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2. Azure Cognitive Search - Wikipedia

   en.wikipedia.org/wiki/Azure_Cognitive_Search

   The Search as a service framework is intended to provide developers with complex search capabilities for mobile and web development while hiding infrastructure requirements and search algorithm complexities. Azure Search is a recent addition to Microsoft's Infrastructure as a Service (IaaS) approach.

3. Feature hashing - Wikipedia

   en.wikipedia.org/wiki/Feature_hashing

   In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From this, a bag of words (BOW) representation is constructed: the individual tokens are extracted and counted, and each distinct token in the training set defines a feature (independent variable) of each of the documents in both the training and test sets.

4. Vector database - Wikipedia

   en.wikipedia.org/wiki/Vector_database

   A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.

5. Retrieval-augmented generation - Wikipedia

   en.wikipedia.org/wiki/Retrieval-augmented_generation

   Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.

6. Support vector machine - Wikipedia

   en.wikipedia.org/wiki/Support_vector_machine

   Alternatively, recent work in Bayesian optimization can be used to select and , often requiring the evaluation of far fewer parameter combinations than grid search. The final model, which is used for testing and for classifying new data, is then trained on the whole training set using the selected parameters.

7. Automatic vectorization - Wikipedia

   en.wikipedia.org/wiki/Automatic_vectorization

   Automatic vectorization, in parallel computing, is a special case of automatic parallelization, where a computer program is converted from a scalar implementation, which processes a single pair of operands at a time, to a vector implementation, which processes one operation on multiple pairs of operands at once.

8. Vectorization - Wikipedia

   en.wikipedia.org/wiki/Vectorization

   Automatic vectorization, a compiler optimization that transforms loops to vector operations; Image tracing, the creation of vector from raster graphics; Word embedding, mapping words to vectors, in natural language processing

9. Transformer (deep learning architecture) - Wikipedia

   en.wikipedia.org/wiki/Transformer_(deep_learning...

   The RNNsearch model introduced an attention mechanism to seq2seq for machine translation to solve the bottleneck problem (of the fixed-size output vector), allowing the model to process long-distance dependencies more easily. The name is because it "emulates searching through a source sentence during decoding a translation".