Prof. Dr. - Ing. Morris Riedel

PRACE Tutorial: Parallel and Scalable Machine Learning

Invited Tutorial
PRACE Advanced Training Center, Juelich Supercomputing Centre, Germany
2019-02-25 – 2019-02-27
[ EVENT ]


Parallel and Scalable Machine Learning Morris Riedel

Lecture 1 – Parallel and Scalable Machine Learning driven by HPC (pdf, ~9,97 MB)

Lecture 2 – Introduction to Machine Learning Fundamentals – Theory (pdf, ~12,9 MB)

Lecture 3 – Introduction to Machine Learning Fundamentals – Practice (Jupyter Notebooks – request resources contacting m.riedel@morrisriedel.de)

[Martin Schultz] Lecture 4 – Feed Forward Neural Networks (Jupyter Notebooks – request resources contacting m.riedel@morrisriedel.de)

[Martin Schultz] Lecture 5 – Feed Forward Neural Networks (Jupyter Notebooks – request resources contacting m.riedel@morrisriedel.de)

Lecture 6 – Validation and Regularization – Theory (pdf, ~6,56 MB)

Lecture 7 – Regularization and Validation – Practice (Jupyter Notebooks – request resources contacting m.riedel@morrisriedel.de)

[Gabriele Cavallaro] Lecture 8 – Data Preparation and Performance Evaluation – Theory (pdf, ~2,45 MB)

[Gabriele Cavallaro] Lecture 9 – Data Preparation and Performance Evaluation – Practice (pdf, ~0,97 MB)

Lecture 10 – Theory of Generalization (pdf, ~4,00 MB)

[Gabriele Cavallaro] Lecture 11 – Unsupervised Clustering and Applications – Theory (pdf, ~2,62 MB)

[Gabriele Cavallaro] Lecture 12 – Unsupervised Clustering and Applications – Practice (pdf, ~2,24 MB)

[Jenia Jitsev] Lecture 13 – Deep Learning Introduction (pdf, ~31,6 MB)


PRACE Tutorial Parallel and Scalable Machine Learning Morris Riedel Group

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