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.

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The course:

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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
Assumed knowledge:
Basic programming knowledge in a structured programming language.
Schedule:
1 day (9:00 AM - 5:00 PM )
Education Partner:
Sprinx Systems
Language: