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Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI

ODSC - Open Data Science

This process is entirely automated, and when the same XGBoost model was re-trained on the cleaned data, it achieved 83% accuracy (with zero change to the modeling code). Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms that now power ML applications at hundreds of companies.

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The Hidden Cost of Poor Training Data in Machine Learning: Why Quality Matters

How to Learn Machine Learning

Real-Life Examples of Poor Training Data in Machine Learning Amazon’s Hiring Algorithm Disaster In 2018, Amazon made headlines for developing an AI-powered hiring tool to screen job applicants. Data Quality Factors to Consider So, how can you avoid these types of failures in your ML projects? Sounds great, right?

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Predict football punt and kickoff return yards with fat-tailed distribution using GluonTS

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The player tracking data contains the player’s position, direction, acceleration, and more (in x,y coordinates). There are around 3,000 and 4,000 plays from four NFL seasons (2018–2021) for punt and kickoff plays, respectively. The data distribution for punt and kickoff are different.

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Present and future of data cubes: an European EO perspective

Mlearning.ai

It can be gradually “enriched” so the typical hierarchy of data is thus: Raw data ↓ Cleaned data ↓ Analysis-ready data ↓ Decision-ready data ↓ Decisions. For example, vector maps of roads of an area coming from different sources is the raw data. 2018, July). Remote Sensing, 12(24), 4033.

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Introduction to Autoencoders

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By using our mathematical notation, the entire training process of the autoencoder can be written as follows: Figure 2 demonstrates the basic architecture of an autoencoder: Figure 2: Architecture of Autoencoder (inspired by Hubens, “Deep Inside: Autoencoders,” Towards Data Science , 2018 ).

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. He has collaborated with the Amazon Machine Learning Solutions Lab in providing clean data for them to work with as well as providing domain knowledge about the data itself.

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Tableau: 9 years a Leader in Gartner Magic Quadrant for Analytics and Business Intelligence Platforms

Tableau

We also reached some incredible milestones with Tableau Prep, our easy-to-use, visual, self-service data prep product. In 2020, we added the ability to write to external databases so you can use clean data anywhere. Tableau Prep can now be used across more use cases and directly in the browser.

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