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 Multiple Data Types
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 toattend a hands-on deep learning workshop on Tuesday, June 30, 2020 from 9:00 am – 5:00 pm on Zoom,exclusively for verifiable academic students, staff, and researchers. This workshop uses a series ofhands-on exercises to teach deep learning techniques for a range of problems involving multiple datatypes. You will work with widely-used deep learning tools, frameworks, and workflows to performneural network training on a fully-configured, GPU-accelerated workstation in the cloud. After a quick
introduction to deep learning, you will advance to building deep learning applications for image
segmentation, sentence generation, and image and video captioning — while simultaneously learningrelevant computer vision, neural network, and natural language processing concepts. At the end of theworkshop, you will be able to assess a broad spectrum of problems where deep learning can be applied.
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 aB.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), anda member of the editorial boards of several international publications. Dr. Chan is the VP of Education
and a Governing Board member of the Asia Pacific Neural Network Society (APNNS). In addition, he is afounding member and the current Chair of the IEEE-CIS Thailand Chapter. Dr. Chan is a senior member ofIEEE, ACM, and INNS, and a member of the Professional Engineers of Ontario (PEO). Furthermore, heholds an NVIDIA Deep Learning Institute (DLI) University Ambassadorship and is a certified DLI instructor.
His research interests include intelligent systems, biomedical informatics, and data science and machinelearning.
Duration:
Assessment type:
Certification:
Prerequisites:
Tools, libraries,and frameworks:

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6 hours+
Multiple choice
Upon successful completion of the workshop, participants will receive NVIDIA DLICertification to recognize subject matter competency and support professional career
growth.
Successful completion of ‘Fundamentals of Deep Learning for Computer Vision’ DLI
course, or equivalent. Familiarity with basic Python (functions and variables) and priorexperience training neural networks is expected.
TensorFlow, TensorBoard

1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 Multiple Data Types

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 segmentation and text generation.
● Compare and contrast data types, workflows, and frameworks.
● Combine deep learning-powered computer vision and natural language processing to startsolving sophisticated real-world problems that require multiple input data types.

Why Deep Learning Institute Hands-On Training?
●Learn how to build deep learning and accelerated computing applications across a wide range ofindustry segments such as autonomous vehicles, digital content creation, finance, gamedevelopment, and healthcare● Obtain guided hands-on experience using the most widely-used, industry-standard software,tools, and frameworks● Gain real-world expertise through content designed in collaboration with industry leaders
including the Children’s Hospital Los Angeles, Mayo Clinic, and PwC● Earn NVIDIA DLI Certification to demonstrate your subject matter competency and support
professional career growth● Access content anywhere, anytime with a fully-configured, GPU-accelerated workstation in thecloudWorkshop Outline
Introduction(10 mins)
ImageSegmentation
withTensorFlow(120 mins)
Break (15 mins)
Word
Generation
withTensorFlow(120 mins)
Lunch (45 mins)
Image andVideo
Captioning(120 mins)

Summary(15 mins)

Components● Content overview● Project briefing

Description
Briefly present the situations in which deeplearning is useful and project briefing.

●Compare image segmentation toother computer vision problems
Experiment with TensorFlow tools
Implement effective metrics forassessing model performance

Hands-on exercise: Segment MRI images tomeasure parts of the heart using tools such asTensorBoard and the TensorFlow Python API.

Introduction to natural language
processing (NLP) and recurrent
neural networks (RNNs)
Create network inputs from text data
Test with new data
Iterate to improve performance

Hands-on exercise: Train a recurrent neuralnetwork to understand both images and text,
and to predict the next word of a sentence using
the Microsoft Common Objects in Context(MSCOCO) dataset.

Combine computer vision andnatural language processing todescribe scenes
Learn to harness the functionality of
convolutional neural networks(CNNs) and RNNs

Hands-on exercise: Train a model that generates
a description of an image from raw pixel data by
combining outputs of multiple networks (CNNsand RNNs) through concatenation and/oraveraging.

Summary of key learnings
Workshop survey

Review of concepts and practical takeaways

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