Algorithm, Database and K-nearest Neighbors

Healthcare revolution: Vector databases for patient similarity search and precision diagnosis

Data Science Dojo

JANUARY 30, 2024

Traditional hea l t h c a r e databases struggle to grasp the complex relationships between patients and their clinical histories. Vector databases are revolutionizing healthcare data management. That’s where vector databases come in handy—they are made on purpose to handle this special kind of data.

Database

Database K-nearest Neighbors Natural Language Processing Algorithm

Implementing Approximate Nearest Neighbor Search with KD-Trees

PyImageSearch

DECEMBER 23, 2024

Or think about a real-time facial recognition system that must match a face in a crowd to a database of thousands. These scenarios demand efficient algorithms to process and retrieve relevant data swiftly. This is where Approximate Nearest Neighbor (ANN) search algorithms come into play.

K-nearest Neighbors

K-nearest Neighbors Algorithm Deep Learning Deep Learning

OpenSearch Vector Engine is now disk-optimized for low cost, accurate vector search

Flipboard

JANUARY 24, 2025

Start by estimating the memory required to support your disk-optimized k-NN index (with the default 32 times compression rate) using the following formula: Required memory (bytes) = 1.1 Disk mode uses the HNSW algorithm to build indexes, so m is one of the algorithm parameters, and it defaults to 16.

K-nearest Neighbors

K-nearest Neighbors ML ML Algorithm

Webinars

Automation, Evolved: Your New Playbook For Smarter Knowledge Work

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Data mining

Dataconomy

MARCH 4, 2025

Data mining is a fascinating field that blends statistical techniques, machine learning, and database systems to reveal insights hidden within vast amounts of data. By utilizing algorithms and statistical models, data mining transforms raw data into actionable insights.

Data Mining

Data Mining Data Mining Data Mining Decision Trees

Stacking Ensemble Method for Brain Tumor Classification: Performance Analysis

Towards AI

MAY 10, 2024

Ensemble models can be generated using a single algorithm with numerous variations, known as a homogeneous ensemble, or by using different techniques, known as a heterogeneous ensemble [3]. 4] Dataset The dataset comes from Kaggle [5], which contains a database of 3206 brain MRI images. Stacking Model Representation Diagram. [4]

K-nearest Neighbors

K-nearest Neighbors Decision Trees Machine Learning Machine Learning

Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning Blog

NOVEMBER 13, 2024

It works by analyzing the visual content to find similar images in its database. Store embeddings : Ingest the generated embeddings into an OpenSearch Serverless vector index, which serves as the vector database for the solution. Display results : Display the top K similar results to the user. b64encode(resized_image).decode('utf-8')

AWS

AWS Database K-nearest Neighbors AI

Vector Databases 101: A Beginner’s Guide to Vector Search and Indexing

Towards AI

FEBRUARY 19, 2025

Vector Databases 101: A Beginners Guide to Vector Search and Indexing Photo by Google DeepMind on Unsplash Introduction Alright, folks! The secret sauce behind all of this is vector search and vector databases, helping power similarity-based recommendations and retrieval! Traditional databases? They tap out.

Database

Database K-nearest Neighbors Machine Learning Machine Learning

Use language embeddings for zero-shot classification and semantic search with Amazon Bedrock

AWS Machine Learning Blog

FEBRUARY 13, 2025

Caching is performed on Amazon CloudFront for certain topics to ease the database load. Amazon Aurora PostgreSQL-Compatible Edition and pgvector Amazon Aurora PostgreSQL-Compatible is used as the database, both for the functionality of the application itself and as a vector store using pgvector. Its hosted on AWS Lambda.

AWS

AWS K-nearest Neighbors Clustering Algorithm

Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning Blog

MARCH 11, 2025

The goal is to index these five webpages dynamically using a common embedding algorithm and then use a retrieval (and reranking) strategy to retrieve chunks of data from the indexed knowledge base to infer the final answer. Vector database FloTorch selected Amazon OpenSearch Service as a vector database for its high-performance metrics.

K-nearest Neighbors

K-nearest Neighbors AWS Database AI

OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service

AWS Machine Learning Blog

FEBRUARY 5, 2025

Previously, OfferUps search engine was built with Elasticsearch (v7.10) on Amazon Elastic Compute Cloud (Amazon EC2), using a keyword search algorithm to find relevant listings. The search microservice processes the query requests and retrieves relevant listings from Elasticsearch using keyword search (BM25 as a ranking algorithm).

K-nearest Neighbors

K-nearest Neighbors Machine Learning Machine Learning Database

Five machine learning types to know

IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

Machine Learning

Machine Learning Machine Learning Supervised Learning Clustering

Approximate Nearest Neighbor with Locality Sensitive Hashing (LSH)

PyImageSearch

JANUARY 27, 2025

Refinement: The candidate set is then refined by computing the actual distances between the query point and the candidates to find the approximate nearest neighbors. Random Projection The first step in the algorithm is to sample random vectors in the same -dimensional space as input vector.

K-nearest Neighbors

K-nearest Neighbors Algorithm Data Preparation Database

Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

AWS Machine Learning Blog

SEPTEMBER 8, 2023

You then use Exact k-NN with scoring script so that you can search by two fields: celebrity names and the vector that captured the semantic information of the article. You also generate an embedding of this newly written article, so that you can search OpenSearch Service for the nearest images to the article in this vector space.

K-nearest Neighbors

K-nearest Neighbors AWS ML ML

A Guide to Unsupervised Machine Learning Models | Types | Applications

Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. What is Unsupervised Machine Learning?

Machine Learning

Machine Learning Machine Learning Clustering K-nearest Neighbors

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of treating each input as entirely unique, we can use a distance-based approach like k-nearest neighbors (k-NN) to assign a class based on the most similar examples surrounding the input. For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module.

Algorithm

Algorithm Machine Learning Machine Learning K-nearest Neighbors

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

AWS Machine Learning Blog

APRIL 19, 2024

We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) 7b) model to generate text responses to user questions based on the most similar slide retrieved from the vector database. OpenSearch Serverless is an on-demand serverless configuration for Amazon OpenSearch Service.

AWS

AWS ML ML Database

Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

DECEMBER 6, 2023

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

SQL

SQL AWS Analytics Analytics

Implement serverless semantic search of image and live video with Amazon Titan Multimodal Embeddings

AWS Machine Learning Blog

JUNE 3, 2024

You store the embeddings of the video frame as a k-nearest neighbors (k-NN) vector in your OpenSearch Service index with the reference to the video clip and the frame in the S3 bucket itself (Step 3). Conversely, a smaller K leads to faster search times and lower costs, but may lower result quality.

AWS

AWS K-nearest Neighbors ML ML

How to Use Machine Learning (ML) for Time Series Forecasting?—?NIX United

Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. The selection of the number of neighbors and feature selection is a daunting task.

Machine Learning

Machine Learning Machine Learning ML ML

Understanding and Building Machine Learning Models

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. It involves algorithms that identify and use data patterns to make predictions or decisions based on new, unseen data. Types of Machine Learning Machine Learning algorithms can be categorised based on how they learn and the data type they use.

Machine Learning

Machine Learning Machine Learning Algorithm Decision Trees

Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

Data Analyst

Data Analyst Data Science Machine Learning Machine Learning

Classification in ML: Lessons Learned From Building and Deploying a Large-Scale Model

The MLOps Blog

DECEMBER 19, 2022

Lesson 1: Mitigating data sparsity problems within ML classification algorithms What are the most popular algorithms used to solve a multi-class classification problem? The selection of the correct loss function plays a pivotal role in the success of the algorithm. Let’s take a look at some of them.

ML

ML ML Algorithm Deep Learning

Image Embedding: Benefits, Use Cases, and Best Practices

DagsHub

JUNE 24, 2024

A great example of traditional image features is SIFT (Scale Invariant Feature Transform) which is a quite involved algorithm that finds key points in images: Source: [link] By leveraging image embeddings, all the weight lifting of feature extraction is done by a neural network.

Clustering

Clustering Machine Learning Machine Learning K-nearest Neighbors

Debugging data to build better and more fair ML applications

Snorkel AI

APRIL 28, 2023

Often, it requires you to co-design the algorithm and also the system set. If they’re necessary, how can we create a new algorithm to accommodate it? On one hand, there’s a data management community trying to understand data transformation and computing some functions over exponentially many databases for decades.

ML

ML ML Machine Learning Machine Learning

Debugging data to build better and more fair ML applications

Snorkel AI

APRIL 28, 2023

Often, it requires you to co-design the algorithm and also the system set. If they’re necessary, how can we create a new algorithm to accommodate it? On one hand, there’s a data management community trying to understand data transformation and computing some functions over exponentially many databases for decades.

ML

ML ML Machine Learning Machine Learning

Build a multimodal social media content generator using Amazon Bedrock

AWS Machine Learning Blog

SEPTEMBER 25, 2024

#LuxuryBrand #TimelessElegance #ExclusiveCollection Retrieve and analyze the top three relevant posts The next step involves using the generated image and text to search for the top three similar historical posts from a vector database. The following code snippet shows the implementation of this step.

AWS

AWS K-nearest Neighbors ML ML

[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

MAY 23, 2023

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science. What is Data Science? Let us see some examples.

Data Science

Data Science Decision Trees Machine Learning Machine Learning

Data Science Current

Healthcare revolution: Vector databases for patient similarity search and precision diagnosis

Implementing Approximate Nearest Neighbor Search with KD-Trees

Webinars

Trending Sources

OpenSearch Vector Engine is now disk-optimized for low cost, accurate vector search

Webinars

Data mining

Stacking Ensemble Method for Brain Tumor Classification: Performance Analysis

Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

Vector Databases 101: A Beginner’s Guide to Vector Search and Indexing

Use language embeddings for zero-shot classification and semantic search with Amazon Bedrock

Benchmarking Amazon Nova and GPT-4o models with FloTorch

OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service

Five machine learning types to know

Approximate Nearest Neighbor with Locality Sensitive Hashing (LSH)

Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

A Guide to Unsupervised Machine Learning Models | Types | Applications

How IDIADA optimized its intelligent chatbot with Amazon Bedrock

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

Implement serverless semantic search of image and live video with Amazon Titan Multimodal Embeddings

How to Use Machine Learning (ML) for Time Series Forecasting?—?NIX United

Understanding and Building Machine Learning Models

Basic Data Science Terms Every Data Analyst Should Know

Classification in ML: Lessons Learned From Building and Deploying a Large-Scale Model

Image Embedding: Benefits, Use Cases, and Best Practices

Debugging data to build better and more fair ML applications

Debugging data to build better and more fair ML applications

Build a multimodal social media content generator using Amazon Bedrock

[Updated] 100+ Top Data Science Interview Questions

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