NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning.
Learn how to train and deploy a neural network to solve real-world problems, how to generate effective descriptions of content within images and video clips, how to effectively parallelize training of deep neural networks on Multi-GPUs and how to accelerate your applications with CUDA C/C++ and OpenACC.
This 4-days workshop combines lectures about fundamentals of Deep Learning for Multiple Data Types and Multi-GPUs with lectures about Accelerated Computing with CUDA C/C++ and OpenACC.
The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.
Prerequisites and content level
Please note, that the workshop is exclusively for verifiable students, staff, and researchers from any academic institution (for industrial participants, please contact NVIDIA for industrial specific training).
Technical background, basic understanding of machine learning concepts, basic C/C++ or Fortran programming skills. In addition, basics in Python will be helpful. Since Python 2.7 is used, the following tutorial can be used to learn the syntax: docs.python.org/2.7/tutorial/index.html.
For the 1st day familiarity with TensorFlow will be a plus as all the hands-on sessions are using TensorFlow. For those who do not program in TensorFlow, please go over TensorFlow tutorial (especially the "Learn and use ML" section): www.tensorflow.org/tutorials/.
The content level of the course is broken down as: beginner's - 5,2 h (20%), intermediate - 14,3 h (55%), advanced - 6,5 h (25%), community-targeted content - 0,0 h (0%).
Lecturers: Dr. Momme Allalen, Dr. Juan Durillo Barrionuevo, Dr. Volker Weinberg (LRZ and NVIDIA University Ambassadors), Georg Zitzlsberger (IT4Innovations and NVIDIA University Ambassador)
Price: Free of charge (4 training days)
For further detailed information, agenda and registration please visit:
REGISTRATION is OBLIGATORY since the details to access the online course will be provided to the registered and accepted attendees only. If you have registered to this course and you are not able to attend, please CANCEL your registration in advance by sending an email to firstname.lastname@example.org