cdac-lab / iecon-tutorial Goto Github PK

Title of Tutorial: Hands-on Deep Learning for Industrial Applications
Date: Monday 17 Oct 2022
Time: 01:00 pm – 02:30 pm CET (GMT+2)

Abstract: Deep learning is a mature AI paradigm in both research and practice. Supported by a substantial evidence base, it demonstrates increasing potential for industrial electronics and industrial informatics applications in factory automation, energy, manufacturing, transport, communication and human interfaces. This workshop aims to develop essential knowledge of deep learning with hands-on exercises in Python, using Google Collaboratory and Jupyter Notebooks. 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 settings. 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.

Requirements: Participants will access Google Collaboratory using a Gmail account. A laptop with an Internet browser and a stable Internet connection is mandatory.

Presented by: Prof Daswin De Silva (SMIEEE) and Sachin Kahawala
Centre for Data Analytics and Cognition (CDAC) La Trobe University, Australia