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Do you know how easy it can be to apply Deep Learning with just a few lines of MATLAB code?

Yes, you can apply deep learning techniques to your work, from standard architectures for image and signal processing to advanced neural networks for a wide range of tasks. Join our webinar and workshop to learn how automation can help you label your data more efficiently, and how optimization techniques for hyperparameters can be used to maximize the performance of your networks.

Learn from our engineers and guest speakers from MathWorks who will share how companies worldwide are applying MATLAB and deep learning techniques to their projects in various industries.

Session #1: Deep Learning Overview

20 January 2022 | 15:00 - 16:00 (GMT+8) | Via WebEx
20 January 2022 | 14:00 - 15:00 (GMT+7) | Via WebEx
 

Overview

Deep Learning is a key technology driving the current Artificial Intelligence (AI) megatrend. You may have heard of some mainstream applications of deep learning, but how many of them would you consider to your engineering and science applications? MathWorks developers have purposely built MATLAB's deep learning functionality for engineering and science workflows. We understand that success goes beyond just developing a deep learning model. Ultimately, models need to be incorporated into an entire system design workflow to deliver a product or a service to the market.

Highlights:

  • Introduction and Invite for the remaining sessions + Workshop

  • Deep learning applications in engineering and science

  • Workflow for researching, developing & deploying your deep learning application

  • How to get started with deep learning in MATLAB

 

Session #2: Automated and Iterative Labeling for Images and Signals by Rishu Gupta, Senior Application Engineer, MathWorks Inc.

17 February 2022 | Timing to be Advised | Via WebEx
17 February 2022 | Timing to be Advised | Via WebEx
 

Overview

In this session, we will be introducing the signal, image and video labelers and discuss the ways on how to extend these tools to facilitate labeling imagery to build AI models. We will be describing the use of preprocessing to facilitate the extraction of information from images, and present approaches to building models in an iterative fashion, validating predicted labels and incorporating on-the-fly models to label more and more data. In addition, we will discuss an approach to automating pixel-level labeling for semantic segmentation workflows.

Highlights:

  • Using and Extending the Signal, Image and Video Labelers

  • Preprocessing to facilitate image labeling

  • Iteratively building and incorporating computer-vision and machine-learning models

  • Automating pixel-level labeling

 

Session #3: Deep Dive - Designing Experiments with AWS and Azure

24 February 2022 | 15:00 - 16:00 (GMT+8) | Via WebEx
24 February 2022 | 14:00 - 15:00 (GMT+7) | Via WebEx

 

Overview

Take a deeper dive into designing, training, and tuning deep learning models. Learn how MATLAB® deep learning apps can help you edit neural networks and devise and run experiments. See a brief teaser on how to customize deep learning training to handle more advanced types of neural networks.

You’ll be able to:

  • Set up your AWS Cloud and Azure Cloud
  • Use and Leverage cloud compute resources to speed up your training
  • Compare results of on laptop vs. on cloud resources

Highlights:

  • How to setup your MATLAB to Connect with AWS or Azure
  • Using the Deep Network Designer app to graphically create, edit, and train models
  • Tracking and running modeling runs with the Experiment Manager app for rapid, automated iteration
  • Introducing the extended deep learning framework to customize and train advanced neural networks

 

Session #4: Deploying your Deep Learning Models to Production

10 March 2022 | 15:00 - 16:00 (GMT+8) | Via WebEx
10 March 2022 | 14:00 - 15:00 (GMT+7) | Via WebEx
 

Overview

You’ve developed your algorithm, trained your deep learning model, and optimized it for the best performance possible. What’s next? In this session, you'll learn how to:

  • Deploy on Embedded GPUs
  • Deploy on to Website (MATLAB Production Server)
  • Deploy to a Desktop
  • Deploy on MCU

 

Session #5: Deep Learning Hands-On Workshop (For New Customer Only)

25 March 2022 | 14:00 - 17:00 (GMT+8) | Via WebEx
25 March 2022 | 13:00 - 16:00 (GMT+7) | Via WebEx
 

Overview

Artificial Intelligence techniques like deep and machine learning are introducing automation to the products we build, processes we develop and the way we do business. You can use these techniques to solve complex problems related to images, signals, text and controls.

In this hands-on workshop, you will write code and use MATLAB Online to:

  • Train deep neural networks on GPUs in the cloud
  • Design deep neural networks using MATLAB’s interactive apps
  • Explore Deep Learning techniques to work with images, time series, and text data

Installation Guide MATLAB Online

We will provide trial licenses to use MATLAB Online [Download Now]. There won’t be any MATLAB installation on desktop; we will be using it from browser for the workshop. However, the trial will also allow users to install MATLAB on their computers. The duration of this Workshop is about 3 hours.

Terms and Conditions:

  • Due to the limited availability of seats, early registration is strongly recommended to ensure your participation.
  • Registration will be confirmed upon your receipt of the confirmation, on a first-come-first-serve basis.
  • Should you wish to cancel your registration, please inform us in writing to events@techsource-asia.com
  • TechSource reserves the right to reschedule or cancel the event due to unforeseen circumstances. Every effort, however, will be made to inform participants of the change.

Save Your Spot!

Our Key Speakers

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Rishu Gupta

Senior Application Engineer

MathWorks Inc (India)

Rishu Gupta is a Senior Application Engineer at MathWorks India. He primarily focuses on image processing, computer vision, and deep learning applications. Rishu has over nine years of experience working on applications related to visual contents. He previously worked as a scientist at LG Soft India in the Research and Development unit.

He has published and reviewed papers in multiple peer-reviewed conferences and journals. Rishu holds a bachelor’s degree in electronics and communication engineering from BIET Jhansi, a master’s in visual contents from Dongseo University, South Korea, working on the application of computer vision, and a Ph.D. in electrical engineering from University Technology Petronas, Malaysia with focus on biomedical image processing for ultrasound images.

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Seow Kok Huei

Senior Application Engineer

Seow Kok Huei is a Senior Application Engineer at TechSource Asia., specializing in the application of data engineering, bioinformatics, Image Processing, Deep Learning, and Machine learning to biological datasets for diagnostics.

He helps customers across different industries on projects such Data Driven Analysis (Biomarkers Identification), Deep Learning and Computer Vision, AI Workflow and others.

He received a Ph.D. in Systems Biology from the Singapore-MIT-Alliance and a B.Eng. in Chemical & Biomolecular Engineering from the National University of Singapore (NUS).

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Nestor Fernandez

Application Engineer

Nestor Fernandez works as an application engineer at TechSource and he focuses on areas of data analytics for applications involving time-series data. Nestor Fernandez has extensive research and industrial experience in developing signal processing and AI workflows.

He also helps companies and agencies augment their technical computation workflows ranging from financial models to physical systems. His field of expertise covers Instrumentation and Condensed Matter Physics.

He received a Master’s Degree in Applied Science from the National University of Kaohsiung, Taiwan after finishing his Bachelor’s Degree in Applied Physics from UP Diliman.

 

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Ian M. Alferez

Application Engineering Manager

Ian M. Alferez is the Application Engineering Manager at TechSource Systems. He specializes in in the field of embedded system (embedded coder configuration), data analytics (Machine Learning) and technical computing with MATLAB/Simulink. He holds a Bachelor of Science in Electronics and Communication Engineering from the University of San Carlos in Cebu, Philippines. Before joining Techsource Asia, he worked as a Software Development Engineer at Lear Corporation where he refined his skills in Model-Based Design with regards to the Verification and Validation Workflow and Embedded Software / Hardware. 

He has built his forte in Process Automation with MATLAB, Production Code Customization, Optimization and Generation with Embedded Coder, MATLAB/Simulink Algorithm for Auto Code Generation and Hardware Target Deployment, Customizing the Auto Test Generation / Property Proving with Simulink Design Verifier.