Towards AI

JULY 3, 2024

The competition for best algorithms can be just as intense in machine learning and spatial analysis, but it is based more objectively on data, performance, and particular use cases. Community & Support: Verify the availability of documentation and the level of community support. So, Who Do I Have?

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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? You just want to create and analyze simple maps not to learn algebra all over again.

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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.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

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Pickl AI

MARCH 26, 2024

Summary: The KNN algorithm in machine learning presents advantages, like simplicity and versatility, and challenges, including computational burden and interpretability issues. Unlocking the Power of KNN Algorithm in Machine Learning Machine learning algorithms are significantly impacting diverse fields.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates 25 years of experience innovating with AI and machine learning at Amazon. For more information on managing credentials securely, see the AWS Boto3 documentation. He holds six AWS certifications, including the Machine Learning Specialty Certification.

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AWS Machine Learning Blog

DECEMBER 6, 2023

Such data often lacks the specialized knowledge contained in internal documents available in modern businesses, which is typically needed to get accurate answers in domains such as pharmaceutical research, financial investigation, and customer support. For example, imagine that you are planning next year’s strategy of an investment company.

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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. Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models.

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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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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. 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.

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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.

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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.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

Amazon Rekognition makes it easy to add image analysis capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

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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.

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Mlearning.ai

MARCH 28, 2023

The Effect of Class Imbalance This has a significant impact on the performance of machine learning models. Handling class imbalance can improve the performance and robustness of machine learning models, and ensure that they generalize well to new data. You can reach the documentation from here. Image by the author.

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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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Mlearning.ai

JUNE 6, 2023

In today’s blog, we will see some very interesting Python Machine Learning projects with source code. This list will consist of Machine learning projects, Deep Learning Projects, Computer Vision Projects , and all other types of interesting projects with source codes also provided.

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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. These models are designed to replicate the human brain’s cognitive functions, enabling them to perceive, reason, learn, and make decisions based on data.

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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. These models are designed to replicate the human brain’s cognitive functions, enabling them to perceive, reason, learn, and make decisions based on data.

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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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AWS Machine Learning Blog

JANUARY 6, 2023

This mapping can be done by manually mapping frequent OOC queries to catalog content or can be automated using machine learning (ML). 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.

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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).

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PyImageSearch

JUNE 19, 2023

machine learning, statistics, probability, and algebra) are used to achieve this. machine learning, statistics, probability, and algebra) are employed to recommend our popular daily applications. This is where machine learning, statistics, and algebra come into play. These engines utilize user data (e.g.,

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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. Part 3 compares the two approaches.

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ODSC - Open Data Science

JANUARY 5, 2024

What makes it popular is that it is used in a wide variety of fields, including data science, machine learning, and computational physics. Scikit-learn A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists.

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AWS Machine Learning Blog

AUGUST 26, 2024

This includes sales collateral, customer engagements, external web data, machine learning (ML) insights, and more. Numbers checking – Identifies numerical data in both the input and generated documents, determining their intersection and flagging potential hallucinations.

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AWS Machine Learning Blog

JUNE 3, 2024

Kinesis Video Streams makes it straightforward to securely stream video from connected devices to AWS for analytics, machine learning (ML), playback, and other processing. It enables real-time video ingestion, storage, encoding, and streaming across devices. You split the video files into frames and save them in a S3 bucket (Step 1).

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AWS Machine Learning Blog

APRIL 5, 2023

Amazon SageMaker Serverless Inference is a purpose-built inference service that makes it easy to deploy and scale machine learning (ML) models. You save those embeddings into a k-NN index in OpenSearch Service. Ananya Roy is a Senior Data Lab architect specialised in AI and machine learning based out of Sydney Australia.

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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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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. You dont use it directly; you create an OpenSearch model for that.

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Pickl AI

SEPTEMBER 12, 2024

By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success. Data Cleaning: Raw data often contains errors, inconsistencies, and missing values.

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Mlearning.ai

FEBRUARY 15, 2023

Figure 1 Preprocessing Data preprocessing is an essential step in building a Machine Learning model. We will generate a measure called Term Frequency, Inverse Document Frequency, shortened to tf-idf for each term in our dataset. K-Nearest Neighbou r: The k-Nearest Neighbor algorithm has a simple concept behind it.

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PyImageSearch

OCTOBER 30, 2023

We will now examine how Spotify uses these data sources and advance machine learning techniques to address the music recommendation problem. In this, each playlist is considered as an ordered ‘document’ of songs. RL agents hence can interact and learn important playlist generation aspects to improve user satisfaction metrics.

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DagsHub

DECEMBER 23, 2024

Targeted Resource Allocation Traditional machine-learning approaches often require extensive data labeling, which can be costly and time-consuming. Active Learning significantly reduces these costs through strategic selection of data points.   Traditional Active Learning has the following characteristics.

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Google Research AI blog

NOVEMBER 28, 2022

Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems ( NeurIPS 2022 ), the biggest machine learning conference of the year.

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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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