AWS Machine Learning Blog

NOVEMBER 13, 2024

If you haven’t set up a SageMaker Studio domain, see this Amazon SageMaker blog post for instructions on setting up SageMaker Studio for individual users. To search against the database, you can use a vector search, which is performed using the k-nearest neighbors (k-NN) algorithm.

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AWS Machine Learning Blog

FEBRUARY 5, 2025

In this two-part blog post series, we explore the key opportunities OfferUp embraced on their journey to boost and transform their existing search solution from traditional lexical search to modern multimodal search powered by Amazon Bedrock and Amazon OpenSearch Service. For data handling, 24 data nodes (r6gd.2xlarge.search

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IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. K-means clustering is commonly used for market segmentation, document clustering, image segmentation and image compression.

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Pickl AI

JULY 17, 2023

The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. There are different kinds of unsupervised learning algorithms, including clustering, anomaly detection, neural networks, etc. K-Means Clustering: K-means is a popular and widely used clustering algorithm.

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IBM Journey to AI blog

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

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PyImageSearch

JUNE 19, 2023

This can be especially useful when recommending blogs, news articles, and other text-based content. K-Nearest Neighbor K-nearest neighbor (KNN) ( Figure 8 ) is an algorithm that can be used to find the closest points for a data point based on a distance measure (e.g.,

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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K-nearest Neighbors Decision Trees AI AI 91

Towards AI

FEBRUARY 19, 2025

In this short blog, were diving deep into vector databases what they are, how they work, and, most importantly, how to use them like a pro. But heres the catch scanning millions of vectors one by one (a brute-force k-Nearest Neighbors or KNN search) is painfully slow. Traditional databases? They tap out. 💡 Why?

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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. This doesnt imply that clusters coudnt be highly separable in higher dimensions.

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AWS Machine Learning Blog

JANUARY 6, 2023

OpenSearch Service currently has tens of thousands of active customers with hundreds of thousands of clusters under management processing trillions of requests per month. The IMDb-Knowledge-Graph-Blog/part3-out-of-catalog/run_imdb_demo.py versions), as well as visualization capabilities powered by OpenSearch Dashboards and Kibana (1.5

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AWS Machine Learning Blog

APRIL 5, 2023

out" embeddings.append(json.load(open(embedding_file))[0]) Create an ML-powered unified search engine This section discusses how to create a search engine that that uses k-NN search with embeddings. This includes configuring an OpenSearch Service cluster, ingesting item embedding, and performing free text and image search queries.

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AWS Machine Learning Blog

APRIL 19, 2024

This solution includes the following components: Amazon Titan Text Embeddings is a text embeddings model that converts natural language text, including single words, phrases, or even large documents, into numerical representations that can be used to power use cases such as search, personalization, and clustering based on semantic similarity.

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Mlearning.ai

MAY 23, 2023

Hey guys, we will see some of the Best and Unique Machine Learning Projects for final year engineering students in today’s blog. This is going to be a very interesting blog, so without any further due, let’s do it… 1. Self-Organizing Maps In this blog, we will see how we can implement self-organizing maps in Python.

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Mlearning.ai

MAY 21, 2023

Hey guys, we will see some of the Best and Unique Machine Learning Projects with Source Codes in today’s blog. Youtube Comments Extraction and Sentiment Analysis Flask App Hey, guys in this blog we will implement Youtube Comments Extraction and Sentiment Analysis in Python using Flask. This is going to be a very short blog.

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Machine Learning Machine Learning Python K-nearest Neighbors 52

Pickl AI

SEPTEMBER 3, 2023

The following blog will provide you a thorough evaluation on how Anomaly Detection Machine Learning works, emphasising on its types and techniques. Density-Based Spatial Clustering of Applications with Noise (DBSCAN): DBSCAN is a density-based clustering algorithm.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

We design a K-Nearest Neighbors (KNN) classifier to automatically identify these plays and send them for expert review. As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle).

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Mlearning.ai

JUNE 6, 2023

In today’s blog, we will see some very interesting Python Machine Learning projects with source code. Doctor-Patient Appointment System in Python using Flask Hey guys, in this blog we will see a Doctor-Patient Appointment System for Hospitals built in Python using Flask. I myself made this as my final year major project.

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PyImageSearch

OCTOBER 30, 2023

Spotify also establishes a taste profile by grouping the music users often listen into clusters. These clusters are not based on explicit attributes (e.g., Check out the complete blog series and dive deeper into recommendation systems with lessons that explore various recommendation engines (e.g., genre, artist, etc.)

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Pickl AI

NOVEMBER 18, 2024

Summary: The blog provides a comprehensive overview of Machine Learning Models, emphasising their significance in modern technology. Clustering and dimensionality reduction are common tasks in unSupervised Learning. customer segmentation), clustering algorithms like K-means or hierarchical clustering might be appropriate.

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The MLOps Blog

DECEMBER 19, 2022

A set of classes sometimes forms a group/cluster. So, we can plot the high-dimensional vector space into lower dimensions and evaluate the integrity at the cluster level. index.add(xb) # xq are query vectors, for which we need to search in xb to find the k nearest neighbors. # Creating the index.

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Pickl AI

DECEMBER 9, 2024

This blog aims to clarify the concept of inductive bias and its impact on model generalisation, helping practitioners make better decisions for their Machine Learning solutions. k-Nearest Neighbors (k-NN) The k-NN algorithm assumes that similar data points are close to each other in feature space.

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Mlearning.ai

MAY 23, 2023

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. The K-Nearest Neighbor Algorithm is a good example of an algorithm with low bias and high variance.

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Google Research AI blog

NOVEMBER 28, 2022

Lee, Chris De Sa, Karthik Sridharan On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games Runyu Zhang, Jincheng Mei , Bo Dai , Dale Schuurmans , Na Li Matryoshka Representation Learning Aditya Kusupati , Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha , Vivek Ramanujan, William Howard-Snyder, (..)

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AWS Machine Learning Blog

NOVEMBER 1, 2024

We tried different methods, including k-nearest neighbor (k-NN) search of vector embeddings, BM25 with synonyms , and a hybrid of both across fields including API routes, descriptions, and hypothetical questions. The request arrives at the microservice on our existing Amazon Elastic Container Service (Amazon ECS) cluster.

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