Data Science Dojo

JANUARY 30, 2024

Unlike traditional, table-like structures, they excel at handling the intricate, multi-dimensional nature of patient information. Working with vector data is tough because regular databases, which usually handle one piece of information at a time, can’t handle the complexity and large amount of this type of data.

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Database K-nearest Neighbors Algorithm Natural Language Processing 361

Data Science Dojo

MAY 27, 2024

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

Using Amazon Bedrock Knowledge Bases, FMs and agents can retrieve contextual information from your company’s private data sources for RAG. It supports exact and approximate nearest-neighbor algorithms and multiple storage and matching engines. For information on creating service roles, refer to Service roles.

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K-nearest Neighbors AWS ML ML 115

Flipboard

DECEMBER 18, 2024

The ability to quickly access relevant information is a key differentiator in todays competitive landscape. improves search results for best matching 25 (BM25), a keyword-based algorithm that performs lexical search, in addition to semantic search. This post is co-written with Elliott Choi from Cohere. Overview of Cohere Rerank 3.5

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K-nearest Neighbors AWS ML ML 121

Flipboard

MAY 25, 2025

Feature selection via the Boruta and LASSO algorithms preceded the construction of predictive models using Random Forest, Decision Tree, K-Nearest Neighbors, Support Vector Machine, LightGBM, and XGBoost. Demographic data, physiological status, and non-invasive test indicators were collected.

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K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 81

Dataconomy

MARCH 4, 2025

Data mining refers to the systematic process of analyzing large datasets to uncover hidden patterns and relationships that inform and address business challenges. By utilizing algorithms and statistical models, data mining transforms raw data into actionable insights.

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 91

Pickl AI

APRIL 13, 2025

Summary: Classifier in Machine Learning involves categorizing data into predefined classes using algorithms like Logistic Regression and Decision Trees. Classifiers are algorithms designed to perform this task efficiently, helping industries solve problems like spam detection, fraud prevention, and medical diagnosis.

Machine Learning 52

Machine Learning Machine Learning Decision Trees K-nearest Neighbors 52

Towards AI

MAY 10, 2024

There are various techniques used to obtain information about tumors. Ensemble models can be generated using a single algorithm with numerous variations, known as a homogeneous ensemble, or by using different techniques, known as a heterogeneous ensemble [3]. For the meta-model, k-nearest neighbors were used again.

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K-nearest Neighbors Decision Trees Machine Learning Machine Learning 105

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.

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Machine Learning Machine Learning Algorithm K-nearest Neighbors 116

AWS Machine Learning Blog

FEBRUARY 13, 2025

However, to demonstrate how this system works, we use an algorithm designed to reduce the dimensionality of the embeddings, t-distributed Stochastic Neighbor Embedding (t-SNE) , so that we can view them in two dimensions. This is the k-nearest neighbor (k-NN) algorithm.

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AWS K-nearest Neighbors Clustering Algorithm 117

AWS Machine Learning Blog

NOVEMBER 13, 2024

A reverse image search engine enables users to upload an image to find related information instead of using text-based queries. For more information on managing credentials securely, see the AWS Boto3 documentation. The closer vectors are to one another in this space, the more similar the information they represent is.

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AWS Database K-nearest Neighbors AI 120

Dataconomy

FEBRUARY 26, 2025

It bridges the gap between mere correlation and genuine insight, enabling organizations to make informed decisions based on the root causes of events. Step-by-step process Collect observational data: Gather extensive datasets that track various events over time to inform causal relationships.

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K-nearest Neighbors AI AI Artificial Intelligence 91

Towards AI

APRIL 4, 2024

Created by the author with DALL E-3 Machine learning algorithms are the “cool kids” of the tech industry; everyone is talking about them as if they were the newest, greatest meme. Shall we unravel the true meaning of machine learning algorithms and their practicability?

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K-nearest Neighbors Machine Learning Machine Learning Decision Trees 80

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? For example, it takes millions of images and runs them through a training algorithm.

Machine Learning 102

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 102

AWS Machine Learning Blog

MARCH 11, 2025

The goal is to index these five webpages dynamically using a common embedding algorithm and then use a retrieval (and reranking) strategy to retrieve chunks of data from the indexed knowledge base to infer the final answer. For more information, contact us at info@flotorch.ai. FloTorch used HSNW indexing in OpenSearch Service.

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K-nearest Neighbors AWS Database AI 116

Dataversity

JUNE 4, 2021

What Is the KNN Classification Algorithm? 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. It is useful for recognizing patterns […].

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K-nearest Neighbors Algorithm Data Analysis Data Analysis 52

Towards AI

APRIL 14, 2023

In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations. However, typical algorithms do not produce a binary result but instead, provide a relevancy score for which labels are the most appropriate. Thus tail labels have an inflated score in the metric.

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K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

AWS Machine Learning Blog

FEBRUARY 25, 2025

Services class Texts belonging to this class consist of explicit requests for services such as room reservations, hotel bookings, dining services, cinema information, tourism-related inquiries, and similar service-oriented requests. The aim is to understand which approach is most suitable for addressing the presented challenge.

Algorithm 85

Algorithm Machine Learning Machine Learning K-nearest Neighbors 85

AWS Machine Learning Blog

SEPTEMBER 8, 2023

For this post, you use the following: Name, Id, and Urls – The celebrity name, a unique Amazon Rekognition ID, and list of URLs such as the celebrity’s IMDb or Wikipedia link for further information. Using the k-nearest neighbors (k-NN) algorithm, you define how many images to return in your results.

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Pickl AI

JULY 31, 2023

Examples of Eager Learning Algorithms: Logistic Regression : A classic Eager Learning algorithm used for binary classification tasks. Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. Eager Learning Algorithms: How does it work?

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

PyImageSearch

JANUARY 27, 2025

Random Projection The first step in the algorithm is to sample random vectors in the same -dimensional space as input vector. These word vectors are trained from Twitter data making them semantically rich in information. Each word is mapped to its corresponding vector representation.

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K-nearest Neighbors Algorithm Data Preparation Database 101

IBM Journey to AI blog

DECEMBER 19, 2023

Common machine learning algorithms for supervised learning include: K-nearest neighbor (KNN) algorithm : This algorithm is a density-based classifier or regression modeling tool used for anomaly detection. Isolation forest: This type of anomaly detection algorithm uses unsupervised data.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Dataconomy

APRIL 4, 2023

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. The information from previous decisions is analyzed via the decision tree. This technique is based on the concept that related information tends to cluster together.

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K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. The information from previous decisions is analyzed via the decision tree. This technique is based on the concept that related information tends to cluster together.

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K-nearest Neighbors Decision Trees AI AI 91

Mlearning.ai

FEBRUARY 2, 2023

The Multi-Armed Bandit (MAB) algorithm is a type of reinforcement learning algorithm that addresses the trade-off between exploration and exploitation in decision-making. In the context of the MAB algorithm, each arm represents a decision that can be taken, and the reward corresponds to some measure of performance or utility.

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K-nearest Neighbors Algorithm Support Vector Machines Decision Trees 52

Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. What is Unsupervised Machine Learning?

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Machine Learning Machine Learning Clustering K-nearest Neighbors 52

PyImageSearch

JUNE 19, 2023

Each service uses unique techniques and algorithms to analyze user data and provide recommendations that keep us returning for more. By analyzing how users have interacted with items in the past, we can use algorithms to approximate the utility function and make personalized recommendations that users will love.

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K-nearest Neighbors Clustering Algorithm Deep Learning 57

Mlearning.ai

FEBRUARY 17, 2023

ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning. Strictly, everything that I said earlier is based on Machine learning algorithms and, of course, strong math and theory of algorithms behind them. Great example of this tecnique is K-means clustering algorithm.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

AWS Machine Learning Blog

APRIL 19, 2024

Solution overview The solution provides an implementation for answering questions using information contained in text and visual elements of a slide deck. We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query. I need numbers. Up to 4x higher throughput.

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Mlearning.ai

JULY 4, 2023

In the era of data-driven decision making, social media platforms like Twitter have become more than just channels for communication, but also a trove of information offering profound insights into human behaviors and societal trends. This initial data collection lays the foundation for our subsequent analysis and modeling.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 52

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. This retrieval can happen using different algorithms.

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AWS Natural Language Processing K-nearest Neighbors Computer Science 124

Google Research AI blog

AUGUST 15, 2023

We developed the STUDY algorithm in partnership with Learning Ally , an educational nonprofit, aimed at promoting reading in dyslexic students, that provides audiobooks to students through a school-wide subscription program. However, this data representation requires careful diligence if it is to be modeled by transformers.

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AWS Machine Learning Blog

JUNE 3, 2024

It processes and generates information from distinct data types like text and images. For more information, refer to Amazon Titan Multimodal Embeddings G1 model. Determining the optimal value of K in the k-NN algorithm for vector similarity search is significant for balancing accuracy, performance, and cost.

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AWS K-nearest Neighbors ML ML 125

Snorkel AI

APRIL 11, 2023

Even for simple tasks like information extraction, locating entities and relations can take a half an hour or more, even for simple news stories. So the key problem here is, how can we efficiently identify the most informative training examples? Annotation at word level can actually take 10 times longer than the audio clip.

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K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

Even for simple tasks like information extraction, locating entities and relations can take a half an hour or more, even for simple news stories. So the key problem here is, how can we efficiently identify the most informative training examples? Annotation at word level can actually take 10 times longer than the audio clip.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

Even for simple tasks like information extraction, locating entities and relations can take a half an hour or more, even for simple news stories. So the key problem here is, how can we efficiently identify the most informative training examples? Annotation at word level can actually take 10 times longer than the audio clip.

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K-nearest Neighbors Clustering Deep Learning Deep Learning 52

AWS Machine Learning Blog

DECEMBER 6, 2023

This method of enriching the LLM generation context with information retrieved from your internal data sources is called Retrieval Augmented Generation (RAG), and produces assistants that are domain specific and more trustworthy, as shown by Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

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