Workshop Session Presented by
Achini Adikari and Daswin De Silva and Rashmika Nawaratne, Centre for Data Analytics and Cognition (CDAC), La Trobe University, Australia

Aims and Objectives

Deep learning is gradually becoming a mature artificial intelligence paradigm in both research and practice. Supported by a substantial evidence base, it demonstrates increasing potential for industrial applications in factory automation, energy, manufacturing, transport, communication and human engagement. This workshop aims to develop essential knowledge of deep learning with hands-on exercises in Python, using Google Collaboratory, Jupyter Notebooks and Visual Studio Code. The workshop will begin by exploring the structural elements of deep learning models, hyper-parameters, and comparison to standard machine learning algorithms, followed by the theory and application of deep neural networks (classification), convolutional neural networks (image processing), and recurrent neural networks (time-series prediction). Participants will conduct hands-on experiments of each technique using benchmark and real datasets, for training, testing and evaluation. Each technique will be demonstrated in the context of real-world projects in Industrial Informatics. The learning outcomes of this workshop are; the theoretical foundations of deep learning - when to use and in which settings, the design and development of deep learning models, rapid prototyping, evaluation and deployment using Python.

Important Instructions

Participants will access Google Collaboratory using a Gmail account, a laptop and a stable Internet connection will be essential.

Bio of the Presenters/Organizers

Download the agenda

Click here to download the workshop agenda.

Contact for more details

If you would like to know more about the workshop, please contact, Achini Adikari and Daswin De Silva and Rashmika Nawaratne ( d.desilva@latrobe.edu.au );