Deep Learning Sydney is a meetup group for anybody who has skills or an interest in the hottest area in Machine Learning – deep learning! From data scientists, to researchers, to developers, to companies, to investors, and finally to entrepreneurs. May’s Deep Learning Sydney Meetup was sponsored by Intellify and hosted at AWS.
Pizza and ciders, provided by the sponsors, fed the excitement of the attendees and speakers as the room filled up. Once 100 or so attendees were satisfied and seated, the evening kicked off with a few housekeeping words from the meetup co-founder Andy Zeng and (after some encouragement from the audience) Professor Richard Xu gave an opening speech.
Kale Temple, the co-founder and practice director at Intellify, was introduced. Kale has worked in analytics and data science consulting for more than 5 years and has helped a number of the world’s leading corporate and government organisations to deliver high impact data projects. He is currently an Honorary Affiliate of the University of Sydney’s Business School. Kale gave a short presentation on time-series. Time-series, as a field of study, has largely focused on statistical methods that work well under strict assumptions. Specifically, when there is sufficient history, there is little meta-data and a well-formed auto-correlation structure. However, as an applied practitioner I know that most real-world time series problems violate these assumptions. This leaves us with an opportunity to use more modern time series methods, based on machine learning (and deep learning), to overcome these deficiencies. This session is designed to briefly speak about the unique properties of time-series, how statistical methods work and how and why machine learning (and deep learning) methods can be used to improve accuracy.
Next up was Erica Huang, a first-class graduate from the University of Sydney and a current PhD student at the University of Technology Sydney who is specialising in probabilistic Deep Learning Generation. Erica showed a series of demos using TensorFlow 2.0 which included several examples highlighting the new features of TensorFlow 2.0. Her demos compared and contrasted TensorFlow 2.0 to the features of TensorFlow 1.0.
The evening came to a conclusion with Andy Zeng giving his usual updates about what is new in Deep Learning. Here he updated us on all the new happenings in Deep Learning in the last six months. After the presentations were finished, the attendees and speakers were able to socialise and ask questions about the presentations and the world of Deep Learning in Sydney and beyond. The event was informative as well as entertaining (and very long overdue). Thank you to Intellify for sponsoring the meetup, to AWS for hosting the evening, and the Deep Learning Sydney Meetup Group for enabling people from all industries and places to come together and share a few hours of common interest in one topic: Deep Learning.