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Implementing Approximate Nearest Neighbor Search with KD-Trees

PyImageSearch

One of the most effective methods to perform ANN search is to use KD-Trees (K-Dimensional Trees). KD-Trees are a type of binary search tree that partitions data points into k-dimensional space, allowing for efficient querying of nearest neighbors. Traditional exact nearest neighbor search methods (e.g.,

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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

In this blog, we will explore the details of both approaches and navigate through their differences. A visual representation of discriminative AI – Source: Analytics Vidhya Discriminative modeling, often linked with supervised learning, works on categorizing existing data. What is Generative AI?

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Build cost-effective RAG applications with Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Amazon OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service, a fully managed service that makes it simple to perform interactive log analytics, real-time application monitoring, website search, and vector search with its k-nearest neighbor (kNN) plugin.

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Classifiers in Machine Learning

Pickl AI

Classification is a subset of supervised learning, where labelled data guides the algorithm to make predictions. This blog explores types of classification tasks, popular algorithms, methods for evaluating performance, real-world applications, and why classifiers are indispensable in Machine Learning.

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Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch

PyImageSearch

Home Table of Contents Build a Search Engine: Deploy Models and Index Data in AWS OpenSearch Introduction What Will We Do in This Blog? What Will We Do in This Blog? By the end of this guide, you will have a fully indexed movie dataset with embeddings, ready for semantic search in the next blog. What’s Coming Next?

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Build a Search Engine: Setting Up AWS OpenSearch

Flipboard

Vector and Semantic Search: Leverages machine learning-powered search techniques, including k-NN (k-nearest neighbors) and dense vector embeddings, for applications like AI-driven search, recommendation systems, and similarity search. Figure 16: Image by the Author Whats Next? Thats not the case. Download the code!

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Five machine learning types to know

IBM Journey to AI blog

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. They’re also part of a family of generative learning algorithms that model the input distribution of a given class or/category. Explore the watsonx.ai