Inverted CERN School of Computing 2019

Lectures and exercises

• Daniel Hugo Campora Perez (CERN + University of Seville)
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    A practical approach to Convolutional Neural Networks (lecture)

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    A practical approach to Convolutional Neural Networks (exercise 1)

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    A practical approach to Convolutional Neural Networks (exercise 2)

• Patrick Emonts (Max Planck Institute of Quantum Optics)
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    Tensor Networks - Introduction and Matrix Product States (lecture 1)

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    Tensor Networks - The iTEBD Algorithm (lecture 2)

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    Tensor Networks - Singular Value Decomposition (exercise 1)

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    Tensor Networks - Application of the iTEBD Algorithm (exercise 2)

• Frederic Hemmer (CERN)
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    A word from the IT Department Head

• Giorgio Lopez (CERN)
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    Hardware Acceleration Through FPGAs - Basic Concepts (lecture 1)

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    Hardware Acceleration Through FPGAs - Basics of VHDL (lecture 2)

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    Hardware Acceleration Through FPGAs - First Experiments in VHDL (exercise 1)

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    Hardware Acceleration Through FPGAs - Easy DSP Applications (exercise 2)

• Sebastian Lopienski (CERN)
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    Introduction to the Inverted CSC

• Vaggelis Motesnitsalis (CERN)
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    Big Data Technologies and Physics Analysis with Apache Spark (lecture 1)

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    Big Data Technologies and Physics Analysis with Apache Spark (lecture 2)

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    Big Data Technologies and Physics Analysis with Apache Spark (exercise 1)

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    Big Data Technologies and Physics Analysis with Apache Spark (exercise 2)

• Dmitrii Neverov (Nagoya University)
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    Global track finding algorithms

• Riccardo Poggi (University of Geneva)
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    How container orchestration can strengthen your micro-services: the approach of Kubernetes (lecture)

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    How container orchestration can strengthen your micro-services: the approach of Kubernetes (exercise 1)

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    How container orchestration can strengthen your micro-services: the approach of Kubernetes (exercise 2)

• Alexander Ruede (CERN + KIT-IPE)
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    A Scientist's Guide to FPGAs

• Mikhail Sizov (Budker Institute of Nuclear Physics)
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    Efficient C++ implementation of custom FEM kernel with Eigen