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You are here:: Training/Seminar Training Schedule Introduction to Deep Learning with NVIDIA GPUs (DLI) (อบรมเชิงปฏิบัติการพร้อมสอบประกาศนียบัตรในระดับสากล)

Introduction to Deep Learning with NVIDIA GPUs (DLI) (อบรมเชิงปฏิบัติการพร้อมสอบประกาศนียบัตรในระดับสากล)

Categories : Professional Certification Program
Posted by : kotchaphan | Posted On : Thursday, 31 May 2018 13:57
Training Date : 23 September 2020 - 25 September 2020
27 May 2020 - 29 May 2020
24 February 2020 - 26 February 2020
29 October 2019 - 31 October 2019
24 July 2019 - 26 July 2019
24 April 2019 - 26 April 2019
16 January 2019 - 18 January 2019
24 October 2018 - 26 October 2018
15 August 2018 - 17 August 2018
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กรุณา ล็อกอิน ก่อนลงทะเบียน หรือสร้างบัญชีผู้ใช้ใหม่ได้ ที่นี่
Time : 18 Hour(s)
Days : 3 Day(s)
Duration : 09:00 - 16:00
Fee : 27,500 THB (Excluded Vat 7%)
Language : English
Instructor : Dr.Thi Ngoc Tho LE
Objectives :

Course Overview:

Organizations are using deep learning and AI at every stage of growth, from start-ups 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 the 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 opensource frameworks and NVIDIA’s latest GPU-accelerated deep learning platforms.

Course Objectives:

- Introduction to DeepLearning
- Getting Started with Deep Learning
- Approaches to Object Detection using DIGITS
- Deep Learning for Image Segmentation
- Deep Learning Network Deployment
- Medical Image Segmentation using DIGITS
- Introduction to Deep Learning with R and MXNET
- Introduction to FCNs
- Signal Processing using DIGITS
- Deep Learning with Electronic Health Record

Who Should Attend :

Who Should Attend

Anyone interested in to learn more about Deep Learning, or kickstart a career as a Data Scientist. This includes Students, Data Analysts, Business Owners, Entrepreneurs or any individual who wishes to leverage on powerful Deep Learning tools to add value wherever they are.


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.

** For more Python knowledge to review this side 1st as link below.

Exam Format

Participant will receive a Beginner Lever certificate from NVIDIA Deep Learning Institute once you have completed the 3-day programme inclusive of participation in the 1-day NVIDIA Deep Learning Lab.


- No. of Questions: 30 Questions
- Duration - 1 hour
- Exam Type - Multiple Choice Questions (MCQ)
- Compulsory Passing Rate - 70%

Course Outline :

Day 1

What is Deep Learning and what are Neural Networks? (30 min):
- 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 (60 min):
- 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

BREAK (15 min)

Building an ANN (60 min):
- Getting the python libraries
- Constructing ANN
- Using the bank customer churn dataset
- Predicting if customer will leave or not

Evaluating Performance of an ANN (60 min):
- 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
- 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

Fullyy Convolutional Networks (FCN) Intuition (60 min):
- What are FCN’s?
- Vanishing Gradient problem
- Practical intuition
- LSTM variations

LUNCH (60 min)

Building a FCN (60 min):
- Getting the python libraries
- Constructing FCN
- Using the stock prediction dataset
- Predicting stock price

Evaluating Performance of a FCN (60 min):
- Evaluating the FCN
- Improving the FCN
- Tuning the FCN

BREAK (15 min)

Hands-On Exercise (60 min):
- Participants will be asked to build the FCN from the previous exercise
- Participants will be asked to improve the accuracy of their FCN



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



(45 mins)

● Course Overview 
● Getting Started with Deep Learning

Introduction to deep learning, situations in which it is useful, key terminology, industry trends, and challenges.

Break (15 mins)

Unlocking New Capabilities (120 mins)

● Biological inspiration for Deep Neural Networks (DNNs) 
● Training DNNs with Big Data

Hands-on exercise: Training neural networks to perform image classification by harnessing the three main ingredients of deep learning: Deep Neural Networks, Big Data, and the GPU.

Break (45 mins)

Unlocking New Capabilities (40 mins)

● Deploying DNN models

Deployment of trained neural networks from their training environment into real applications.

Measuring and Improving Performance (100 mins)

● Optimizing DNN Performance 
● Incorporating Object Detection

Hands-on exercise: neural network performance optimization and applying DNNs to object detection.

(20 mins)

● Summary of Key Learnings

Review of concepts and practical takeaways.

Break (15 mins)

(60 mins)

● Assessment Project: Train and Deploy a Deep Neural Network

Validate your learning by applying the deep learning application development workflow (load dataset, train and deploy model) to a new problem.

Next Steps
(15 mins)

● Workshop Survey 
● Setting up your own GPU enabled environment 
● Additional project ideas 
● Getting Data

Learn how to setup your GPU-enabled environment to begin work on your own projects. Get additional project ideas along with resources to get started with NVIDIA AMI on the cloud, nvidia-docker, and the NVIDIA DIGITS container.

- No. of Questions: 30 Questions
- Duration - 1 hour
- Exam Type - Multiple Choice Questions (MCQ)
- Compulsory Passing Rate - 70%

Payment Condition :

Payment can be made by:

    1. Cash or Credit Card or Bank Cheque payable to “Software Park Thailand #2” (a post-dated cheque is not accepted) on the first day of the service or within the last day of the service.
     2. Account transfer and send the proof of the payment (the deposit slip) via fax or email to fax no. 02-583-2884 or email

        2.1 Siam Commercial Bank, Chaengwattana Branch
             Saving Account Number: 324-2-56262-0
             Account Name: Software Park Thailand#2

        2.2 Krungsri Bank, Chaengwattana (Software Park) Branch
             Saving Account Number: 329-1-34850-3
             Account Name: Software Park Thailand#2

- Withholding tax (3%) is exempt.
- Should you need to withdraw, you must send the notice of the withdrawal in writing no later than 7 working days before the commencement date. The cancellation less than 7 days will be subject to a fine of 40% of the fee.
- Software Park Thailand reserves the rights to cancel courses due to unforeseen circumstances.

Contact Person :

For more information, contact our course coordinator on:

Name: Ms.Kotchaphan Aokdeelert

Tel: +66-2583-9992 Ext. 1425

Fax: +66-2583-2884

Email: or

You are encouraged to use the course schedule as a guide to plan your training. The schedule is accessible at for more information.