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Master hyperparameter tuning for machine learning models

Data Science Dojo

Learn about top 10 machine learning demos in detail Why is hyperparameter tuning important? This includes data cleaning, data normalization, and feature selection. Hyperparameters control the model’s behavior, and their values are usually set based on domain knowledge or heuristics.

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10 Technical Blogs for Data Scientists to Advance AI/ML Skills

DataRobot Blog

Apache Airflow orchestration provides an easy but powerful solution to integrate DataRobot capabilities into bigger pipelines, combine with other services, clean data, and store or publish the results. Watch a demo. The post 10 Technical Blogs for Data Scientists to Advance AI/ML Skills appeared first on DataRobot AI Cloud.

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

Flipboard

For more information on how to use GluonTS SBP, see the following demo notebook. He has been with the Next Gen Stats team for the last seven years helping to build out the platform from streaming the raw data, building out microservices to process the data, to building API’s that exposes the processed data.

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Evaluation of generative AI techniques for clinical report summarization

AWS Machine Learning Blog

We also see how fine-tuning the model to healthcare-specific data is comparatively better, as demonstrated in part 1 of the blog series. We expect to see significant improvements with increased data at scale, more thoroughly cleaned data, and alignment to human preference through instruction tuning or explicit optimization for preferences.

AI 138
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How Wayfair accelerated product tagging automation with Snorkel Flow

Snorkel AI

Wayfair and Snorkel developed a workflow that incorporated data preprocessing, curation, and iterative development to extract and apply visual data to product labels. Using Snorkel Flow, Wayfair can clean data, remove outliers and duplicates, and quickly prepare training and evaluation datasets with strategic sampling and prompting.

ML 64
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How Wayfair accelerated product tagging automation with Snorkel Flow

Snorkel AI

Wayfair and Snorkel developed a workflow that incorporated data preprocessing, curation, and iterative development to extract and apply visual data to product labels. Using Snorkel Flow, Wayfair can clean data, remove outliers and duplicates, and quickly prepare training and evaluation datasets with strategic sampling and prompting.

ML 59
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Present and future of data cubes: an European EO perspective

Mlearning.ai

How can we all make environmental data more usable, accessible and more relevant? Presentations include demos of functionality and proposals for the future development work, primarily funded by the Horizon Europe programme. For example, vector maps of roads of an area coming from different sources is the raw data.

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