Deep Learning Jump-Start Workshop | SGInnovate
November 22-23
2018

Location

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

Price

Early Bird Module 1 (Ticket Inclusive of G.S.T) - $813.2
Module 1 (Ticket Inclusive of G.S.T) - $856

Deep Learning Jump-Start Workshop

Organised by SGInnovate and Red Dragon AI

Together with Red Dragon AI, SGInnovate is proud to introduce to you the Deep Learning Developer Series. Back by popular demand, the Deep Learning Jump-start workshop is the first module of the Deep Learning Developer Series. This 2-days packed workshop is designed to introduce you to the skills needed to start your journey as a Deep Learning Developer. By the end of the workshop, expect to be empowered with the ability to take your new-found Deep Learning knowledge and apply it to your job / projects straight away!

This Deep Learning Developer Series is a hands-on and cutting-edge series is targeted at developers and data scientists who are looking to build AI Applications for real-world applications. The Deep Learning Developer Series is an expanded curriculum that breaks away from the regular 8 weeks full-time course structure and allows modular customisation according to your own pace and preference. 

The Jump-start workshop is the first module of the Deep Learning Developer Series and is a pre-requisite to the advanced Deep Learning modules. This workshop is designed to introduce you to the skills needed to start your journey as a Deep Learning Developer. It goes through both the overall concepts and techniques for not only understanding but building a variety of Deep Learning models, for tabular data, image data, audio data and text data. 

Start here

    • Have an interest in Deep Learning?
    • Join us if you are able to read and follow codes
    • This module is compulsory before you take the advanced modules
     
    • You will need to take module 1 before this module
     
    • You will need to take module 1 before this module
     
    • You will need to take module 1 AND module 2 OR 3 before this module
     
    • You will need to take module 1, 2 AND 3 before this module
    • Attain a “Deep Learning Specialist” certification when you complete all five modules
     
    • Have an interest in Deep Learning?
    • Join us if you are able to read and follow codes
    • This module is compulsory before you take the advanced modules
     
    • You will need to take module 1 before this module
     
    • You will need to take module 1 before this module
     
    • You will need to take module 1 AND module 2 OR 3 before this module
     
    • You will need to take module 1, 2 AND 3 before this module
    • Attain a “Deep Learning Specialist” certification when you complete all five modules
     

The 2-days packed curriculum is also an expanded version and will also cover many of the fundamentals needed in Deep Learning projects as well as covering models such as Fully Connected Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks. It goes through real-world examples of when to use each type of technique to fit the various data science problems at hand. 

You will take away from the workshop not just Deep Learning techniques but would also have applied them to a project of your choice. The goal is to empower you to have the ability to take your new found Deep Learning knowledge and apply it to your job / project straight away.

This workshop is eligible for funding support.

Prereqs and preparations:

  • An interest in Deep Learning
  • Ability to be able to read and follow code - We will send out some videos to help you with Python syntax specifically before the course begins.
  • Attendees MUST bring their own laptops

Workshop Overview:
In the course participants will learn:

  • The basic concepts of Neural Networks and an introduction to the mathematics of Deep Learning
  • They will be introduced to the Keras API and how it works as a higher level of abstraction for TensorFlow
  • Building and using TensorFlow native Estimators
  • Building various types of Deep Learning models
  • Building models for Computer Vision challenges
  • Building models for Natural language challenges

The workshop will be held over 2 intensive days and includes 6 hours’ worth of online content for self-revision. This allows you to quickly learn the skills needed to apply Deep Learning and have access to ask your questions one on one onsite. This is especially useful for understanding how to apply these skills to your unique applications.

This workshop is aimed at taking you from no (or little) knowledge to being able to build your own first project in Deep Learning.

Agenda:

Day 1 – Thurs, 22 Nov
8:45am - 9:00am: Registration
9:00am - 10:45am: The Key Concepts behind Deep Learning and Introduction to the basic math
10:45am - 11:00am: Tea Break
11:00am - 12:30pm: Building your first Neural Network
12:30pm - 1:30pm: Lunch
1:30pm - 3pm: Building a Convolutional Neural Network
3:00pm - 3:15pm: Tea Break
3:15pm - 4:15pm: Using Transfer Learning for new problems
4:15pm - 5:15pm: Doing a Project
5:15pm: Closing Comments and Questions

Day 2 – Fri, 23 Nov
8:45am - 9:00am: Registration
9:00am - 10:45am: Deep Learning for Natural Language Processing
10:45am - 11:00am: Tea Break
11:00am - 12:30pm: Project Clinic 1
12:30pm - 1:30pm: Lunch
1:30pm - 2:30pm: Deep Learning for Computer Vision 
2:30pm - 3:15pm: Building a Model for Structured Data with TensorFlow Estimators
3:15pm - 3:30pm: Tea Break
3:30pm - 4:30pm: Project Clinic 2
4:30pm: Closing Comments and Questions


DAY 1

Section 1: The Key Concepts behind Deep Learning and Introduction to the basic math
A simple introduction to how the math behind networks works

  • Math of Neural Networks and Back Propagation
  • Activation functions
  • Loss functions
  • Optimization functions

Section 2: Building your first Neural Network
Frameworks: TensorFlow, Keras - A look into the Keras API

  • Parts of a Model
  • Hidden Layers in action
  • Keras Layers API
  • Multi-Layer Perceptrons 
  • Setting Hyperparamaters

Section 3: Building a Convolutional Neural Network

  • Convolution layers
  • Pooling layers
  • Dropout and how it effects networks
  • Combining Convolution layers

Section 4: Using Transfer Learning for new problems
Frameworks: TensorFlow, Keras - Understanding the Estimator framework and its advantages

  • Inception Network
  • Building a classifier with a pre-trained network
  • Reusing and retraining weights for a specific task

Section 5: Doing a Project
Frameworks:TensorFlow, Keras - Actually *doing something* is very important

  • Ideas for projects to do
  • Q&A on ‘doable projects’
  • Homework: What to bring to the next session


DAY 2

Section 6: Deep Learning for Natural Language Processing Evening Session
Frameworks: TensorFlow, Keras, Estimators - Using Deep Learning for problems related to language

  • Ways to represent words and language
  • Intro to RNNs
  • Classifying Text
  • Project questions and general follow up

Session 1, Project Clinic 1

Session 2, Section 7: Deep Learning for Computer Vision Evening Session
Frameworks: TensorFlow, Keras, Estimators - Various types of Computer vision tasks

  • Understanding more advance image networks
  • Generative modelling for images
  • Examples of Style Transfer and Deep Dream

Section 8: Building a Model for Structured Data with TensorFlow Estimators
Frameworks:TensorFlow, Estimators, Datasets API - Understanding the Estimator framework and its advantages

  • What makes up an Estimator 
  • Canned Estimator
  • Building a network for Structured Data
  • Estimator Input function
  • Intro to the TensorFlow Datasets API

Project Clinic 2

    
Trainers

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 specialty is in natural language processing and understanding. In 2017, Google appointed Martin as one of the first 12 Google Developer Experts for Machine Learning.

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.

Funding Support
This workshop is eligible for CITREP+ funding. 

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


Funding Amount: 

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

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

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

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

Funding Eligibility: 

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

Please note: 

  • Employees of local government agencies and Institutes of Higher Learning (IHLs) will qualify for CITREP+ under the self-sponsored category
  • Sponsoring SMEs organization who wish to apply for up to 90% funding support for course must meet 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:

  

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

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

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