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Episode 54: Reproducible machine learning

Episode 54: Reproducible machine learning

Tackling the Machine Learning Reproducibility Crisis – KJ Schmidt & Aristana Scourtas on Foundry-ML

Tackling the Machine Learning Reproducibility Crisis – KJ Schmidt & Aristana Scourtas on Foundry-ML

Building reproducible machine learning in production (Ep. 96)

Building reproducible machine learning in production (Ep. 96)

TidyX Episode 54 | MLB Pitch Classification 2 - KNN, Caret, & UMAP

TidyX Episode 54 | MLB Pitch Classification 2 - KNN, Caret, & UMAP

#54 Bayes in Theoretical Ecology, with Florian Hartig

#54 Bayes in Theoretical Ecology, with Florian Hartig

#54 Bayes in Theoretical Ecology, with Florian Hartig

#54 Bayes in Theoretical Ecology, with Florian Hartig

#61: Prof. YANN LECUN: Interpolation, Extrapolation and Linearisation (w/ Dr. Randall Balestriero)

#61: Prof. YANN LECUN: Interpolation, Extrapolation and Linearisation (w/ Dr. Randall Balestriero)

Using Notebooks As The Unifying Layer For Data Roles At Netflix with Matthew Seal - Episode 54

Using Notebooks As The Unifying Layer For Data Roles At Netflix with Matthew Seal - Episode 54

Data Developer Relations - Hugo Bowne-Anderson

Data Developer Relations - Hugo Bowne-Anderson

Mastering Self-Learning in Machine Learning - Aaisha Muhammad

Mastering Self-Learning in Machine Learning - Aaisha Muhammad

GitOps Guide to the Galaxy (Ep 54) | GitOps Cookbook

GitOps Guide to the Galaxy (Ep 54) | GitOps Cookbook

how bioinformaticians make their code reproducible: docs, venvs & containers

how bioinformaticians make their code reproducible: docs, venvs & containers