{"id":843,"date":"2020-01-13T10:31:42","date_gmt":"2020-01-13T09:31:42","guid":{"rendered":"https:\/\/www.uni.lu\/snt-fr\/events\/nvidia-workshop-fundamentals-of-deep-learning-for-multi-gpus\/"},"modified":"2020-01-13T10:31:42","modified_gmt":"2020-01-13T09:31:42","slug":"nvidia-workshop-fundamentals-of-deep-learning-for-multi-gpus","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/snt-fr\/events\/nvidia-workshop-fundamentals-of-deep-learning-for-multi-gpus\/","title":{"rendered":"NVIDIA Workshop: Fundamentals of Deep Learning for Multi-GPUs"},"content":{"rendered":"

Booking required!<\/p>

Please register on Eventbrite<\/a>.\u00a0<\/p>

Today the computational requirements of deep neural networks that enable complex AI applications like autonomous driving, language translation, and speech synthesis are enormous. A single training cycle can take weeks on a single GPU or even years for the larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.<\/p>

At the University of Luxembourg, we have access to state-of-the-art HPC systems perfectly suitable for the largest Deep Learning Training sessions. This course will give you all the necessary tools to utilize that power using just Python and the framework of choice between TensorFlow, PyTorch, Caffe2, MXNET, CNTK.<\/p>

This course will teach you how to use multiple GPUs to training neural networks. You’ll learn:<\/p>