Beginners' content:

  • Deep Learning for Computer Vision
  • Understand general terms and background of deep learning
  • Implement common deep learning workflows such as Image Classification and Object Detection
  • Manipulate training parameters to improve accuracy
  • Modify internal layers of neural networks to adapt to new problems
  • Deploy your networks to start solving real-world problems

Intermediate content:

  • Deep Learning for Multiple Data Types
  • Implementing deep learning workflows like image segmentation and text generation
  • Comparing and contrasting data types, workflows, and frameworks
  • Combining computer vision and natural language processing
  • Modify Tensorflow / Pytorch code using Python 

Advanced content:

  • Deep Learning for Multi-GPUs

  • Approaches to multi-GPUs training

  • Algorithmic and engineering challenges to large-scale training

  • Key techniques used to overcome the challenges mentioned above

  • Deep Learning on HPC clusters



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