Advanced Deep Learning
Dates: 6 Dec, 7 Dec 2023 | 9am-5.30pm | Online
Duration: 2 Days
Course Overview
Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders, and Deep Reinforcement Learning, are creating impressive results in a series of traditional and creative applications. For learners with basic deep learning and Python programming knowledge, it is critical to complete their knowledge towards implementation of deep learning systems in the real-world ecosystems, as well as to have more information about advanced deep learning models, how they work, what are their advantages, and applications. Again, since the implementation of robust natural language processors is one of the abilities of deep neural networks, and Natural Language Processing (NLP) has got many applications in a diverse set of businesses, practicing that application of deep learning is advisable and rational.
This course is part of Professional Certificate in Text Processing.
Course Objectives
At the completion of the course, the participants will be able to:
- Articulate advanced deep learning models, their strengths and constraints, and their applications.
- Cultivate advanced deep learning systems in different businesses.
- Have basic skills in using deep learning algorithms to implement simple Natural Learning Processing (NLP) systems.
Who Should Attend
ICT executives, managers, engineers and analysts.
Prerequisites
Basic deep learning knowledge, and basic Python programming skills. Click to view the course detail of Python Programming.
Course Convener
(Click photo to view biography)
Assoc Prof Xavier Bresson
Assoc Prof Xavier Bresson
A/Prof Xavier Bresson is an international leader in the field of deep learning. Particularly, he co-pioneered a new machine learning technology called graph neural networks (GNN), which combines graph theory and neural network techniques to tackle complex data domains. He has organised several international conferences and tutorials on graph deep learning such as the recent UCLA’21 workshop on “Deep Learning and Combinatorial Optimization”, the MLSys’21 workshop on “Graph Neural Networks and Systems”, the UCLA’19 workshop on “New Deep Learning Techniques”, and the NeurIPS’17, CVPR’17 and SIAM’18 tutorials on “Geometric Deep Learning on Graphs and Manifolds”. He has been a speaker at the top machine learning conferences KDD’21, AAAI’21, ICLR’20 and ICML’20. In 2002, he co-developed with Turing award winner Yoshua Bengio a new class of expressive GNN. He has published more than 70 peer-reviewed articles, which have been cited 13,500+.
A/Prof Bresson has been the main lecturer of undergraduate deep learning courses since 2015 at EPFL, NTU and NUS, and has received outstanding evaluations for his teaching material and engaging style. He has also taught graduate courses on advanced deep learning at NUS and was a guest lecturer for Turing award winner Yann LeCun’s course at NYU. He has provided industrial short courses on AI and Deep Learning to Fortune 500 companies s.a. Deloitte, UnitedHealth and university alumni training centres at UCLA, EPFL, and NTU.
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Codes
TGS-2022012788 (Classroom Learning)
TGS- 2022012752 (Synchronous e-learning)
Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older
