Flipboard

DECEMBER 18, 2024

In this blog post, well dive into the various scenarios for how Cohere Rerank 3.5 It supports advanced features such as result highlighting, flexible pagination, and k-nearest neighbor (k-NN) search for vector and semantic search use cases.

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Towards AI

FEBRUARY 7, 2024

However, after seeing its significant progress, I compiled it as a blog post so everyone else interested can benefit from it. self.index.add(self.embeddings_vec) def topk(self, vector, k = 4): """ A function that takes in a vector and an optional parameter k and returns the indices of the k nearest neighbors in the index.

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Pickl AI

MARCH 26, 2024

The K Nearest Neighbors (KNN) algorithm of machine learning stands out for its simplicity and effectiveness. This blog aims to familiarise you with the fundamentals of the KNN algorithm in machine learning and its importance in shaping modern data analytics methodologies. What are K Nearest Neighbors in Machine Learning?

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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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Dataversity

JUNE 4, 2021

The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. Click to learn more about author Kartik Patel. In this article, we will discuss the KNN Classification method of analysis. What Is the KNN Classification Algorithm?

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AWS Machine Learning Blog

FEBRUARY 13, 2025

This is the k-nearest neighbor (k-NN) algorithm. In k-NN, you can make assumptions around a data point based on its proximity to other data points. You can use the embedding of an article and check the similarity of the article against the preceding embeddings.

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AWS Machine Learning Blog

MARCH 11, 2025

Retrieval (and reranking) strategy FloTorch used a retrieval strategy with a k-nearest neighbor (k-NN) of five for retrieved chunks. Each provisioned node was r7g.4xlarge, 4xlarge, selected for its availability and sufficient capacity to meet the performance requirements. FloTorch used HSNW indexing in OpenSearch Service.

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Pickl AI

JANUARY 21, 2025

This blog explores various types of Machine Learning algorithms, illustrating their functionalities and applications with relevant examples. k-Nearest Neighbors (k-NN) k-NN is a simple algorithm that classifies new instances based on the majority class among its k nearest neighbours in the training dataset.

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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.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

The embedded image is stored in an OpenSearch index with a k-nearest neighbors (k-NN) vector field. Example with a multimodal embedding model The following is a code sample performing ingestion with Amazon Titan Multimodal Embeddings as described earlier.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

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. Using the k-nearest neighbors (k-NN) algorithm, you define how many images to return in your results.

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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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AWS Machine Learning Blog

SEPTEMBER 6, 2023

A k-Nearest Neighbor (k-NN) index is enabled to allow searching of embeddings from the OpenSearch Service. Three separate endpoints using the recommended default SageMaker instance types are deployed.

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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.

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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. AI studio The post Five machine learning types to know appeared first on IBM Blog. Naïve Bayes classifiers —enable classification tasks for large datasets. Explore the watsonx.ai

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AWS Machine Learning Blog

APRIL 3, 2024

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.

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Pickl AI

AUGUST 28, 2024

In this blog, we will delve into the world of classification algorithms, exploring their basics, key algorithms, how they work, advanced topics, practical implementation, and the future of classification in Machine Learning. Instead, they memorise the training data and make predictions by finding the nearest neighbour.

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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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PyImageSearch

JANUARY 27, 2025

On Line 28 , we sort the distances and select the top k nearest neighbors. This demonstrates the efficiency of the LSH in finding nearest neighbors compared to more straightforward, brute-force methods (e.g., -NN Finally, on Lines 32-37 , we measure the time taken to perform the -NN search and print the results.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The function then searches the OpenSearch Service image index for images matching the celebrity name and the k-nearest neighbors for the vector using cosine similarity using Exact k-NN with scoring script. The function generates an embedding of the summarized article using the Amazon Titan Multimodal Embeddings model.

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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. It aims to partition a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean. Hence, it is considered as one of the best-unsupervised learning algorithms.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Formally, often k-nearest neighbors (KNN) or approximate nearest neighbor (ANN) search is often used to find other snippets with similar semantics. Semantic retrieval BM25 focuses on lexical matching.

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SAS Software

NOVEMBER 10, 2023

K Nearest Neighbour is an algorithm that stores all the available observations and classifies the new data based on a similarity measure. The post Using K Nearest Neighbours algorithm in scenario tuning appeared first on SAS Blogs.

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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). You split the video files into frames and save them in a S3 bucket (Step 1).

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AWS Machine Learning Blog

JANUARY 6, 2023

In this post, we present a solution to handle OOC situations through knowledge graph-based embedding search using the k-nearest neighbor (kNN) search capabilities of OpenSearch Service. The IMDb-Knowledge-Graph-Blog/part3-out-of-catalog/run_imdb_demo.py Solution overview.

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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. To make this work, we need to transform the textual interactions into a format that allows algebraic operations.

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AWS Machine Learning Blog

JUNE 9, 2023

To solve the problem of finding the field of study for any given paper, simply perform a k-nearest neighbor search on the embeddings. In this case, the model reaches an MRR of 0.31 on the test set of the constructed graph. python3 -m graphstorm.run.gs_link_prediction --inference --num_trainers 8 --part-config /data/oagv2.1/mag_bert_constructed/mag.json

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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. The user input is converted into embeddings using the Titan Multimodal Embeddings model accessed via Amazon Bedrock.

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Mlearning.ai

APRIL 6, 2023

⚠ You can solve the below-mentioned questions from this blog ⚠ ✔ What if I am building Low code — No code ML automation tool and I do not have any orchestrator or memory management system ? In contrast, for datasets with low dimensionality, simpler algorithms such as Naive Bayes or K-Nearest Neighbors may be sufficient.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this analysis, we use a K-nearest neighbors (KNN) model to conduct crop segmentation, and we compare these results with ground truth imagery on an agricultural region. For more information about Planet, including its existing data products and upcoming product releases, visit [link].

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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. The user input is converted into embeddings using the Amazon Titan Text Embeddings model accessed using Amazon Bedrock. An OpenSearch Service vector search is performed using these embeddings.

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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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ODSC - Open Data Science

JANUARY 5, 2024

In this blog, we’re going to take a look at some of the top Python libraries of 2023 and see what exactly makes them tick. Python is still one of the most popular programming languages that developers flock to. Some are well-known names, and others are known within their communities.

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AWS Machine Learning Blog

AUGUST 26, 2024

The indexing process consists of the following stages: Document preprocessing – Clean and normalize text from various sources Chunking – Break documents into manageable pieces (1,200 tokens with 50-token overlap) Vectorization – Convert text chunks into vector representations using an embeddings model Storage – Index vectors and metadata in the database (..)

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