2.7 GB | 33min 41s | mp4 | 1920X1080  | 16:9
Genre:eLearning |Language:English

Files Included :
001  Part 1  Modern search relevance.mp4 (4.48 MB)
002  Chapter 1  Introducing AI-powered search.mp4 (48.91 MB)
003  Chapter 1  Understanding user intent.mp4 (42.85 MB)
004  Chapter 1  How does AI-powered search work.mp4 (74.09 MB)
005  Chapter 1  Summary.mp4 (5.53 MB)
006  Chapter 2  Working with natural language.mp4 (57.31 MB)
007  Chapter 2  The structure of natural language.mp4 (13.2 MB)
008  Chapter 2  Distributional semantics and embeddings.mp4 (40.52 MB)
009  Chapter 2  Modeling domain-specific knowledge.mp4 (21.46 MB)
010  Chapter 2  Challenges in natural language understanding for search.mp4 (36.26 MB)
011  Chapter 2  Content + signals The fuel powering AI-powered search.mp4 (13.07 MB)
012  Chapter 2  Summary.mp4 (5.45 MB)
013  Chapter 3  Ranking and content-based relevance.mp4 (79.93 MB)
014  Chapter 3  Controlling the relevance calculation.mp4 (66.55 MB)
015  Chapter 3  Implementing user and domain-specific relevance ranking.mp4 (8.81 MB)
016  Chapter 3  Summary.mp4 (3.85 MB)
017  Chapter 4  Crowdsourced relevance.mp4 (65.59 MB)
018  Chapter 4  Introducing reflected intelligence.mp4 (78.07 MB)
019  Chapter 4  Summary.mp4 (4.39 MB)
020  Part 2  Learning domain-specific intent.mp4 (6.33 MB)
021  Chapter 5  Knowledge graph learning.mp4 (19.56 MB)
022  Chapter 5  Using our search engine as a knowledge graph.mp4 (5.53 MB)
023  Chapter 5  Automatically extracting knowledge graphs from content.mp4 (28.73 MB)
024  Chapter 5  Learning intent by traversing semantic knowledge graphs.mp4 (110.29 MB)
025  Chapter 5  Using knowledge graphs for semantic search.mp4 (4.05 MB)
026  Chapter 5  Summary.mp4 (4.04 MB)
027  Chapter 6  Using context to learn domain-specific language.mp4 (21.4 MB)
028  Chapter 6  Query-sense disambiguation.mp4 (25.76 MB)
029  Chapter 6  Learning related phrases from query signals.mp4 (55.69 MB)
030  Chapter 6  Phrase detection from user signals.mp4 (14.54 MB)
031  Chapter 6  Misspellings and alternative representations.mp4 (35.86 MB)
032  Chapter 6  Pulling it all together.mp4 (5.43 MB)
033  Chapter 6  Summary.mp4 (2.37 MB)
034  Chapter 7  Interpreting query intent through semantic search.mp4 (25.29 MB)
035  Chapter 7  Indexing and searching on a local reviews dataset.mp4 (12.14 MB)
036  Chapter 7  An end-to-end semantic search example.mp4 (9.69 MB)
037  Chapter 7  Query interpretation pipelines.mp4 (80.83 MB)
038  Chapter 7  Summary.mp4 (4.4 MB)
039  Part 3  Reflected intelligence.mp4 (5.82 MB)
040  Chapter 8  Signals-boosting models.mp4 (9.61 MB)
041  Chapter 8  Normalizing signals.mp4 (11.22 MB)
042  Chapter 8  Fighting signal spam.mp4 (21.32 MB)
043  Chapter 8  Combining multiple signal types.mp4 (15.91 MB)
044  Chapter 8  Time decays and short-lived signals.mp4 (29.07 MB)
045  Chapter 8  Index-time vs  query-time boosting Balancing scale vs  flexibility.mp4 (50.03 MB)
046  Chapter 8  Summary.mp4 (4.48 MB)
047  Chapter 9  Personalized search.mp4 (24.29 MB)
048  Chapter 9  Recommendation algorithm approaches.mp4 (25.82 MB)
049  Chapter 9  Implementing collaborative filtering.mp4 (59.04 MB)
050  Chapter 9  Personalizing search using content-based embeddings.mp4 (72.55 MB)
051  Chapter 9  Challenges with personalizing search results.mp4 (17.51 MB)
052  Chapter 9  Summary.mp4 (4.84 MB)
053  Chapter 10  Learning to rank for generalizable search relevance.mp4 (27.9 MB)
054  Chapter 10  Step 1 A judgment list, starting with the training data.mp4 (7.11 MB)
055  Chapter 10  Step 2 Feature logging and engineering.mp4 (21.95 MB)
056  Chapter 10  Step 3 Transforming LTR to a traditional machine learning problem.mp4 (33.17 MB)
057  Chapter 10  Step 4 Training (and testing!) the model.mp4 (19.95 MB)
058  Chapter 10  Steps 5 and 6 Upload a model and search.mp4 (22.15 MB)
059  Chapter 10  Rinse and repeat.mp4 (5.44 MB)
060  Chapter 10  Summary.mp4 (5.26 MB)
061  Chapter 11  Automating learning to rank with click models.mp4 (64.33 MB)
062  Chapter 11  Overcoming position bias.mp4 (26.98 MB)
063  Chapter 11  Handling confidence bias Not upending your model due to a few lucky clicks.mp4 (39.48 MB)
064  Chapter 11  Exploring your training data in an LTR system.mp4 (9.55 MB)
065  Chapter 11  Summary.mp4 (4.34 MB)
066  Chapter 12  Overcoming ranking bias through active learning.mp4 (33.51 MB)
067  Chapter 12  AB testing a new model.mp4 (39.92 MB)
068  Chapter 12  Overcoming presentation bias Knowing when to explore vs  exploit.mp4 (49.3 MB)
069  Chapter 12  Exploit, explore, gather, rinse, repeat A robust automated LTR loop.mp4 (11.9 MB)
070  Chapter 12  Summary.mp4 (5.04 MB)
071  Part 4  The search frontier.mp4 (5.81 MB)
072  Chapter 13  Semantic search with dense vectors.mp4 (19.09 MB)
073  Chapter 13  Search using dense vectors.mp4 (34.41 MB)
074  Chapter 13  Getting text embeddings by using a Transformer encoder.mp4 (23.82 MB)
075  Chapter 13  Applying Transformers to search.mp4 (42.58 MB)
076  Chapter 13  Natural language autocomplete.mp4 (68.66 MB)
077  Chapter 13  Semantic search with LLM embeddings.mp4 (26.52 MB)
078  Chapter 13  Quantization and representation learning for more efficient vector search.mp4 (103.73 MB)
079  Chapter 13  Cross-encoders vs  bi-encoders.mp4 (25.15 MB)
080  Chapter 13  Summary.mp4 (4.56 MB)
081  Chapter 14  Question answering with a fine-tuned large language model.mp4 (54.54 MB)
082  Chapter 14  Constructing a question-answering training dataset.mp4 (49.07 MB)
083  Chapter 14  Fine-tuning the question-answering model.mp4 (29.59 MB)
084  Chapter 14  Building the reader with the new fine-tuned model.mp4 (5.05 MB)
085  Chapter 14  Incorporating the retriever Using the question-answering model with the search engine.mp4 (20.37 MB)
086  Chapter 14  Summary.mp4 (3.67 MB)
087  Chapter 15  Foundation models and emerging search paradigms.mp4 (38.48 MB)
088  Chapter 15  Generative search.mp4 (110.47 MB)
089  Chapter 15  Multimodal search.mp4 (47.26 MB)
090  Chapter 15  Other emerging AI-powered search paradigms.mp4 (18.14 MB)
091  Chapter 15  Hybrid search.mp4 (36.03 MB)
092  Chapter 15  Convergence of contextual technologies.mp4 (10.04 MB)
093  Chapter 15  All the above, please!.mp4 (5.66 MB)
094  Chapter 15  Summary.mp4 (5.02 MB)
095  Appendix A  Running the code examples.mp4 (7.39 MB)
096  Appendix A  Pulling the source code.mp4 (2.69 MB)
097  Appendix A  Building and running the code.mp4 (10.69 MB)
098  Appendix A  Working with Jupyter.mp4 (7.74 MB)
099  Appendix A  Working with Docker.mp4 (6.18 MB)
100  Appendix B  Supported search engines and vector databases.mp4 (3.48 MB)
101  Appendix B  Swapping out the engine.mp4 (3.49 MB)
102  Appendix B  The engine and collection abstractions.mp4 (8.92 MB)
103  Appendix B  Adding support for additional engines.mp4 (4.82 MB)]
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