Remove 2022 Remove Algorithm Remove K-nearest Neighbors
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Five machine learning types to know

IBM Journey to AI blog

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

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Everything you should know about AI models

Dataconomy

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. K-nearest Neighbors For both regression and classification tasks, the K-nearest Neighbors (kNN) model provides a straightforward supervised ML solution.

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Everything you should know about AI models

Dataconomy

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. K-nearest Neighbors For both regression and classification tasks, the K-nearest Neighbors (kNN) model provides a straightforward supervised ML solution.

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Fundamentals of Recommendation Systems

PyImageSearch

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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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. And this work appeared in AAAI 2022. The following is a transcript of his presentation, edited lightly for readability. AB : Got it.

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. And this work appeared in AAAI 2022. The following is a transcript of his presentation, edited lightly for readability. AB : Got it.