SYNOPSIS   

This hands-on workshop introduces participants to the fundamentals of deep learning through the development of a handwritten digit classification system using the widely adopted MNIST dataset. While open to all learners, it is intentionally designed to be inclusive and supportive of women engineering learners, fostering confidence and engagement in AI and machine learning. Participants will explore the complete deep learning workflow, from data preparation and visualization to model design, training, and evaluation.

The workshop will cover:

•    Understanding and exploring the MNIST handwritten digit dataset.
•    Designing a Multi-Layer Perceptron (MLP) neural network architecture.
•    Applying regularization techniques to improve model generalization.
•    Configuring optimizers and key training hyperparameters.
•    Training model and interpreting learning and loss curves.
•    Evaluating model performance on unseen test data.
•    Understanding common challenges and best practices in deep learning model development.

By the end of the workshop, participants will have gained practical experience in building, training, and evaluating a deep learning model, providing a solid foundation for further exploration of artificial intelligence applications.


BIODATA OF SPEAKER

Ir. Dr. Lim Lam Ghai received the Bachelor of Engineering (B.Eng.), Master of Science (M.Sc.), and Doctor of Philosophy (Ph.D.) degrees in Electrical and Electronic Engineering from Universiti Teknologi PETRONAS, Malaysia, in 2014, 2017, and 2021, respectively. He is currently a Lecturer at Monash University Malaysia. His research interests span machine learning, artificial intelligence, neuroimaging, and precision agriculture, with a particular focus on developing data-driven solutions for engineering applications.