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Debugging data to build better and more fair ML applications

Snorkel AI

The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier. Registration is now open for The Future of Data-Centric AI 2023.

ML 52
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Debugging data to build better and more fair ML applications

Snorkel AI

The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier. Registration is now open for The Future of Data-Centric AI 2023.

ML 52
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Everything you should know about AI models

Dataconomy

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!

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Everything you should know about AI models

Dataconomy

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!

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8 of the Top Python Libraries You Should be Using in 2024

ODSC - Open Data Science

In this blog, we’re going to take a look at some of the top Python libraries of 2023 and see what exactly makes them tick. Top Python Libraries of 2023 and 2024 NumPy NumPy is the gold standard for scientific computing in Python and is always considered amongst top Python libraries. What’s next for me and these top Python libraries?

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

AWS Machine Learning Blog

In this series, we use the slide deck Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023 to demonstrate the solution. We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query.

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Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

In late 2023, Planet announced a partnership with AWS to make its geospatial data available through Amazon SageMaker. In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Planet’s data is therefore a valuable resource for geospatial ML.