Machine Learning Programming Workshop in Tensorflow, Keras and Pytorch
林小姐,電話:2788 5800
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Course Description

This course is intended for participants who aspire to learn machine learning and deep learning for AI digital transformation.

TensorFlow, Keras and PyTorch are among the top three frameworks that are preferred by Data Scientists as well as beginners in the field of Deep Learning. This 1 day course will describe the application of Python programming using Tensorflow, Keras and Pytorch Libraries. Various innovative development approaches of machine learning and deep learning will also be covered.

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Course Outline:

Content (highlights)


  • Dataflow programming across a range of tasks.
  • Machine learning applications using neural networks.
  • Images recognition and pattern analysis for eCommerce.


  • Neural networks on top of Tensorflow or self-contained library.
  • Fast experimentation with deep neural networks.
  • Vision models and text manipulations for business analytics.


  • Natural language processing.
  • Application of regression and classifications.
  • Build intelligent language.

Applications and use cases

  • Python programming examples using the above 3 libraries.
  • Uses cases in Business and daily life.


Date : 10 Apr 2021 (SAT)
Time: 09:30 – 17:00 (Total 6 hours)


Cantonese, supplemented with English technical terms

Who Should Attend

It is highly relevant to participants who involve in the requirements input, design, development, delivery and ultimate use of the digital initiatives including data analyst, programmer, developers, and innovators.


  • Basic Python programming skill
  • Basic Linux skill

Course Fee

HK$3,000  (May up to HK$2,000* subsidy)

*This course is subject to approval under Reindustrialisation and Technology Training  Programme  (RTTP) ,  which offers up to 2/3 course fee reimbursement upon successful applications. For details:

Award of Certificate of Attendance

Participant with full attendance of class will be awarded a Certificate of Attendance issued by the Hong Kong Productivity Council