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NVIDIA-BDI Deep Learning Hands-On Labs

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Cheung On Tak Lecture Theatre E (LTE)

Academic Building, HKUST

Clearwater Bay

Hong Kong

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NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning. DLI and HKUST Big Data Institute are excited to announce this half-day practical Deep Learning Labs at location on Nov. 28th, 2017 exclusively for verifiable academic students, staff, and researchers.

In this half-day Deep Learning Fundamentals workshop, you will learn to

- Understand general terms and background of deep learning

- Leverage deep neural networks (DNN) within the deep learning workflow to solve a real-world image classification problem using DIGITS

Agenda:

1:00 – 1:15 – opening talk ( NVIDIA & HKUST )

1:15 – 3:00 – Hands on Lab 1

3:00 – 5:00 – Hands on Lab 2

5:00 – 5:30 – Q & A

Lab #1: Image Classification with DIGITS
Learn how to leverage deep neural networks (DNN) within the deep learning workflow to solve a real-world image classification problem using NVIDIA DIGITS. You’ll walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance using GPUs. On completion of this lab, you will be able to use NVIDIA DIGITS to train a DNN on your own image classification application.

Lab #2: Neural Network Deployment with DIGITS and TensorRT
Abstract: Once a deep neural network (DNN) has been trained using GPU acceleration, it needs to be deployed into production. The step after training is called inference as it uses a trained DNN to make predictions from new data.
In this lab we will show different approaches to deploying a trained DNN for inference. The first approach is to directly use inference functionality within a deep learning framework, in this case DIGITS and Caffe. The second approach is to integrate inference within a custom application by using a deep learning framework API, again using Caffe but this time through it’s Python API. The final approach is to use the NVIDIA TensorRT™ which will automatically create an optimized inference run-time from a trained Caffe model and network description file. You will learn about the role of batch size in inference performance as well as various optimizations that can be made in the inference process. You’ll also explore inference for a variety of different DNN architectures trained in other DLI labs.

Content level: beginner

Pre-Requisites: technical background and basic understanding of deep learning concepts

IMPORTANT: To reserve your seat, you MUST register at with a valid university email address and follow these pre-workshop instructions:

  • You must bring your own laptop to this workshop.
  • Create an account by going to https://nvlabs.qwiklab.com/ prior to getting to the workshop.
  • 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.
  • Please remember to sign in to nvlabs.qwiklab.com using the same email address as for event registration, since class access is given based on the event registration list.
  • Use the “eduroam” SSID such that visitors from other eduroam member institutions can freely access the internet. Time-limited Wi-Fi access is also available for anonymous clients connecting the “Wi-Fi.HK” SSID.

Looking forward to seeing you at HKUST!


NVIDIA Deep Learning Institute

The NVIDIA Deep Learning Institute delivers hands-on training for developers, data scientists, and engineers. The program is designed to help you get started with training, optimizing, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services, and robotics.

Twitter NVIDIAAI

Website www.nvidia.com/dli

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Date and Time

Location

Cheung On Tak Lecture Theatre E (LTE)

Academic Building, HKUST

Clearwater Bay

Hong Kong

View Map

Refund Policy

No Refunds

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