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Artificial Intelligence Application – Intermediate Level (September)


Artificial Intelligence Application – Intermediate Level (September)

19 Sep - 21 September 2018 9:00am - 6:00pm
Vietnam Room (2nd Floor, MaGIC) View map
Admission RM1,800 (before promotional price)
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Session Summary 

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 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.

What Will You Learn?

  • Understand how to design, train, and deploy neural network-powered machine learning in your applications.
  • Explore widely used open-source frameworks and NVIDIA’s latest GPU-accelerated deep learning platforms.

Course Breakdown

The session will encompass the following content:

  • 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
      • 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
      • What are CNN’s?
      • Convolution operation
      • ReLU Layer
      • Pooling
      • Flattening
      • Full Connection
      • Softmax and Cross-entropy
    • Building a CNN
      • 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
      • Evaluating the CNN
      • Improving the CNN
      • Tuning the CNN
    • Hands-On Exercise
      • 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
      • What are RNN’s?
      • Vanishing Gradient problem
      • LSTMs
      • Practical intuition
      • LSTM variations
    • Building a RNN
      • Getting the python libraries
      • Constructing RNN
      • Using the stock prediction dataset
      • Predicting stock price
    • Evaluating Performance of a RNN
      • Evaluating the RNN
      • Improving the RNN
      • Tuning the RNN
    • Hands-On Exercise
      • Participants will be asked to build the RNN from the previous exercise
      • Participants will be asked to improve the accuracy of their RNN
  • Day 3
    • Image Classification with DIGITS
      • How to leverage deep neutral networks (DNN) within the deep learning workflow
      • Process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance using GPUs.
      • Train a DNN on your own image classification application
    • Object Detection with DIGITS
      • Train and evaluate an image segmentation network
    • Neutral Network Deployment with DIGITS and TensorRT
      • Uses a trained DNN to make predictions from new data.
      • Show different approaches to deploying a trained DNN for inference.
      • Learn about the role of batch size in inference performance as well as virus optimisations that can be made in the inference process.


  1. Exposure to Business Intelligence
  2. Exposure to Data Storage Solutions/Databases
  3. Exposure to introductory level in AI

Register today to secure your seats!

If you have any enquiry about the course, please email us at

Please be informed that we will get back to you within 3 working days.

**Please be informed that food and beverage will not be provided during this training.

Our Speakers

Fares Hassan

Fares is a Data Scientist at Sedania Innovator Berhad, building the analytics capability and transforming into a data-driven organization. An artificial intelligence researcher passionate about medical imaging & computer-aided detection systems, he was the 2nd runner-up in MMU data analytics hackathon. He has been freelancing as a Machine learning engineer working in conceptualizing machine learning and computer vision systems for commercial and research projects. Currently, Faris is building Pod, a startup and the first Microsaving Platform in Malaysia in data strategy and analytics. Fares is highly experienced in Python stack for data science and machine learning Matlab, OpenCV, Keras, and visualizations with Seaborn, Bokeh, Plotly, Tableau, and Data Studio. He is also dedicated to spreading the knowledge of Artificial Intelligence and actively involved with workshops and community meetups.

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.