$309.49 – $924.49

BANGKOK: Introduction to Deep Learning with NVIDIA GPUs

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NSTDA Academy

73/1 NSTDA 6th Floor, Yothi Alley, Khwaeng Thung Phaya Thai, Khet Ratchathewi, Krung Thep Maha Nakhon

Bangkok, 10400

Thailand

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Please take note that there are 2 types of tickets.

TICKET 1: Introduction to Deep Learning with NVIDIA GPUs

Fee for 3-Days (16-18 MAY) :
USD900 / THB 28,782.00 per pax

Required:
Basic high school mathematics knowledge, no prior Deep Learning knowledge.

You will receive a Beginner Level certificate from NVIDIA Deep Learning Institute once you completed the 3-day programme with inclusive participation of the 1-day NVIDIA Deep Learning Lab


TICKET 2: NVIDIA Deep Learning Institute Fundamentals Workshop

Fee for 1-Day Workshop (18 MAY) :
USD300 / THB 9,594.00 per pax

Required:
MUST have technical background and basic understanding of Deep Learning concepts


You will receive a Certificate in Deep Learning Fundamentals by NVIDIA Deep Learning Institute upon completion of this 1-day workshop.

*EARLY BIRD DISCOUNT OF 10% APPLIES ONLY FOR 3-DAY WORKSHOP VALID UNTIL 2 MAY 2018*



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.--

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

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

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


Day 3

Image Classification with DIGITS (120 min)

  • 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 (120 min)

  • Train and evaluate an image segmentation network

Neutral Network Deployment with DIGITS and TensorRT (120 min)

  • 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

Closing comments and questions


ABOUT YOUR TRAINERS:

TARUN SUKHANI

Tarun Sukhani has 16 years of both academic and industry experience as a data scientist over the course of his career. Starting off as an EAI consultant in the USA, Tarun was involved in a number of integration/ETL projects for a variety of Fortune 500 and Global 1000 clients, such as BP Amoco, Praxair, and GE Medical Systems.

While completing his Master's degree in Data Warehousing, Data Mining, and Business Intelligence at Loyola University Chicago GSB in 2005, Tarun also worked as a BI consultant for a number of Fortune 500 clients at Revere Consulting, a Chicago-based boutique IT firm focusing on Data Warehousing/Mining projects. Tarun continues to work within the BI space, most recently focusing his time on Deep/Reinforcement Learning projects within the Fintech sector.

Tarun Sukhani has worked on parametric statistical modeling as well within the Data Science and Big Data Science space, using tools such as SciPy in Python and R and R/Hadoop for Big Data projects.

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WARASINEE CHAISANGMONGKON, Ph.D.

Dr. Chaisangmongkon is a lecturer at the Institute of Field Robotics, King Mongkut’s University of Technology Thonburi and a research associate at Center of Neural Science at New York University. She was awarded a Ph.D. in Computational Neuroscience from Yale University, where she performed data analytics and used machine learning models to understand human brain. Upon joining KMUTT, she pursues research and technology development in the area of big data analytics, specialized in consumer behaviors and web mining.


In the last decade, Dr. Chaisangmongkon has been studying human cognition in the area of decision making and economic judgement. She used a multitude of data mining techniques to analyze big data in neuroscience and employed machine learning to make predictions about observable behaviors and associated neural mechanisms. She has extensive experiences building mathematical models of complex dynamical neural system on large-scale computing clusters. She collaborated with scientists from world-class research institutes including but not limited to University of Chicago, Johns Hopkins University, and IBM.

She also specializes in Large-scale machine learning for customer analytics, banking and financial services, retail, telecommunications, human resource analytics, predictive marketing, digital marketing.


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Date and Time

Location

NSTDA Academy

73/1 NSTDA 6th Floor, Yothi Alley, Khwaeng Thung Phaya Thai, Khet Ratchathewi, Krung Thep Maha Nakhon

Bangkok, 10400

Thailand

View Map

Refund Policy

No Refunds

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