Skip Main Navigation
Page Content

Save This Event

Event Saved

Advanced Computer Vision with Deep Learning (29 – 30 April 2020)

SGInnovate and Red Dragon AI

Wednesday, April 29, 2020 at 9:00 AM - Thursday, April 30, 2020 at 5:30 PM (Singapore Standard Time Singapore Time)

Advanced Computer Vision with Deep Learning (29 – 30...

Ticket Information

Use promotional code to access tickets.

Share Advanced Computer Vision with Deep Learning (29 – 30 April 2020)

Event Details

Overview

 

Together with Red Dragon AI, SGInnovate is pleased to present the second module of the Deep Learning Developer Series. In this module, we go beyond the basic skills taught in module 1, such as Convolutional Neural Networks (CNNs). This would expand your ability to build image networks — using a variety of architectures — for applications beyond simple classification.

 

About the Deep Learning Developer Series:

 

The Deep Learning Developer Series is a hands-on series targeted at developers and data scientists who are looking to build Artificial Intelligence (AI) applications for real-world usage. It is an expanded curriculum that breaks away from the regular eight-week long full-time course structure and allows for modular customisation according to your own pace and preference. In every module, you will have the opportunity to build your own Deep Learning models as part of your main project. You will also be challenged to use your new skills in an application that relates to your field of work or interest.

 

About this module:

 

Building on the learnings from the first module, we will be going beyond TensorFlow and Keras. PyTorch and TorchVision, which are used for Computer Vision research, will be introduced.

 

To understand the current state-of-the-art technologies, we will review the history of ImageNet winning models and focus on Inception and Residual models. We will also look at models such as NASNet and AmoebaNet, to explore how the field has gone beyond hand-engineered models.

 

One critical skill that you will acquire is how to apply these modern architectures to create applications like image search and similarity comparisons. We will also touch on object detection, allowing you to learn how models (like YOLO) can detect multiple objects in an image.

 

You will also learn about image segmentation and classification at the pixel level with architectures like U-Nets and DenseNets. Furthermore, you will learn how they are used in a variety of image segmentation tasks from perception for self-driving cars to medical image analysis.

 

As with the other Deep Learning Developer modules, you will have the opportunity to build multiple models yourself.

 

This workshop is eligible for funding support. For more details, please refer to the "Pricing" tab above.

 

In this course, participants will learn: 

  • About advanced classification and objection detection
  • An introduction into PyTorch and TorchVision
  • Skills to create applications like image search and similarity comparisons
  • About image segmentation and classification at the pixel level with architectures like U-Nets and DenseNets

Recommended Prerequisites:

 

Agenda

 

Day 1 (29 April 2020)

 

08:45am – 09:00am: Registration
09:00am – 10:30am: Convolutional Neural Networks (CNNs) Recap Part 1
Frameworks: TensorFlow, Keras

  • Convolution math in layers
  • Pooling and Strides
  • Alexnets
  • Building CNN networks
  • Calculating the parameters and shapes of various networks
  • Tuning CNN
  • VGG Network 

10:30am – 10:45am: Tea Break
10:45am – 12:30pm: CNNs Recap Part 2
Frameworks: TensorFlow, Keras 

12:30pm – 1:30pm: Lunch
1:30pm – 3:30pm: Intermediate CNNs Part 1
Frameworks: TensorFlow, Keras, PyTorch

  • Modern Convolutional Nets
  • Transfer learning with CNNs and fine tuning
  • Inception architectures
  • Residual networks
  • ImageNet history and applications
  • Building a classifier using transfer learning
  • Kaggle competition for images part 1
  • Start personal project 1

3:30pm – 3:45pm: Tea Break
3:45pm – 5:30pm: Intermediate CNNs Part 2
Frameworks: TensorFlow, Keras, PyTorch

 

6:30pm – 6:00pm: Closing comments and questions

 

Day 2 (30 April 2020)

 

08:45am – 09:00am: Registration
09:00am – 10:30am: CNN Architecture Part 1
Frameworks: TensorFlow, Keras, PyTorch

  • Auto Encoders
  • Repurposing CNN models
  • Object detection 
  • YOLO
  • Build an image search system
  • Continue personal project 1

10:30am – 10:45am: Tea Break
10:45am – 12:45pm: CNN Architecture Part 2
Frameworks: TensorFlow, Keras, PyTorch 

12:45pm – 1:45pm: Lunch
1:45pm – 3:45pm: CNNs Segmentation Part 1
Frameworks: TensorFlow, Keras, PyTorch 

  • Image search
  • Segmentation networks
  • U-Net and Skip connections architectures
  • Batch normalisation

3:45pm – 4:00pm: Tea Break
4:00pm – 5:30pm: Facial Recognition 
Frameworks: TensorFlow

5:30pm – 6:00pm: Closing comments and questions

Participants will be given two weeks to complete online learning and individual project. 

Online Learning 

  • Building CNNs from scratch
  • Building auto encoders
  • Understanding object detection and location models
  • Style transfer
  • Fast style transfer

Assessments

Participants must fulfil the criteria stated below to pass the course.

1. Online Tests: Participants are required to score a minimum grade of more than 75% 

2. Project: Participants are required to present, and achieve a pass on a project that demonstrates the following:

  • The ability to use or create a data processing pipeline that gets data in the correct format for running in a Deep Learning model
  • The ability to create a model from scratch or use transfer learning to create a Deep Learning model
  • The ability to train that model and get results.
  • The ability to evaluate the model on held out data

 

Pricing

 

Funding Support 

 

CITREP+ is a programme under the TechSkills Accelerator (TeSA) – an initiative of SkillsFuture, driven by Infocomm Media Development Authority (IMDA).

 


*Please see ‘Guide for CITREP+ funding eligibility and self-application process’  below for more information. 

Funding Amount: 

  • CITREP+ covers up to 90% of your nett payable course fee depending on your eligibility (for professionals)

Please note: funding is capped at $3,000 per course application

  • CITREP+ covers up to 100% of your nett payable course fee for eligible students / full-time National Servicemen (NSF)

Please note: funding is capped at $2,500 per course application

Funding Criteria:

  • Singaporean / PR
  • Meets course admission criteria
  • Sponsoring organisations must be registered or incorporated in Singapore (only for individuals sponsored by organisations)

Please note: 

  • Employees of local government agencies and Institutes of Higher Learning (IHLs) will qualify for CITREP+ under the “Individuals / Self-Sponsored” category
  • Sponsoring SMEs who wish to apply for up to 90% funding support for course must meet the SME status as defined here

Claim Conditions: 

  • Meet the minimum attendance (75%)
  • Complete and pass all assessments and / or projects

Guide for CITREP+ funding eligibility and self-application process:

For more information on CITREP+ eligibility criteria and application procedure, please click here

 

In partnership with:

   Driven by:

 

In partnership with employers to support employability:

 

 

For enquiries, please send an email to learning@sginnovate.com

 

Trainer

 



Dr Martin Andrews
Martin has over 20 years’ experience in Machine Learning and has used it to solve problems in financial modelling and has created AI automation for companies. His current area of focus and speciality is in natural language processing and understanding. In 2017, Google appointed Martin as one of the first 12 Google Developer Experts for Machine Learning. Martin is also one of the co-founders of Red Dragon AI. 
 



Sam Witteveen
Sam has used Machine Learning and Deep Learning in building multiple tech start-ups, including a children’s educational app provider which has over 4 million users worldwide. His current focus is AI for conversational agents to allow humans to interact easier and faster with computers. In 2017, Google appointed Sam as one of the first 12 Google Developer Experts for Machine Learning in the world. Sam is also one of the co-founders of Red Dragon AI. 

Have questions about Advanced Computer Vision with Deep Learning (29 – 30 April 2020)? Contact SGInnovate and Red Dragon AI

Save This Event

Event Saved

When & Where


SGInnovate
32 Carpenter Street
059911
Singapore

Wednesday, April 29, 2020 at 9:00 AM - Thursday, April 30, 2020 at 5:30 PM (Singapore Standard Time Singapore Time)


  Add to my calendar

Please log in or sign up

In order to purchase these tickets in installments, you'll need an Eventbrite account. Log in or sign up for a free account to continue.