Deep Learning on Graphics Processors (CUDA6)

Programming, Programming - other

This course focuses on the subset of machine learning executed on graphics processors. It explains GPU/GPGPU concepts, the CUDA and OpenCL ecosystems, and core deep learning algorithms and libraries, plus hands-on examples and performance considerations.

The course covers workstation configuration, installation, and dependency management for workstation setup, reviews key libraries and computation frameworks, and presents real-world case studies and hands-on labs on practical applications, taught in Czech and English.

THIS TRAINING COURSE WILL HELP YOU:

  • Understand GPU/GPGPU architecture and parallel computing basics
  • Install and configure a workstation for deep learning projects
  • Use CUDA, OpenCL and related frameworks for DL development
  • Work with core deep learning algorithms and common libraries
  • Apply DL solutions with practical examples and case studies

WHO SHOULD ATTEND?

  • Software developers working on ML or high-performance code
  • Data scientists seeking GPU-accelerated training workflows
  • System engineers who prepare DL workstations and clusters
  • Researchers and students interested in practical GPU deep learning

COURSE LOCATION AND AVAILABLE DATES



Public courses are usually delivered in Czech, but this course is also available in English. We can arrange private training for your team online, at your premises or in our classrooms, and tailor the content to your needs.

For groups of around 4 or more participants, private training can already be comparable in price to booking individual places on a public course. Send us your requirements and we’ll recommend the best format and provide an exact quote.

Request training in English

Course content:

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  • Introduction to AI (Artificial Intelligence)
  • AI, deep learning, machine learning and neural networks
  • Support for deep learning
    1. Multiprocessors
    2. GPU / GPGPU
    3. CUDA, OpenCL, OpenACC
    4. GPGPU frameworks
  • Workstation parameters, installation and configuration for DL
  • Deep learning algorithms and libraries
  • Computational software for deep learning
  • Deep learning applications combined with GPU/GPGPU
  • Practical examples, applications and real-world use
  • References
Prerequisites:
Basic programming knowledge in a structured programming language.
Schedule:
1 day (9:00-17:00)
Training Partner:
Sprinx Systems

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