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Introduction to Deep Learning with NVIDIA GPUs

Do you want to learn data science from industry experts? This certification course for data science offers just that! Data science is undoubtedly the gold mine of the present and future. It enables you to seek valuable information through data analysis and machine learning functions.

From deriving strategic business decisions, to unlocking new economic values for businesses, data science allows presentation of predictions derived from data evidence.

This Certified Data Science Specialist Certification (CDSS) combines a good balance of theoretical knowledge and practical application. Upon completion of this 5-day course, you will be capable of leveraging data and put yourself in the forefront of any business decisions.

Audience Profile

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.

Participant Prerequisites

Basic high school mathematics knowledge, no Prior Deep Learning knowledge. Basic Python understanding can be used for some exercise.

Course Objectives

Upon completion of this course, you will be able to:

  • Introduction to Deep Learning
  • 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 and MXNET
  • Introduction to RNNs
  • Signal Processing using DIGITS
  • Deep Learning with Electronic Health Record

Course Outline

The following items describe the outline of the course:

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

Building a CNN (60 min)

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

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

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
  • Practical intuition
  • LSTM variations
  • LSTMs

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:

Introduction (45 mins)

Components

  • Course Overview
  • Getting Started with Deep Learning

Description

Introduction to Deep Learning, situations in which it is useful, key terminology, industry trends, and challenges

Unlock New Capabilities (120 mins)

Components

  • The biological inspiration for Deep Neural Networks (DNNs)
  • Training DNNs with Big Data

Description

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

Unlock New Capabilities (40 mins)

Components

  • Deploying DNN Models

Description

Hands-on exercise: Deployment of trained neural networks from their training environment into real applications

Measuring and Improving Performance (100 mins)

Components

  • Optimizing DNN Performance
  • Incorporating Object Detection

Description

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

Summary (20 mins)

Components

  • Summary of Key Learnings

Description

Review of concepts and practical takeaways

Assessment (60 mins)

Components

  • Assessment Project: Train and Deploy a Deep Neural Network

Description

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)

Components

  • Workshop Survey
  • Setting up your own GPU enabled-environment
  • Additional project ideas

Description

Learn how to set up 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

Course Materials

The following materials are included as part of the course:

  • iTrain Asia official digital curriculum

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.

DL - Introduction to Deep Learning with NVIDIA GPUs

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