The 3rd Deep Learning and Artificial Intelligence (DLAI3) Summer/Winter School 2020
29th June – 3rd July 2020, Bangkok, Thailand (Virtually)

Fundamentals of Deep Learning for Computer Vision
Jonathan H. Chan

Associate Professor of Computer Science,
School of Information Technology,
King Mongkut’s University of Technology Thonburi, Thailand
The NVIDIA Deep Learning Institute (DLI) and Data Science and Engineering Laboratory (D-Lab), School ofInformation Technology, King Mongkut’s University of Technology Thonburi (KMUTT) invite you to attend
a hands-on deep learning workshop on Monday, June 29, 2020 from 9:00 am – 5:00 pm on Zoom,exclusively for verifiable academic students, staff, and researchers. This workshop teaches deep learningtechniques for a range of computer vision tasks through a series of hands-on exercises. You will work withwidely-used deep learning tools, frameworks, and workflows to train and deploy neural network models
on a fully-configured, GPU-accelerated workstation in the cloud. After a quick introduction to deeplearning, you will advance to: building and deploying deep learning applications for image classificationand object detection, modifying your neural networks to improve their accuracy and performance, andimplementing the workflow you have learned on a final project. At the end of the workshop, you will haveaccess to additional resources to create new deep learning applications on your own.
Bio: Dr. Jonathan H. Chan is an Associate Professor of Computer Science at the School of Information
Technology (SIT), King Mongkut's University of Technology Thonburi (KMUTT), Thailand. Dr. Chan holds a
B.A.Sc., M.A.Sc., and Ph.D. degree from the University of Toronto. He is the Section Editor of Heliyon
Computer Science (Cell Press – an imprint of Elsevier), an Action Editor of Neural Networks (Elsevier), and amember of the editorial boards of several international publications. Dr. Chan is the VP of Education and aGoverning Board member of the Asia Pacific Neural Network Society (APNNS). In addition, he is a founding
member and the current Chair of the IEEE-CIS Thailand Chapter. Dr. Chan is a senior member of IEEE, ACM,and INNS, and a member of the Professional Engineers of Ontario (PEO). Furthermore, he holds an NVIDIA
Deep Learning Institute (DLI) University Ambassadorship and is a certified DLI instructor. His researchinterests include intelligent systems, biomedical informatics, and data science and machine learning.
Duration:
Assessment type:
Certification:
Prerequisites:

6 hours+Coding
Upon successful completion of the workshop, participants will receive NVIDIA DLICertification to recognize subject matter competency
Familiarity with programming fundamentals such as functions and variables

Learning Objectives
At the conclusion of the workshop, you will have an understanding of the fundamentals of deep learningand be able to:● Implement common deep learning workflows, such as image classification and object detection.
● Experiment with data, training parameters, network structure, and other strategies to increase
performance and capability of neural networks.
● Integrate and deploy neural networks in your own applications to start solving sophisticatedreal-world problems.
DLI+C-FX-01+V2 DSV 4.01

The 3rd Deep Learning and Artificial Intelligence (DLAI3) Summer/Winter School 2020
29th June – 3rd July 2020, Bangkok, Thailand (Virtually)

Fundamentals of Deep Learning for Computer Vision
Workshop Setup Instructions:
1. Create an NVIDIA Developer account at http://courses.nvidia.com/join.
2. Make sure that WebSockets works for you:
• Test your laptop at http://websocketstest.com• Under ENVIRONMENT, confirm that “WebSockets” is checked yes.
• Under WEBSOCKETS (PORT 80), confirm that “Data Receive,” “Send,” and “Echo Test” arechecked yes.
3. If there are issues with WebSockets, try updating your browser. We recommend Chrome, Firefox,
or Safari for an optimal performance.
4. Once onsite, visit http://courses.nvidia.com/dli-event and enter the event code provided by theinstructor.
Components
Course overview
Getting started with deeplearning

Description
Introduction to deep learning, situations in which
it is useful, key terminology, industry trends, andchallenges●

Biological inspiration for deepneural networks (DNNs)
Training DNNs with big data

Hands-on exercise: training neural networks toperform image classification by harnessing thethree main ingredients of deep learning: deepneural networks, big data, and the GPU

Unlocking New
Capabilities(40 mins)
Measuring andImproving
Performance(100 mins)●

Deploying DNN models●●

Optimizing DNN performance
Incorporating object detection

Hands-on exercise: deployment of trained neuralnetworks from their training environment into
real applications
Hands-on exercise: neural network performanceoptimization and applying DNNs to object
detection

Summary(20 mins)
Break (15 mins)●

Summary of key learnings

Review of concepts and practical takeaways

Assessment(60 mins)●Assessment project: train anddeploy a deep neural network

Next Steps(15 mins)●●Workshop survey
Setting up your own
GPU-enabled environment
Additional project ideas

Validate learnings by applying the deep learning
application development workflow (load dataset,train and deploy model) to a new problem
Learn how to setup your GPU-enabledenvironment to begin work on your own
projects. Explore additional project ideas alongwith resources to get started with NVIDIA RAPIDS
the cloud, nvidia-docker, and so on.

Introduction(45 mins)
Break (15 mins)
Unlocking New
Capabilities(120 mins)●●●

Break (45 mins)●DLI+C-FX-01+V2 DSV 4.02