2022, K-nearest Neighbors and ML - Data Science Current

Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning Blog

MARCH 11, 2025

simple Finance Did meta have any mergers or acquisitions in 2022? Retrieval (and reranking) strategy FloTorch used a retrieval strategy with a k-nearest neighbor (k-NN) of five for retrieved chunks. Dr. Hemant Joshi has over 20 years of industry experience building products and services with AI/ML technologies.

K-nearest Neighbors

K-nearest Neighbors AWS Database AI

Five machine learning types to know

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. However, the growing influence of ML isn’t without complications.

Machine Learning

Machine Learning Machine Learning Supervised Learning Clustering

Everything you should know about AI models

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? In March of 2022, DeepMind released Chinchilla AI.

K-nearest Neighbors

K-nearest Neighbors Decision Trees AI AI

Webinars

What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

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Everything you should know about AI models

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? In March of 2022, DeepMind released Chinchilla AI.

K-nearest Neighbors

K-nearest Neighbors Decision Trees AI AI

Use DeepSeek with Amazon OpenSearch Service vector database and Amazon SageMaker

Flipboard

FEBRUARY 7, 2025

For more information, see Creating connectors for third-party ML platforms. Create an OpenSearch model When you work with machine learning (ML) models, in OpenSearch, you use OpenSearchs ml-commons plugin to create a model. You created an OpenSearch ML model group and model that you can use to create ingest and search pipelines.

Database

Database AWS Python ML

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

AWS Machine Learning Blog

DECEMBER 6, 2023

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.

SQL

SQL AWS Analytics Analytics

Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

ML

ML ML Machine Learning Machine Learning

Debugging data to build better and more fair ML applications

Snorkel AI

APRIL 28, 2023

He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. A transcript of the talk follows.

ML

ML ML Machine Learning Machine Learning

Debugging data to build better and more fair ML applications

Snorkel AI

APRIL 28, 2023

He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. A transcript of the talk follows.

ML

ML ML Machine Learning Machine Learning

Retell a Paper: “Self-supervised Learning in Remote Sensing: A Review”

Mlearning.ai

JULY 6, 2023

2022’s paper. 2022 Deep learning notoriously needs a lot of data in training. 2022 Figure 3. 2022 Figure 4. 2022 for further reference. Some common quantitative evaluations are linear probing , K nearest neighbors (KNN), and fine-tuning. Image: Wang et al., Taxonomy of SSL. Source: Wang et al.,

Supervised Learning

Supervised Learning Deep Learning Deep Learning K-nearest Neighbors

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

Snorkel AI

APRIL 11, 2023

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.

K-nearest Neighbors

K-nearest Neighbors Clustering Deep Learning Deep Learning

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

Snorkel AI

APRIL 11, 2023

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.

K-nearest Neighbors

K-nearest Neighbors Clustering Deep Learning Deep Learning

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

Snorkel AI

APRIL 11, 2023

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.

K-nearest Neighbors

K-nearest Neighbors Clustering Deep Learning Deep Learning

Everything to know about Anomaly Detection in Machine Learning

Pickl AI

SEPTEMBER 3, 2023

CAGR during 2022-2030. On the other hand, 48% use ML and AI for gaining insights into the prospects and customers. k-Nearest Neighbors (k-NN): In the supervised approach, k-NN assigns labels to instances based on their k-nearest neighbours. Billion which is supposed to increase by 35.6%

Machine Learning

Machine Learning Machine Learning K-nearest Neighbors Algorithm

From prediction to prevention: Machines’ struggle to save our hearts

Dataconomy

SEPTEMBER 1, 2023

The time has come for us to treat ML and AI algorithms as more than simple trends. This technological journey of humanity, which started with the slow integration of IoT systems such as Alexa into our lives, has peaked in the last quarter of 2022 with the increase in the prevalence and use of ChatGPT and other LLM models.

Decision Trees

Decision Trees Machine Learning Machine Learning Support Vector Machines

Google at NeurIPS 2022

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.

Machine Learning

Machine Learning Machine Learning Algorithm Clustering

Data Science Current

Benchmarking Amazon Nova and GPT-4o models with FloTorch

Five machine learning types to know

Webinars

Trending Sources

Everything you should know about AI models

Webinars

Everything you should know about AI models

Use DeepSeek with Amazon OpenSearch Service vector database and Amazon SageMaker

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

Identifying defense coverage schemes in NFL’s Next Gen Stats

Debugging data to build better and more fair ML applications

Debugging data to build better and more fair ML applications

Retell a Paper: “Self-supervised Learning in Remote Sensing: A Review”

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

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

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

Everything to know about Anomaly Detection in Machine Learning

From prediction to prevention: Machines’ struggle to save our hearts

Google at NeurIPS 2022

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