Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI.

Machine Learning 105

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 105

Pickl AI

MARCH 26, 2024

The K Nearest Neighbors (KNN) algorithm of machine learning stands out for its simplicity and effectiveness. What are K Nearest Neighbors in Machine Learning? Definition of KNN Algorithm K Nearest Neighbors (KNN) is a simple yet powerful machine learning algorithm for classification and regression tasks.

K-nearest Neighbors 52

K-nearest Neighbors Machine Learning Machine Learning Algorithm 52

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.

Database 113

Database SQL Data Analysis Data Analysis 113

Towards AI

NOVEMBER 6, 2024

Evaluation allows us to select the top embedding models across various dimensions, potentially considering multiple values for k nearest neighbors. Create a Golden Dataset The first step is to create a “golden dataset” comprising queries, relevant context (chunks or documents from the corpus), and ground truth answers.

K-nearest Neighbors 116

K-nearest Neighbors AI AI Machine Learning 116

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.

Machine Learning 119

Machine Learning Machine Learning Algorithm K-nearest Neighbors 119

AWS Machine Learning Blog

MARCH 11, 2025

One of the most critical applications for LLMs today is Retrieval Augmented Generation (RAG), which enables AI models to ground responses in enterprise knowledge bases such as PDFs, internal documents, and structured data. Each provisioned node was r7g.4xlarge, FloTorch used HSNW indexing in OpenSearch Service.

K-nearest Neighbors 94

K-nearest Neighbors AWS Database AI 94

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.

AWS 109

AWS Database K-nearest Neighbors AI 109

Pickl AI

JANUARY 21, 2025

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. Example: Organising documents into a tree structure based on topic similarity for better information retrieval systems.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

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.

K-nearest Neighbors 116

K-nearest Neighbors AWS ML ML 116

AWS Machine Learning Blog

FEBRUARY 25, 2025

These included document translations, inquiries about IDIADAs internal services, file uploads, and other specialized requests. This approach allows for tailored responses and processes for different types of user needs, whether its a simple question, a document translation, or a complex inquiry about IDIADAs services.

Algorithm 70

Algorithm Machine Learning Machine Learning K-nearest Neighbors 70

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.

AWS 115

AWS K-nearest Neighbors AI AI 115

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!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

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!

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

PyImageSearch

JANUARY 27, 2025

Another example is in the field of text document similarity. Imagine you have a vast library of documents and want to identify near-duplicate documents or find documents similar to a query document. text documents, images, and other multimedia content).

K-nearest Neighbors 82

K-nearest Neighbors Algorithm Data Preparation Database 82

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 92

Machine Learning Machine Learning Supervised Learning Clustering 92

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.,

K-nearest Neighbors 56

K-nearest Neighbors Clustering Algorithm Deep Learning 56

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.

AWS 121

AWS ML ML Database 121

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.

AWS 119

AWS ML ML K-nearest Neighbors 119

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.

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

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.

AWS 119

AWS AI AI K-nearest Neighbors 119

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).

AWS 103

AWS K-nearest Neighbors ML ML 103

Flipboard

FEBRUARY 7, 2025

You will create a connector to SageMaker with Amazon Titan Text Embeddings V2 to create embeddings for a set of documents with population statistics. Alternately, you can follow the Boto 3 documentation to make sure you use the right credentials. When you build a RAG application, you choose a knowledge base and a retrieval mechanism.

Database 87

Database AWS Python ML 87

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.

Machine Learning 130

Machine Learning Machine Learning ML ML 130

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.

AWS 101

AWS Natural Language Processing K-nearest Neighbors Computer Science 101

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.

AWS 96

AWS ML ML Machine Learning 96

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

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.

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

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.

ML 104

ML ML AWS K-nearest Neighbors 104

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).

K-nearest Neighbors 66

K-nearest Neighbors Algorithm Clustering Machine Learning 66

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

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

Flipboard

DECEMBER 18, 2024

It supports advanced features such as result highlighting, flexible pagination, and k-nearest neighbor (k-NN) search for vector and semantic search use cases. Lexical search relies on exact keyword matching between the query and documents. The querys encoding is then compared to pre-computed document embeddings.

K-nearest Neighbors 121

K-nearest Neighbors AWS ML ML 121

DagsHub

DECEMBER 23, 2024

  Image classification Text categorization Document sorting Sentiment analysis Medical image diagnosis Advantages Pool-based active learning can leverage relationships between data points through techniques like density-based sampling and cluster analysis.   Traditional Active Learning has the following characteristics.

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52

Flipboard

JANUARY 24, 2025

You can then run searches for the top K documents in an index that are most similar to a given query vector, which could be a question, keyword, or content (such as an image, audio clip, or text) that has been encoded by the same ML model. To learn more, refer to the documentation.

K-nearest Neighbors 145

K-nearest Neighbors ML ML Algorithm 145

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?

Machine Learning 52

Machine Learning Machine Learning Algorithm Clustering 52

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.

K-nearest Neighbors 108

K-nearest Neighbors AWS ML ML 108

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.

AWS 82

AWS K-nearest Neighbors Database AI 82