Skip Ribbon Commands
Skip to main content

Course Detail

Neural Networks and Deep Learning


Artificial neural networks are models of reasoning based on human brain functioning and have been successful in many real-world applications including pattern classification, regression, forecasting, etc. The course will introduce models, learning, implementations and applications of neural networks and deep learning.


Date(s): 6 to 8 January 2020
Time: 9:00AM to 5:00PM
Venue: NTU@one-north campus, Executive Centre (Buona Vista)
Closing Date:
23 December 2019
Course Fee: Standard: S$1123.50   View more
Registration fees inclusive of:
  • Complimentary Lunch
  • Light refreshments
  • Course materials
  • Prevailing GST


To equip participants with the basic concepts and methodologies of neural networks and deep learning systems. In particular, this course covers the information processing techniques inspired by the workings of biological neural networks, which provides solution to interrogatives that current linear systems are not able to resolve. Basic neuron models, neural layers, feedforward networks, convolutional neural networks, autoencoders, and recurrent neural networks will be covered in the course.

Students will be given hands-on experience in building neural network models, using Python and Tensorflow libraries. After taking this course, from shallow to deep neural networks, students will be able to design and select suitable neural network model for solving real world applications and perform required simulations and implementations.


Day 1

1. Introduction to Neural Networks

2. Pattern Recognition

3. Regression

4. Implementing Neural Networks, using Python and Theano


Day 2

5. Neural Layers

6. Feedforward Neural Networks

7. Model Selection and Overfitting


Day 3

8. Convolutional Neural Networks

9. Recurrent Neural Networks

10. Gated RNN

11. Autoencoders

Read more


Rajapakse, Jagath C

Jagath Rajapakse is Professor of Computer Engineering at the School of Computer Science and Engieering at the Nanyang Technological University (NTU), Singapore. He obtained Ph.D. degrees in electrical and computer engineering from the University of Buffalo. He was Visiting Professor of Biological Engineering at the Massachusetts Institute of Technology, Visiting Scientist at the Max-Plank Institute of Cognitive and Brain Sciences (Leipzig), and Visiting Fellow at the National Institute of Health (USA). His current research interests are in brain imaging, and computational and systems biology. Professor Rajapakse has published over 300 research papers in scientific journals and conferences. He served as Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Transactions on Computational Biology and Bioinformatics, and the IEEE Transactions on Medical Imaging. He is a Fellow of Institute of Electrical and Electronics Engineering (IEEE).

Technicians, engineers, data modelers, and computational scientists who are interested in developing neural network models to solve computational problems, including pattern/object recognition, regression, prediction, forecasting, etc. Knowledge of linear algebra, calculus, basic programming skills, and Python would be useful.

Standard Course Fee S$1123.50
Course fee payable after SSG funding, if eligible under various schemes

Cat-A SSG Funded Courses1


Enhanced Training Support for SMEs (ETSS) 2


Mid-Career Enhanced Subsidy (MCES)3


Workfare Training Support (WTS)4

Standard Course Fee $1123.50
Course fee payable after funding or subsidy, if eligible under various schemes
Course fee payable after Discount^

Group of 3 pax and above


NTUC Member

- All fees stated are inclusive of 7% GST
- SkillsFuture Credit can be used to offset course fees payable.
With effect from 1 April 2020, eligible Singaporeans can start using their one-off SkillsFuture Credit top-up (up to S$500 credit), claimable for full range of SkillsFuture Credit-eligible courses offered by PaCE@NTU. For more information, please visit
1 Cat-A SSG Funded Courses - Eligible Singapore Citizens and PRs may enjoy up to 70% of the course fee. For more information, visit
Enhanced Training Support for SMEs (ETSS) - SME-sponsored employees (Singapore Citizens and PRs) may enjoy subsidies up to 90% of the course fee.
For more information, visit
3 Mid-Career Enhanced Subsidy (MCES) - Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fee.
For more information, visit

4 Workfare Training Support (WTS) - Singaporeans aged 35 and above (13 years and above for persons with disabilities) and earn not more than S$2,000 per month, may enjoy subsidies up to 95% of the course fee. For more information, visit
- The NTUC Training Fund (SEPs) is applicable for courses under the SkillsFuture Series courses. For more information, visit

^ Discount cannot be used in conjunction with other SSG funding scheme or NTU Alumni Course Credit. Participants are eligible for only ONE of the discount schemes.
Not sure which programme to go for? Use our programme finder
Loading header/footer ...