Flipboard

NOVEMBER 17, 2023

The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to make foundation models effective for domain-specific tasks. MongoDB Atlas Vector Search uses a technique called k-nearest neighbors (k-NN) to search for similar vectors.

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Towards AI

JULY 15, 2024

We shall look at various machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can install and call their libraries in R studios, including executing the code. In-depth Documentation- R facilitates repeatability by analyzing data using a script-based methodology.

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Data Science Dojo

JULY 15, 2024

K-Nearest Neighbors (KNN): This method classifies a data point based on the majority class of its K nearest neighbors in the training data. Document Clustering: Grouping documents based on topic or content for efficient information retrieval.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

Amazon Titan Text Embeddings models generate meaningful semantic representations of documents, paragraphs, and sentences. It supports exact and approximate nearest-neighbor algorithms and multiple storage and matching engines. RAG helps FMs deliver more relevant, accurate, and customized responses.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Broadly speaking, a retriever is a module that takes a query as input and outputs relevant documents from one or more knowledge sources relevant to that query. Document ingestion In a RAG architecture, documents are often stored in a vector store. You must use the same embedding model at ingestion time and at search time.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

OpenSearch Service allows you to store vectors and other data types in an index, and offers rich functionality that allows you to search for documents using vectors and measuring the semantical relatedness, which we use in this post. 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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K-nearest Neighbors Decision Trees AI AI 91

PyImageSearch

JUNE 19, 2023

For example, term frequency–inverse document frequency (TF-IDF) ( Figure 7 ) is a popular text-mining technique in content-based recommendations. Inverse document frequency (IDF) assigns weight inversely proportional to the times the keyword occurs in the whole corpus. Figure 6: Illustration of how text mining works (source: Ko et al.,

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ODSC - Open Data Science

JANUARY 5, 2024

It is easy to use, with a well-documented API and a wide range of tutorials and examples available. First, it’s easy to use, the code is easy to learn and it has a well-documented API. Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use. What really makes Django are a few things.

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Python K-nearest Neighbors Data Science Data Visualization 52

AWS Machine Learning Blog

SEPTEMBER 6, 2023

Embeddings for documents are generated using the text-to-embeddings model and these embeddings are indexed into OpenSearch Service. A k-Nearest Neighbor (k-NN) index is enabled to allow searching of embeddings from the OpenSearch Service.

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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.

Machine Learning 97

Machine Learning Machine Learning Supervised Learning Clustering 97

AWS Machine Learning Blog

NOVEMBER 1, 2024

Intelligent responses and a direct conduit to Druva’s documentation – Users can gain in-depth knowledge about product features and functionalities without manual searches or watching training videos. Dru also suggests follow-up questions to enhance user experience. Dru also suggests resources for further learning.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

This centralized system consolidates a wide range of data sources, including detailed reports, FAQs, and technical documents. The system integrates structured data, such as tables containing product properties and specifications, with unstructured text documents that provide in-depth product descriptions and usage guidelines.

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Database SQL Data Analysis Data Analysis 112

AWS Machine Learning Blog

NOVEMBER 13, 2024

For more information on managing credentials securely, see the AWS Boto3 documentation. For example: aws s3 cp /Users/username/Documents/training/loafers s3://footwear-dataset/ --recursive Confirm the upload : Go back to the S3 console, open your bucket, and verify that the images have been successfully uploaded to the bucket.

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Mlearning.ai

MARCH 28, 2023

You can reach the documentation from here. For each sample in the minority class, it selects k nearest neighbors from the same class. It then selects one of these k neighbors at random and computes the difference between the feature vector of the original sample and the selected neighbor.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Python 52

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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AWS Machine Learning Blog

AUGUST 26, 2024

Numbers checking – Identifies numerical data in both the input and generated documents, determining their intersection and flagging potential hallucinations. This helps catch any fabricated or misrepresented quantitative information in the summaries.

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AWS Machine Learning Blog

JANUARY 30, 2024

This event in the SQS queue acts as a trigger to run the OSI pipeline, which in turn ingests the data (JSON file) as documents into the OpenSearch Serverless index. We perform a k-nearest neighbor (k=1) search to retrieve the most relevant embedding matching the user query.

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Mlearning.ai

FEBRUARY 15, 2023

Figure 5 Feature Extraction and Evaluation Because most classifiers and learning algorithms require numerical feature vectors with a fixed size rather than raw text documents with variable length, they cannot analyse the text documents in their original form.

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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

NOVEMBER 9, 2023

This benefits enterprise software development and helps overcome the following challenges: Sparse documentation or information for internal libraries and APIs that forces developers to spend time examining previously written code to replicate usage. Semantic retrieval BM25 focuses on lexical matching.

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AWS Machine Learning Blog

JANUARY 6, 2023

OpenSearch Service offers kNN search, which can enhance search in use cases such as product recommendations, fraud detection, and image, video, and some specific semantic scenarios like document and query similarity. Solution overview.

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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 documentation on Planet’s SDK for Python, see Planet SDK for Python.

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Machine Learning Machine Learning ML ML 133

Pickl AI

SEPTEMBER 12, 2024

J Jupyter Notebook: An open-source web application that allows users to create and share documents containing live code, equations, visualisations, and narrative text. K K-Means Clustering: An unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

PyImageSearch

OCTOBER 30, 2023

In this, each playlist is considered as an ordered ‘document’ of songs. text mining, K-nearest neighbor, clustering, matrix factorization, and neural networks). More specifically they use the Continuous Bag-of-Words (CBoW) algorithm with negative sampling. Netflix, LinkedIn, Amazon, and YouTube recommendation systems).

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AWS Machine Learning Blog

APRIL 5, 2023

Implementing this unified image and text search application consists of two phases: k-NN reference index – In this phase, you pass a set of corpus documents or product images through a CLIP model to encode them into embeddings. You save those embeddings into a k-NN index in OpenSearch Service.

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Mlearning.ai

JUNE 6, 2023

Document Scanner using OpenCV So guys, in this blog we will see how we can build a very simple yet powerful Document scanner using OpenCV. How to perform Face Recognition using KNN In this blog, we will see how we can perform Face Recognition using KNN (K-Nearest Neighbors Algorithm) and Haar cascades.

Machine Learning 52

Machine Learning Machine Learning Python Deep Learning 52

Google Research AI blog

NOVEMBER 28, 2022

Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź, Piotr Miłoś, Yuhuai Wu , Mateja Jamnik TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s Felix Chern , Blake Hechtman , Andy Davis , Ruiqi Guo , David Majnemer , Sanjiv Kumar When Does Dough Become a Bagel?

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Machine Learning Machine Learning Algorithm Clustering 52