IEEE Joint Tutorial/Workshop on Deep Learning

2-Days Tutorial
Fri-Sat, 5-6 February 2021
Hybrid: Onsite and Virtual modes
basic theory, some basic structures/models and the training procedure of deep leaning, for applications such as classification, de-noising and others
Optional Labs
2-Days Workshop
Fri-Sat, 5-6 March 2021
for Scientific applications: Robotic vision, super-resolution imaging, object recognition, video analytic, speech recognition, traffic control, autonomous vehicle, machine translation, medical imaging/diagnosis, smart city,
for Commercial applications
Call-for-Paper
Fri-Sat, 5-6 March 2021
provide audience with knowledge on the way to apply AI to the subject area
nomination of speakers and self-nominations are both welcome
Deadline: Mon 21 Dec 2020
Announcement of Speaker Acceptance: 30 Dec 2020

2-Days Tutorial on Deep Learning

Date: Friday-Saturday, 5-6 February 2021
Venue: Hybrid: onsite and virtual modes (details to be announced)
Organized and Sponsored by: IEEE Hong Kong Section, IEEE Hong Kong Life-Member Affinity Group, Caritas Institute of Higher Education, Hong Kong Polytechnic University
Co-Sponsors and Technical Co-Sponsors: Asia Pacific Association on Signal and Information Processing (APSIPA), TeleEye Founders’ Charity Foundation Ltd
Organizers: Prof. H. Anthony Chan (FIEEE) and Dr. Paulina Chan (IEEE Hong Kong Section)
Chief Speaker: Prof. Wan-Chi Siu, PhD, DIC, Life-FIEEE
Speakers: Dr. Yingchao Zhao, Dr. Tina, Xueting Liu, Dr. Zhisong Liu
Tutors for Lab: Dr. LIU ZhiSong
Dr. LI ChengZe
Artificial Intelligence May take additional AI courses to enhance professional competence globally for now and in future jobs
Course Fee: HK$1,000 (this is a reduced fee, and the course is subsidized by the sponsors)
HK$ 800 for IEEE and/or APSIPA members
Registration: Please register online
Contact: Tel: +852 3653 6700, Email: caps@cihe.edu.hk
Enquiries: Ms Daisy Kwok, dkwok@cihe.edu.hk , CIHE of the CAPS office at 3653 6700
file Download this announcement

Hong Kong is aspiring to become one of the smart cities in the world. An indispensable part of this development relies on the smart use of computer and information technology (CIT). In the recent years, deep learning in machine learning has achieved drastic achievements for segmentation, pattern recognition, classification, forecasting, .. which are extremely useful for applications such as robotic, imaging, video analytic, surveillance, autonomous vehicle, medical diagnosis, DNA identification, big data, business analysis, finance forecasting, etc. This progress relies on the great advancement of computer architecture and graphic cards which allow heavy learning and the development of various deep learning architectures and system structures. This is a short course on Continuing Education, for which we assume the attendees have some basic background knowledge of computer and linear systems. Upon completion of this course, attendees should be able to understand the basic theory behind and enhance their capability on some basic structures/models and the training procedure of deep leaning, for applications such as classification, de-noising and others. Contents of this tutorial include the following.

  1. Learning Approach with Black Box Learning tools
  2. Review of Neural Networks (NN), and Back Propagation Model, etc., with numerical examples
  3. Deep learning with Autoencoder Network
  4. CNN: Convolutional Neural Netwrok
  5. LeNet and other successful CNN networks
  6. AlexNet for object classification
  7. Back Projection and Residual Network (ResNet)
  8. 1x1 convolution and Inception Network
  9. GAN (Generative Adversarial Network) against Conventional Discriminative model
  10. Experiment(s) - Optional (to be arranged outside the lecture sessions)

Optional Labs: A set of graded experimental exercises is available, which (i) starts with input and output formats of a Deep Learning model as a Black Box Learning Tool, and allows attendees to produce results, demonstrations and plotting, (ii) guides attendees going through the training procedure of deep learning for object classifications, (iii) shows details of image denoising with deep learning, etc. For those who finish these experiments should be able to (i) start or enhance their research or (ii) perform their engineering work making use of basic Deep Learning techniques.

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2-Days Workshop on Deep Learning

Call-for-Speakers

Date: Friday-Saturday, 5-6 March 2021
Venue: Hybrid: onsite and virtual modes (details to be announced)
Organized and Sponsored by: IEEE Hong Kong Section, IEEE Hong Kong Life-Member Affinity Group, Caritas Institute of Higher Education, Hong Kong Polytechnic University
Co-Sponsors and Technical Co-Sponsors: Asia Pacific Association on Signal and Information Processing (APSIPA), TeleEye Founders’ Charity Foundation Ltd
Organizers: Prof. Wan-Chi Siu (PhD, Life-FIEEE), Prof. H. Anthony Chan (PhD, FIEEE), Dr. Paulina Chan (PhD, SrMIEEE, Chair, IEEE HK Section)
Deadline: Monday, 21 December 2020
Announcement of Speaker Acceptance: Wednesday, 30 December 2020
Enquiries: Ms Daisy Kwok, dkwok@cihe.edu.hk , CIHE of the CAPS office at 3653 6700
file Download this Call-for-Paper with Nomination form.

Hong Kong is aspiring to become one of the smart cities in the world. An indispensable part of this development relies on the smart use of computer and information technology (CIT). In the recent years, machine learning with deep learning has achieved drastic achievements for segmentation, pattern recognition, classification, forecasting, .. which are extremely useful for applications such as robotic, imaging, video analytic, surveillance, autonomous vehicle, medical diagnosis, DNA identification, big data, business analysis, finance forecasting, etc. This progress relies on the great advancement of computer architecture and graphic cards which allow heavy learning and the development of various deep learning architectures and system structures. This is a Workshop on Continuing Education. Its objective is to refresh/update attendees' knowledge on machine learning, in particular deep learning. The attendees are assumed to have some basic knowledge on machine learning, and the background in Electronic and Electrical Engineering, Information, Computer Science, System Engineering, Data Science, Science or Medical Science.

Call for Speakers: This announcement is to look for Nomination of Speakers for this workshop. Self-nominations are also much welcome. Speakers must be knowledgeable in the area, with good presentation techniques and at high professional level. In order to appreciate their contribution and compensate partly their time for the preparation of this presentation, a nominal amount of honorarium will be provided to each speaker. Note that the presentation should neither be just a literature review of the past works, nor a review of the past works of the speaker, but it should provide the audience with knowledge on the way to apply AI to the subject area. Answering the following questions may help proposing a presentation:

  1. Why Deep Leaning is useful in the subject area? How? Advantage?
  2. How can one start working in this direction?
  3. What is the common/existing practice using AI (especially Deep Learning) in this area?
  4. Give typical example(s) on the realization structure, network/package to be used (e.g. Alexnet, or, ResNet, or ..), data format, preparation of training samples, testing, etc.

Possible Subject Areas but not limited to - Scientific applications: Deep Learning for Robotic vision, super-resolution imaging, object recognition, video analytic, speech recognition, traffic control, autonomous vehicle, machine translation, medical imaging/diagnosis, smart city, ...; Commercial Application: Deep Learning for handling big data, banking, stock market analysis, risk analysis, regulatory compliance, Patent search, .. etc.

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Address

2 Chui Ling Lane, Tseung Kwan O, N.T., HK

Direction by subway station

Take subway (or bus) to Tiu Keng Leng subway station 調景嶺地鐵站.
Take exit B to enter into shopping mall.
Take escalator to go up and walk inside the mall following the sign towards: Shin Ming Estate 善明邨。
The "Shin Ming Estate" sign will lead you go up another escalator and then exit from the mall.
CIHE is the first building on the left after you exit from the mall.

The offices of the academic staff in the School of Computing and Information Sciences are on the A8 level.

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