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Deep Learning Jump-Start Workshop (25 - 26 April 2019)

SGInnovate and Red Dragon AI

Thursday, April 25, 2019 at 9:00 AM - Friday, April 26, 2019 at 5:00 PM (Singapore Standard Time Singapore Time)

Deep Learning Jump-Start Workshop (25 - 26 April 2019)

Ticket Information

Ticket Type Sales End Price Fee Quantity
Module 1 (Ticket Inclusive of G.S.T)
Ticket prices are inclusive of GST. If you are organisation-sponsored and require a Tax Invoice, please email learning@sginnovate.com, instead checking out via PayPal. Eligible funding is on a reimbursement basis upon fulfillment of the funding criteria.
Apr 26, 2019 $856.00 $0.00

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Event Details

Together with Red Dragon AI, we at SGInnovate are pleased to present 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-day 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 Artificial Intelligence (AI) applications for real-world usage. 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 prerequisite 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.

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

Apart from learning Deep Learning techniques, you will also have applied them to a project of your choice. The goal is to empower you with the ability to take your new-found Deep Learning knowledge and apply it to your job / projects right away.

The course will consist of 2 full days of intensive classroom training. At the end of the classroom training, once you understand the basics, you will go home to work on your own models and projects. There will be follow up online learning with learning materials and assessments. This allows you to quickly learn the skills needed to apply Deep Learning and have the 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 eligible for funding support. For more details, please refer to the "Pricing" tab above.

Workshop Overview:

In this course, participants will learn:

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

Recommended Prerequisites:

  • An interest in Deep Learning
  • Ability to read and follow code - We will send out some videos to help you with Python syntax specifically before the course begins.

Pre-Workshop Instructions:

  • You MUST bring your own laptop to this workshop.
  • Please watch the introductory videos that will be sent out separately.
  • Please experiment with the pre-exercises given.

Day 1 

08:45AM – 09:00AM: Registration
09:00AM – 10:45AM: Key Concepts behind Deep Learning and Introduction to the basic math
A simple introduction to how the math behind networks works

  • What is Deep Learning and examples of Deep Learning in the industry
  • The math of Neural Networks and Back Propagation
  • Activation functions
  • Loss functions
  • Optimisation functions

10:45AM – 11:00AM: Tea Break
11:00AM – 12:30PM: 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 Hyperparameters

12:30PM – 1:30PM: Lunch
1:30PM – 3:00PM: Building a Convolutional Neural Network
Frameworks: TensorFlow, Keras - Convolutional Model Architectures

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

3:00PM – 3:15PM: Tea Break
3:15PM – 4:45PM: Using Transfer Learning for new problems
Frameworks: TensorFlow, Keras - Understanding the Estimator framework and its advantages

  • Inception Network
  • VGG16
  • Building a classifier with a pre-trained network
  • Reusing and retraining weights for a specific task

4:45PM – 5:15PM: 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

5:15PM – 5:30PM: Closing Comments and Questions

Day 2 

8:45AM – 9:00AM: Registration
9:00AM – 10:45AM: Deep Learning for Natural Language Processing
Frameworks: TensorFlow, Keras, Estimators - Using Deep Learning for problems related to language

  • Ways to represent words and language
  • Intro to Recurrent Neural Networks (RNNs)
  • Using RNNs on character models
  • Classifying Text
  • Project questions and general follow up

10:45AM – 11:00AM: Tea Break
11:00AM – 12:30PM: Project Clinic 1
Project questions and general follow up

12:30PM – 1:30PM: Lunch
1:30PM – 2:30PM: Deep Learning for Computer Vision
Frameworks: TensorFlow, Keras, Estimators - Various types of Computer vision tasks

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

2:30PM – 3:15PM: Building a Model for Structured Data with TensorFlow estimators
Frameworks: TensorFlow, Estimators, Datasets API - Understanding the Estimator framework and its advantages

  • How does TensorFlow fit the APIs together into an end to end system
  • Building input pipelines
  • Building a network for Structured Data
  • Using tf.Data for pipelines
  • Intro to the TensorFlow Datasets API

3:15PM – 3:30PM: Tea Break
3:30PM – 4:30PM: Project Clinic 2
Project questions and general follow up

4:30PM – 5:00PM: Closing Comments and Questions

Online Learning (9.5 hours)

  • Python Basics
  • Colabs and Notebooks
  • Neural Network Basics
  • Keras Basics
  • CNNs
  • RNNs
  • TensorFlow Estimators
  • Preprocessing Patterns
  • Project Walk Throughs
  • Cloud Training

Passing requirements:

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

1.    Online Tests: Participants are required to score an average grade of more than 75% correct answers to the online questions.

2.    Project: Participants are required to present 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 based on held out data.

Funding Support 

This workshop is eligible for CITREP+ funding.

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


*Please see the section below on ‘Guide for CITREP+ funding eligibility and self-application process’

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 Servicemen (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 organisations)

Please note: 

  • Employees of local government agencies and Institutes of Higher Learning (IHLs) will qualify for CITREP+ under the self-sponsored category
  • Sponsoring SMEs 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:

  

In partnership with employers to support employability:

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



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 Deep Learning Jump-Start Workshop (25 - 26 April 2019)? Contact SGInnovate and Red Dragon AI

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When & Where


BASH, Level 3,
79 Ayer Rajah Crescent
139955
Singapore

Thursday, April 25, 2019 at 9:00 AM - Friday, April 26, 2019 at 5:00 PM (Singapore Standard Time Singapore Time)


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