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[ONLINE] Introduction to Machine and Deep Learning (PTC course)
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Europe/Prague
[ONLINE]
[ONLINE]
Description
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In this course we provide an overview to end-to-end deep learning with the latest version of Tensorflow/Keras. It covers the basic concepts to define models with Keras and data pipelines with Tensorflow’s „Dataset“, and to visualize the results with Tensorboard while training. If training on one node or GPU is not enough, we show how to scale up/out distributed training onto multiple compute nodes and GPUs with Horovod. Furthermore, we provide an introduction to scikit-learn, with an overview of different machine learning algorithms it provides and how to utilize it on GPUs with H2O4GPU. The training courses consist of a hands-on exercises to be run directly on IT4I's infrastructure.
Level
beginner
Language
English
Prerequisites
Understanding of fundamental programming concepts in Python such as functions, loops, dictionaries, and arrays.
About the tutor
Georg Zitzlsberger is a research specialist for Machine and Deep Learning at IT4Innovations. He has for over three years been certified by NVIDIA as a University Ambassador of the NVIDIA Deep Learning Institute (DLI) programme. This certification allows him to offer NVIDIA DLI courses to academic users of IT4Innovations' HPC services. In addition, in collaboration with Bayncore, he is a trainer for Intel HPC and AI workshops and conferences carried out across Europe. He has been contributing to these events, which are held for audiences from industry and academia, for five years.
Acknowledgements
This event was partially supported by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project "e-Infrastruktura CZ – LM2018140“ and partially by the PRACE-6IP project - the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 823767.
This course is supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140).
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