Multi-head and Scaled Dot-Product Attention.mp4
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MP4
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33.8 MB
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006 Our First Q&A Model.mp4
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MP4
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45.7 MB
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006 Q&A Performance With ROUGE.mp4
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MP4
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18.7 MB
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006 Sparse Retrievers.mp4
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MP4
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20.4 MB
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006 Unicode Normalization - Canonical and Compatibility Equivalence.mp4
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MP4
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17 MB
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007 Cleaning the Index.mp4
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MP4
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26.4 MB
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007 Entity Blacklist.mp4
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MP4
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20.1 MB
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007 Fine-tuning with MLM - Training.mp4
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MP4
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69.7 MB
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007 Loading and Prediction.mp4
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MP4
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56.8 MB
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007 Self-Attention.mp4
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MP4
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20.8 MB
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007 Unicode Normalization - Composition and Decomposition.mp4
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MP4
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20.3 MB
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008 Fine-tuning with MLM - Training with Trainer.mp4
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MP4
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19.9 MB
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008 Implementing a BM25 Retriever.mp4
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MP4
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12.5 MB
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008 Multi-head Attention.mp4
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MP4
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13.3 MB
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008 NER With Sentiment.mp4
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MP4
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99.9 MB
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008 Unicode Normalization - NFD and NFC.mp4
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MP4
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20 MB
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009 NER With roBERTa.mp4
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MP4
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59 MB
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009 Positional Encoding.mp4
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MP4
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55.5 MB
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009 The Logic of NSP.mp4
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MP4
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20.9 MB
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009 Unicode Normalization - NFKD and NFKC.mp4
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MP4
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30.4 MB
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009 What is FAISS_.mp4
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MP4
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42.9 MB
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010 FAISS in Haystack.mp4
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MP4
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68.1 MB
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010 Fine-tuning with NSP - Data Preparation.mp4
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MP4
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78 MB
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010 Transformer Heads.mp4
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MP4
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39.8 MB
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011 Fine-tuning with NSP - DataLoader.mp4
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MP4
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14.3 MB
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011 What is DPR_.mp4
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MP4
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29.7 MB
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012 The DPR Architecture.mp4
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MP4
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14.3 MB
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012 The Logic of MLM and NSP.mp4
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MP4
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26.3 MB
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013 Fine-tuning with MLM and NSP - Data Preparation.mp4
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MP4
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43.6 MB
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013 Retriever-Reader Stack.mp4
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MP4
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75.3 MB
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Downloaded from 1337x.html
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HTML
|
512 B
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external-assets-links.txt
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TXT
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1.3 KB
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