2019 HPC Course Fall 2019 Course Outline 0

HPC – Course Fall 2019

High Performance Computing – Course Fall 2019 Advanced Scientific Computing 16 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of Engineering and Natural Sciences Faculty of Industrial Engineering, Mechanical Engineering and Computer Science Fall 2019 Lecture 0 – Prologue Slides PDF (9,16 MB) Fully packed room teaching Lecture 0 – Prologue of our High Performance Computing course with ~50 students of @Haskoli_Islands @uni_iceland including Modular Supercomputing by @DEEPprojects @fzj_jsc @fz_juelich @helmholtz_de @uisens @helmholtz_ai slides: https://t.co/kyb7Sq31LX pic.twitter.com/VztJLeAFpH — Morris Riedel (@MorrisRiedel) August 27, 2019 Practical Lecture 0.1 – Short Introduction to UNIX & SSH...

Morris Riedel Teaching 0

Teaching – Overview

Teaching Experience Adjunct Lecturer, Lecturer, Senior Lecturer or Professor University Course: High Performance Computing – Advanced Scientific Computing, REI105M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2019 [ MORE ] University Course: Cloud Computing and Big Data – Parallel and Scalable Machine Learning and Deep Learning, REI504M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2018 [ MORE ] University Course: High Performance Computing – Advanced Scientific Computing, REI105M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2017 [ MORE ] University Course: Cloud Computing and Big Data – Internet-based...

Introduction to Deep Learning Models Morris Riedel 0

Introduction to Deep Learning Models

Introduction to Deep Learning Models Training Course organized by DEEP-EST Project Juelich Supercomputing Centre, Germany 2019-05-21 – 2019-05-23 [ EVENT ] [Morris Riedel] Lecture 1 – Deep Learning driven by HPC & Jupyter (pdf, ~10,7 MB) [Jenia Jitsev] Lecture 2 – Overview of Deep Learning (pdf, ~33,0 MB) [Morris Riedel] Lecture 3 – Neural Network (pdf, ~14,3 MB) [Morris Riedel] Lecture 4 – Convolutional Neural Network (pdf, ~9,83 MB) [Gabriele Cavallaro] Lecture 5 – Introduction to Deep Learning for Remote Sensing & 1D/2D CNNs for Hyperspectral Images Classification (pdf, ~3,86 MB) [Gabriele Cavallaro] Lecture 6 – 3D CNNs for Hyperspectral...

2019-02-25 PRACE Tutorial Parallel and Scalable Machine Learning Morris Riedel 0

PRACE Tutorial – Parallel and Scalable Machine Learning

PRACE Tutorial: Parallel and Scalable Machine Learning Invited Tutorial PRACE Advanced Training Center, Juelich Supercomputing Centre, Germany 2019-02-25 – 2019-02-27 [ EVENT ] 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...

2019-01-21 DEEP EST Tutorial Machine Learning and Modular Supercomputing Content 0

DEEP-EST Tutorial – Machine Learning and Modular Supercomputing

DEEP-EST Tutorial – Machine Learning and Modular Supercomputing HIPEAC Conference 2019 Conference Centre, Valencia, Spain 2019-01-21 [ Event ] Abstract The fast training of traditional machine learning models and more innovative deep learning networks from increasingly growing large quantities of scientific and engineering datasets (aka ‘Big Data‘) requires high performance computing (HPC) on modern supercomputers today. HPC technologies such as those developed within the European DEEP-EST project provide innovative approaches w.r.t. processing, memory, and modular supercomputing usage during training, testing, and validation processes. Materials [ Lecture 1: Modular Supercomputing and Machine Learning – Welcome to HiPEAC Tutorial – Slides ~1.12...

Cloud-Computing-and-Big-Data-Fall-2018 0

Cloud Computing and Big Data – Course Fall 2018

Cloud Computing and Big Data – Course Fall 2018 Parallel & Scalable Machine Learning & Deep Learning 16 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of Engineering and Natural Sciences Faculty of Industrial Engineering, Mechanical Engineering and Computer Science Fall 2018 Lecture 0 – Prologue Slides PDF (9,59 MB) Starting teaching period with Lecture 0 – Prologue of Cloud Computing & Big Data – Parallel & Scalable Machine Learning & Deep Learning Course of @Haskoli_Islands @uni_iceland today mentioning also Modular Supercomputing driven by @DEEPprojects @fzj_jsc @fz_juelich @helmholtz_de pic.twitter.com/52bmhnZNhs — Morris Riedel (@MorrisRiedel)...

ISC2018 Tutorial on Machine Learning and Data Analytics Morris Riedel 0

ISC2018 Tutorial on Machine Learning and Data Analytics

ISC2018 Tutorial on Machine Learning and Data Analytics Invited Tutorial Science, Technology, Engineering, and Mathematics (STEM) Student Day & Gala International Supercomputing Conference (ISC), Frankfurt, Germany 2018-06-27 [ Event ] [ ISC 2018 Tutorial on Machine Learning and Data Analytics – Slides ~15.4 MB (pdf) ] Thanks to the ISC 2018 @ISCHPC team for inviting me to give a HPC Machine Learning & Data Analytics tutorial at the STEM student day; nice discussions with students about modular supercomputing driven by @DEEPprojects, @fzj_jsc & @Haskoli_Islands; slides: https://t.co/xCDitaxS71 pic.twitter.com/pBQ1JXKPJU — Morris Riedel (@MorrisRiedel) June 29, 2018 Meeting many colleagues of our DEEP-EST...

2018-06-06-Introduction-To-Deep-Learning-Tutorial-Content 0

DEEP-EST Tutorial: Introduction to Deep Learning

DEEP-EST Tutorial: Introduction to Deep Learning Tutorial under the umbrella of the DEEP-EST EU Project Juelich Supercomputing Centre, Germany 2018-06-06 – 2018-06-07 [ Event ] Materials [ Lecture 1 – Introduction to Deep Learning – Slides ~3.29 MB (pdf) ] [ Lecture 2 – Fundamentals of Convolutional Neural Networks (CNNs) – Slides ~3.81 MB (pdf) ] [ Lecture 3 – Deep Learning in Remote Sensing: Challenges – Slides ~8.11 MB (pdf) ] [ Lecture 4 – Deep Learning in Remote Sensing: Applications – Slides ~1.98 MB (pdf) ] [ Lecture 5 – Model Selection and Regularization – Slides ~1.78 MB...

2018-03-06-Parallel-and-Scalable-Machine-Learning-Tutorial-Content 0

DEEP-EST Tutorial: Parallel and Scalable Machine Learning

DEEP-EST Tutorial: Parallel and Scalable Machine Learning Tutorial under the umbrella of the DEEP-EST EU Project Juelich Supercomputing Centre, Germany 2018-03-06 – 2018-03-08 [ Event ] Abstract: The course offers basics of analyzing data with machine learning and data mining algorithms in order to understand foundations of learning from large quantities of data. This course is especially oriented towards beginners that have no previous knowledge of machine learning techniques. The course consists of general methods for data analysis in order to understand clustering, classification, and regression. This includes a thorough discussion of test datasets, training datasets, and validation datasets required...

2018 Tutorial Parallel and Scalable Machine Learning 0

Tutorial: Parallel and Scalable Machine Learning

Tutorial: Parallel and Scalable Machine Learning Invited Tutorial PRACE Advanced Training Center, Juelich Supercomputing Centre, Germany 2018-01-15 – 2018-01-17 [ EVENT ] Abstract: The course offers basics of analyzing data with machine learning and data mining algorithms in order to understand foundations of learning from large quantities of data. This course is especially oriented towards beginners that have no previous knowledge of machine learning techniques. The course consists of general methods for data analysis in order to understand clustering, classification, and regression. This includes a thorough discussion of test datasets, training datasets, and validation datasets required to learn from data...