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Enhancing Search Relevancy with Cohere Rerank 3.5 and Amazon OpenSearch Service

Flipboard

It supports advanced features such as result highlighting, flexible pagination, and k-nearest neighbor (k-NN) search for vector and semantic search use cases. This allows the system to recognize synonyms and related concepts, such as action figures is related to toys and comic book characters to super heroes.

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Spatial Intelligence: Why GIS Practitioners Should Embrace Machine Learning- How to Get Started.

Towards AI

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? This will be a good way to get familiar with ML. Types of Machine Learning for GIS 1.

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Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

AWS Machine Learning Blog

We detail the steps to use an Amazon Titan Multimodal Embeddings model to encode images and text into embeddings, ingest embeddings into an OpenSearch Service index, and query the index using the OpenSearch Service k-nearest neighbors (k-NN) functionality. In her free time, she likes to go for long runs along the beach.

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STUDY: Socially aware temporally causal decoder recommender systems

Google Research AI blog

Effective recommendations that present students with relevant reading material helps keep students reading, and this is where machine learning (ML) can help. ML has been widely used in building recommender systems for various types of digital content, ranging from videos to books to e-commerce items.

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How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

Services class Texts belonging to this class consist of explicit requests for services such as room reservations, hotel bookings, dining services, cinema information, tourism-related inquiries, and similar service-oriented requests. For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module.