Data Fluency: AI with Deep Learning

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: This is an intermediate level workshop. We will be covering the basic concepts in deep learning, however we will be doing so as a revision rather than a thorough introduction. We will be introducing PyTorch (no prior experience is required), however you should have some basic knowledge in Python programming and the usage of command line interfaces. Prior attendance in the course "Introduction to HPC" would be helpful.

What you'll need: A computer with speakers and a microphone (note: webcams and dual monitors are recommended but not required). A web browser and Zoom are the only required software. A Zoom link and instructions will be sent to registrants 2 days prior to the workshop.

Note: This registration page is only open to Monash external affiliate partners, MASSIVE partners and users, Monash ARDC project partners and users.

Monash staff and Graduate Research students please register through MyDevelopment. 



Data Fluency: AI with Deep Learning


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Contact NameData Fluency for Research
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