AWS Machine Learning Blog

APRIL 19, 2024

We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query. This notebook will download a publicly available slide deck , convert each slide into the JPG file format, and upload these to the S3 bucket. We run these notebooks one by one. I need numbers."

AWS 126

AWS ML ML Database 126

AWS Machine Learning Blog

JANUARY 30, 2024

We perform a k-nearest neighbor (k=1) search to retrieve the most relevant embedding matching the user query. Setting k=1 retrieves the most relevant slide to the user question. In this notebook, we download the LLaVA-v1.5-7B An OpenSearch vector search is performed using these embeddings. The model.tar.gz

AWS 130

AWS ML ML K-nearest Neighbors 130

PyImageSearch

JUNE 19, 2023

machine learning, statistics, probability, and algebra) are used to achieve this. machine learning, statistics, probability, and algebra) are employed to recommend our popular daily applications. This is where machine learning, statistics, and algebra come into play. These engines utilize user data (e.g.,

K-nearest Neighbors 57

K-nearest Neighbors Clustering Algorithm Deep Learning 57

PyImageSearch

JANUARY 27, 2025

This lesson is the last in a 2-part series on Mastering Approximate Nearest Neighbor Search : Implementing Approximate Nearest Neighbor Search with KD-Trees Approximate Nearest Neighbor with Locality Sensitive Hashing (LSH) (this tutorial) To learn how to implement LSH for approximate nearest neighbor search, just keep reading.

K-nearest Neighbors 92

K-nearest Neighbors Algorithm Data Preparation Database 92

ODSC - Open Data Science

JANUARY 5, 2024

It is a library for array manipulation that has been downloaded hundreds of times per month and stands at over 25,000 stars on GitHub. What makes it popular is that it is used in a wide variety of fields, including data science, machine learning, and computational physics. What’s next for me and these top Python libraries?

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

AWS Machine Learning Blog

APRIL 5, 2023

Amazon SageMaker Serverless Inference is a purpose-built inference service that makes it easy to deploy and scale machine learning (ML) models. The first step is to download the pre-trained model weighting file, put it into a model.tar.gz You can use CLIP with Amazon SageMaker to perform encoding.

ML 111

ML ML AWS K-nearest Neighbors 111

AWS Machine Learning Blog

SEPTEMBER 25, 2024

Run the following command on the terminal to download the sample code from Github: git clone [link] Generate sample posts and compute multimodal embeddings In the code repository, we provide some sample product images (bag, car, perfume, and candle) that were created using the Amazon Titan Image Generator model. Choose Open JupyterLab.

AWS 92

AWS K-nearest Neighbors ML ML 92

PyImageSearch

SEPTEMBER 16, 2024

Jump Right To The Downloads Section Understanding Anomaly Detection: Concepts, Types, and Algorithms What Is Anomaly Detection? For instance, if a user who typically accesses the network during business hours suddenly logs in at midnight and starts downloading large amounts of data, this behavior would be considered anomalous.

Clustering 57

Clustering Algorithm Machine Learning Machine Learning 57

Flipboard

FEBRUARY 7, 2025

Create an OpenSearch model When you work with machine learning (ML) models, in OpenSearch, you use OpenSearchs ml-commons plugin to create a model. Yaliang Wu is a Software Engineering Manager at AWS, focusing on OpenSearch projects, machine learning, and generative AI applications.

Database 86

Database AWS Python ML 86

Dataconomy

DECEMBER 12, 2024

These samples can provide a good basis for machine learning , which can determine (with some probability) the type of unknown files using a model built on the different distributions. To implement our automated download system, we used Selenium in Python to control the browser using a Firefox driver.

K-nearest Neighbors 91

K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 91