NVIDIA Deep Learning Institute Fundamentals Workshop for Computer Vision | SGInnovate
May 23


Perl @ BASH, Level 3
79 Ayer Rajah Crescent
Singapore 139955


Registration - $298.53

NVIDIA Deep Learning Institute Fundamentals Workshop for Computer Vision

Partnered With SGInnovate, NVIDIA & NSCC

SGInnovate partners with NVIDIA Deep Learning Institute (DLI) to offer hands-on training to developers, data scientists, and researchers looking to solve real world problems with deep learning across diverse industries such as self-driving cars, healthcare, online services and robotics.

This full-day workshop explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.

You will receive a Fundamentals certificate from the Deep Learning Institute!

Attendees MUST bring their own laptops

Workshop Overview:

This full-day workshop covers the foundations of deep learning and offers hands-on training in Image Classification, Object Detection, and Neural Network Deployment using popular frameworks.

You will learn to:
Understand basic of deep learning by training and deploying neural networks.
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.
Deploy your neural networks to start solving real-world problems.

Upon completion, you’ll be able to start solving problems on your own with deep learning.


Recommended Pre-requisites:

  • No background in deep learning is required for this training
  • Basic python understanding can be useful for some exercises
  • The mathematical and theoretical aspects of deep learning will NOT be covered by this training - and they're not a requirement to complete the labs, reading the Wikipedia page of Deep Learning would be a good start if you're interested.

08:45 Registration
09:15 Deep Learning Demystified (lecture)
10:00 Break
10:15 Image Classification with DIGITS (hands-on lab)
12:30 Lunch
13:30 Approaches to Object Detection with DIGITS (hands-on lab)
15:15 Tea Break
15:30 Neural Network Deployment with TensorRT (hands-on lab)
16:45 Closing Comments and Questions
17:00 End

Lab #1: Image Classification with DIGITS

Deep learning enables entirely new solutions by replacing hand-coded instructions with models learned from examples. Train a deep neural network to recognize handwritten digits by:

  • Loading image data to a training environment
  • Choosing and training a network
  • Testing with new data and iterating to improve performance

On completion of this Lab, you will be able to assess what data you should be training from.


Lab #2: Object Detection with DIGITS

Many problems have established deep learning solutions, but sometimes the problem that you want to solve does not. Learn to create custom solutions through the challenge of detecting whale faces from aerial images by:

  • Combining traditional computer vision with deep learning
  • Performing minor “brain surgery” on an existing neural network using the deep learning framework Caffe
  • Harnessing the knowledge of the deep learning community by identifying and using a purpose-built network and end-to-end labeled data.

Upon completion of this lab, you will be able to solve custom problems with deep learning.


Lab #3: Neural Network Deployment with DIGITS and TensorRT

Deep learning allows us to map inputs to outputs that are extremely computationally intense. Learn to deploy deep learning to applications that recognize images and detect pedestrians in real time by:

  • Accessing and understanding the files that make up a trained modeL
  • Building from each function’s unique input and output
  • Optimizing the most computationally intense parts of your application for different performance metrics like throughput and latency

Upon completion of this Lab, you will be able to implement deep learning to solve problems in the real world.


Instructors’ Biodata:

Dr Lim Nengli

Dr Lim is an Assistant Professor at the Engineering and Systems Design (ESD) pillar at SUTD. He received his Ph.D. in Mathematics at Imperial College London (under a joint programme with NUS), where he focused on stochastic analysis and rough paths theory. Before that, he studied random dynamical systems at Princeton University (M.A. Applied Mathematics), and in the Courant Institute of Mathematical Sciences at NYU (M.S. Mathematics).

Prior to obtaining his doctorate, he previously worked at the Bioinformatics Institute in A*STAR on image analysis, and at DSO National Laboratories on designing efficient algorithms to track targets obtained from sensor data. He is currently involved in various projects, such as memory-augmented Monte-Carlo planning, interactive question and answering systems in 3D simulated environments, volatility models in finance and interpretable auto-encoders for video generation and understanding.




DLI Attendees Pre-Workshop Instructions

  1. You MUST bring your own laptop to this workshop.
  2. Create an account by going to https://nvlabs.qwiklab.com/ prior to getting to the workshop.
  3. MAKE SURE your laptop is set up prior to the workshop by following these steps:
    • Ensure websockets runs smoothly on your laptop by going to http://websocketstest.com/
    • Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).
    • If there are issues with WebSockets, try updating your browser or trying a different browser. The labs will not run without WebSockets support.
    • Best browsers for the labs are Chrome, FireFox and Safari. The labs will run in IE but it is not an optimal experience.
  4. Please remember to sign in to https://nvlabs.qwiklab.com/ using the same email address as for event registration, since class access is given based on the event registration list.


For enquiries, please send an email to [email protected]

Topics: Artificial Intelligence / Deep Learning / Machine Learning / Robotics

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