Deep Learning Fundamentals Workshop

Melbourne, Jan 10, 2018 – NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning. DLI is excited to announce this one-day practical Deep Learning workshop in Melbourne, Australia on February 22, 2018.

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

Agenda:

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

Content Level: Beginner

Pre-requisite:

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.

Register Now

Date: Thu, February 22, 2018
Time: 9:00 AM – 5:00 PM AEDT
Venue: Rydges Melbourne
186 Exhibition Street
Melbourne, VIC 3000 Australia

Register

Early Bird Price:

A$350 exc GST
(Normally $450 exc GST)
Early Bird Offer ends 9th February 2018

Training Syllabus

Lab #1: Image Classification with DIGITS	Lab #2: Object Detection with DIGITS	Lab #3: Neural Network Deployment with DIGITS and TensorRT
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.	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.	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.

Lab #1: Image Classification with DIGITS

Lab #2: Object Detection with DIGITS

Lab #3: Neural Network Deployment with DIGITS and TensorRT

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.

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.

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.

IMPORTANT: Please follow these pre-workshop instructions:

You must bring your own laptop, charger and adaptor (if required) 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.

This workshop is brought to you by XENON Systems Ltd.