MATLAB Seminar 

Engineering Data Analytics 

(Deep Learning)

Wednesday, 29th May, 2019
Level 4, Denso / TI Room SHRDC Shah Alam, Selangor 

TechSource Asia collaborate with

It is a great honour that TechSource Systems is invited by Selangor Human Resource Development Corporation  to be a host of technical sharing session on " Engineering Data Analytic-Deep Learning". We aim to have an interactive session where the engineer/researchers will be able to pick up some tips and hear the concerns.

Who Should Attend

We welcome all new user Systems Designers, Software Engineers, R&D Engineers, RF Engineer, DSP Software Engineers, Technical Directors, Test Engineers, Embedded Software Engineers, FPGA Design Engineers, Algorithm Designers, Researchers.



Dr. David Chong

Dr. David Chong Jin Hui is Principal Application Engineer at TechSource Systems.
He specializes in communication, signal processing and application deployment with MATLAB. Prior to joining TechSource Systems, he worked at MIMOS as staff research & developer, at Tunku Abdul Rahman University College as Senior Lecturer & at Intel as Senior Component Engineer.
Dr. David Chong holds BEng of Computer & Communication System Engineering and PhD in wireless communication from University Putra Malaysia.

Principal Application Engineer, TechSource Systems

Topic Outline 

Topic 1: What's New in MATLAB & Simulink

Learn about new capabilities in the MATLAB® and Simulink® product families to support your research, design, and development workflows. This talk highlights new releases and features for deep learning, signal processing, automotive, stateflow chart and so on to help you improve engineering design and decision-making processes for multiple industries. You will see how new MATLAB toolbox and apps simplify the process of modeling, end to end simulation, design validation & verification and SoC hardware implementation.

Topic 2 : Scaling Data Analytics to Cloud using MATLAB

Deep learning can achieve state-of-the-art accuracy in many humanlike tasks such as naming objects in a scene or recognizing optimal paths in an environment.


The main tasks are to assemble large data sets, create a neural network, to train, visualize, and evaluate different models, using specialized hardware - often requiring unique programming knowledge. These tasks are frequently even more challenging because of the complex theory behind them.


In this seminar, we’ll demonstrate new MATLAB features that simplify these tasks and eliminate the low-level programming. In doing so, we’ll decipher practical knowledge of the domain of deep learning.  We’ll build and train neural networks that recognize handwriting, classify food in a scene, and figure out the drivable area in a city environment. 


Along the way, you’ll see MATLAB features that make it easy to 

  • Manage extremely large sets of images

  • Visualize networks and gain insight into the black box nature of deep networks

  • Perform classification and pixel-level semantic segmentation on images

  • Import training data sets from networks such as GoogLeNet and ResNet

  • Import and use pre-trained models from TensorFlow and Caffe

  • Speed up network training with parallel computing on a cluster

  • Automate manual effort required to label ground truth

Discover Malaysian Smart Factory and Big Data Analytics training programs.

Event Program


8.45 am - 9.00 am - Arrival of Guest, Registration & Provision of Vouchers

9:00am – 9.30 am - Welcome  Introductory remarks by emcee

9.30 am - 10:30 am - Topic 1 : What’s New in MATLAB & Simulink 

10:30am - 11:45 am - Topic 2: Deep Learning with MATLAB

11:45 am -12:15pm - Presentation ends, followed by Q&A


12.15pm-12.30pm - Overview of MSF & MSF Programm / Optional visit to MSF Lab


12.30  - Event ends


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