This intermediate-level workshop introduces how to implement and integrate the latest deep learning algorithm into your research by using the HPC cluster. The workshop is divided into three sections. We will first cover the principles of deep learning models and concepts including neural network and convolutional neural network (CNN). Some coding examples will be given under the algorithm platform PyTorch supported by FAIR. The second part of this workshop will cover a case study about how to modify and implement a deep learning based calibration module to fit the research aim. In this last section, we will go through a few best practices on how to use the high-performance computing (HPC) cluster to request GPUs and accelerate the training speed of the deep learning model.
Prerequisite: Suggested learnings : Introductory Machine Learning, Unix Shell and Command Line Interface, HPC and Python
What to bring:
Bring your own laptop. The only required software is a web browser