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Introduction to Deep Learning with NVIDIA GPUs

Stripe
Training/Course

Introduction to Deep Learning with NVIDIA GPUs

21 Feb - 23 February 2018 9:00am - 5:00pm
MaGIC (Malaysian Global Innovation & Creativity Centre) View map
Admission RM 3000 per pax (Early Bird Discount: RM 1500 per pax)
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Course Overview 

Artificial intelligence (AI) is growing exponentially.

We are living in an era where self-driving cars are clocking up millions of miles, with great accuracy, and where Google Deepmind’s AlphaGo beat the world champion at Go – a game where intuition plays a key role.

But as AI advances, the problems become even more complex to solve. Only Deep Learning can solve such complex problems and that’s why it’s at the heart of AI today. While companies like Amazon, Google, and Facebook are pouring billions into Deep Learning projects, what about the rest of us – where do we start?

This course aims to introduce students to Deep Learning as a subject within advanced AI and provides real-life problem sets that can be solved using Deep Learning neural networks.

Learning Objectives

Learn the fundamental concepts in Deep Learning and gain an understanding of the intuition and application of:

  • Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Self-Organizing Maps
  • Boltzmann Machines
  • AutoEncoders

Who Should Attend?

  • Anyone interested in Deep Learning
  • Students who have at least high school knowledge in math and who want to start learning Deep Learning
  • Any intermediate-level enthusiasts who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning
  • Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
  • Any students in college who want to start a career in Data Science
  • Any data analysts who want to level up in Deep Learning
  • Any people who are not satisfied with their job and who want to become a Data Scientist
  • Any people who want to create added value to their business by using powerful Deep Learning tools
  • Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business
  • Any Entrepreneur who want to create disruption in an industry using the most cutting edge Deep Learning algorithms

Course Outline

Day 1


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

2. 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)

3. Building an ANN (60 min)

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

4. Evaluating Performance of an ANN (60 min)

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

LUNCH (60 min)

5. 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

6. Convolutional Neural Networks (CNN) Intuition (60 min)

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

BREAK (15 min)

7. 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


1. Evaluating Performance of a CNN (60 min)

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

2. 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

BREAK (15 min)

3. Recurrent Neural Networks (RNN) Intuition (60 min)

  • What are RNNs?
  • Vanishing Gradient problem
  • LSTMs
  • Practical intuition
  • LSTM variations

LUNCH (60 min)

4. Building a RNN (60 min)

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

5. Evaluating Performance of a RNN (60 min)

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

6. 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


1. Image Classification with DIGITS (120 min)

  • How to leverage deep neural 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

2. Object Detection with DIGITS (120 min)

  • Train and evaluate an image segmentation network

LUNCH (60 min)

3. 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

4. Closing

  • Q & A Session

Prerequisite

  • Basic high school mathematics

Required Software

  • Anaconda for Python (version 3.x)
  • Optionally Sublime Text

 

Our Speaker

Tarun Sukhani

Tarun has 16 years of 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. Having acquired 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. Within the industry, he worked on ETL/Data Science/Machine Learning projects at Profitera, Experian, Atex, E-Radar, ICarAsia, and Max Money. Tarun continues to work within the BI space, most recently focusing his time on Deep/Reinforcement Learning projects within the Fintech sector. Tarun 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.

This course aims to introduce students to Deep Learning as a subject within advanced AI and provides real-life problem sets that can be solved using Deep Learning neural networks.