Discover the key technology behind driverless cars and voice control in Siri and Bixby and other hands-free consumer products in this exciting two-day course!
You will learn the fundamentals of Deep Learning with Python, and gain a deeper understanding on the various techniques of how you can teach computers to recognise different objects and patterns using large neural networks. As part of the workshop, you will also get a first-hand experience to build four different projects using deep learning architectures in Keras, a high-level neural network API that enables fast experimentation.
Please bring along your own laptop for the hands-on exercises. Our trainer will share the installation files for Python as well as the necessary source code editor during class. Additional libraries will also be installed during the workshop.
Who should attend?
This course is designed for participants who has a basic understanding of Python and individuals who wish to build deep neural network to create smart applications with machine learning.
Upon completion of this course, you will gain a deeper understanding and knowledge in the following topics:
- Python virtual environments
- Building blocks of deep learning
- Getting datasets for your deep learning projects
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative adversarial Networks
Prior programming knowledge and basic understanding of Python.
Participants will be awarded the Certificate of Attendance by Republic Polytechnic upon meeting 80% of the attendance.
|Full Course Fee (Others)
||Singapore Citizens aged 40 & above
||SME-employed Singapore Citizens & SPR
||Singapore Citizens aged below 40 OR Singapore PR
*Payment may be made using SkillsFuture Credit.
*Fees reflected are inclusive of GST (Goods & Service Tax).
*From 1 July 2020, Workfare Training Scheme (WTS) and fee subsidy under WTS will cease. WTS will be replaced by Workfare Skills Support Scheme (WSS). For more information on WSS, please refer to
Republic Polytechnic reserves the right to make changes to the course fee and application closing dates without prior notice. The commencement of each course is subject to sufficient number of participants.
All information is accurate at time of publishing.