AI-Powered Search, Video Edition

[kingers]

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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