RM2,572.26 – RM3,674.65

Workshop: Artificial Intelligence Deep Learning Application

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Malaysian Global Innovation and Creativity Centre

Malaysia room (Level 2) @ MaGIC, 3730, Persiaran Apec, Cyberjaya

Cyberjaya, Selangor 63000

Malaysia

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Limited seats available!

Total Program Duration : 3 days

Fee: RM2450 (Original price: RM3500)
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To register directly with iTrain, please email your details info@itrain.com.my. (PAYMENT METHOD: Via transfer)

Organizations are using deep learning and AI at every stage of growth, from startups to Fortune 500s. Deep learning, the fastest growing field in AI, is empowering immense progress in all kinds of emerging markets and will be instrumental in ways we haven’t even imagined.

Today’s advanced deep neural networks use algorithms, big data, and the computational power of the GPU to change this dynamic. Machines are now able to learn at a speed, accuracy, and scale that are driving true artificial intelligence and AI Computing

Learn the latest techniques on how to design, train, and deploy neural network-powered machine learning in your applications. You’ll explore widely used open-source frameworks and NVIDIA’s latest GPU-accelerated deep learning platforms.


DLI Workshop Attendee Instructions: You must bring your own laptop to this workshop.

Pre-requisites to join:

3-Day Introduction to Deep Learning Workshop (19-21 Dec)
Required: Must have technical knowledge in R and Python, understand basic Data Science, Machine Learning and AI algorithms, familiarity with basic programming fundamentals such as functions and variables

This workshop teaches you to apply deep learning techniques to a range of computer vision tasks
through a series of hands-on exercises. You will work with widely-used deep learning tools, frameworks, and workflows to train and deploy neural network models on a fully-configured, GPU accelerated workstation in the cloud. After a quick introduction to deep learning, you will advance to building and deploying deep learning applications for image classification and object detection, followed by modifying your neural networks to improve their accuracy and performance, and finish by implementing the workflow that you have learned on a final project. At the end of the workshop, you will have access to additional resources to create new deep learning applications on your own.


Course Outline

INTRODUCTION LEVEL - DAY 1 & 2
Day 1

What is Deep Learning and what are Neural Networks?

  • Deep Learning as a branch of AI
  • Neural networks and their history and relationship to neurons
  • Creating a neural network in Python

Artificial Neural Networks (ANN) Intuition

  • Understanding the neuron and neuroscience
  • The activation function (utility function or loss function)
  • How do NN’s work?
  • How do NN’s learn?
  • Gradient descent
  • Stochastic Gradient descent
  • Backpropagation

Building an ANN

  • Getting the python libraries
  • Constructing ANN
  • Using the bank customer churn dataset
  • Predicting if customer will leave or not

Evaluating Performance of an ANN

  • Evaluating the ANN
  • Improving the ANN
  • Tuning the ANN

Hands-On Exercise (60 min)

  • Participants will be asked to build the ANN from the previous exercise
  • Participants will be asked to improve the accuracy of their ANN

Convolutional Neural Networks (CNN) Intuition (60 min)

  • What are CNN’s?
  • Convolution operation
  • ReLU Layer
  • Pooling
  • Flattening
  • Full Connection
  • Softmax and Cross-entropy

Building a CNN (60 min)

  • Getting the python libraries
  • Constructing a CNN
  • Using the Image classification dataset
  • Predicting the class of an image

Day 2

Evaluating Performance of a CNN (60 min)

  • Evaluating the CNN
  • Improving the CNN
  • Tuning the CNN

Hands-On Exercise (60 min)

  • Participants will be asked to build the CNN from the previous exercise
  • Participants will be asked to improve the accuracy of their CNN

Recurrent Neural Networks (RNN) Intuition (60 min)

  • What are RNN’s?
  • Vanishing Gradient problem
  • LSTMs
  • Practical intuition
  • LSTM variations

Building a RNN (60 min)

  • Getting the python libraries
  • Constructing RNN
  • Using the stock prediction dataset
  • Predicting stock price

Evaluating Performance of a RNN (60 min)

  • Evaluating the RNN
  • Improving the RNN
  • Tuning the RNN

Hands-On Exercise (60 min)

  • Participants will be asked to build the RNN from the previous exercise
  • Participants will be asked to improve the accuracy of their RNN


FUNDAMENTALS LEVEL - Day 3

HANDS-ON

Duration: 8 hours

Certification: Upon successful completion of this workshop, you will receive NVIDIA DLI Certification tO prove subject matter competency and support professional career growth

Tools, libraries and frameworks: Caffe, DIGITS


ABOUT YOUR TRAINER:

Dr Ibrahim Shapiai


Dr Ibrahim is a Nvidia Deep Learning Institute (DLI) certified trainer in Fundamentals of Computer Vision. He is a senior lecturer at Universiti Teknologi Malaysia. He received MEng from University of York, UK in 2007 and PhD from Universiti Teknologi Malaysia in the area of machine learning in 2013. He has also been appointed as the member of Special Group Interest on Machine Learning for Academy of Sciences Malaysia. During July until August 2015 he was a visiting researcher at Department of System Design Engineering Keio University, Japan for his collaborative research work with Assoc. Prof. Yasue Mitsukura.

He is also the visiting researcher at Faculty of Design Kyushu University, Japan for his collaborative research work with Assoc. Prof. Gerard Remijn in March 2017. He is now working actively in the area of brain computer interface (BCI) as deep learning focused application. He has conducted several deep learning training in Kuala Lumpur since 2017. He has been invited as speaker at HPC, Grid, Cloud & Identity (HGCI) Summit 2017 for his work on “Artificial Intelligence for Image and Signal Processing in Biomedical Applications” which exploring the advancement of deep learning for brain computer interface technology. He is now heading the project to establish Nvidia AI Centre at MJIIT, UTMKL with GTX Station as computing platform for modelling and deployment.

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Date and Time

Location

Malaysian Global Innovation and Creativity Centre

Malaysia room (Level 2) @ MaGIC, 3730, Persiaran Apec, Cyberjaya

Cyberjaya, Selangor 63000

Malaysia

View Map

Refund Policy

No Refunds

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