Getting started with Deep Learning by using PowerAI

Course Details
Code: QCC10DG-WBT
Tuition (USD): $99.50 • Self Paced (4 hours)

This course shows students how they may quickly learn about working with data through Deep Learning (DL). Student are shown how to get hands-on experience by signing up for PowerAI free trial or free class that provides lab environments. Beginners learn how to get started with DL basics by running Jupyter Notebook. Experienced learners are shown how they may work with popular DL frameworks such as TensorFlow or try out their own practical projects within the free lab environments.

Skills Gained

After completing this course, you should be able to:

  • Recognize the need for Deep Learning (DL) when working with data
  • Summarize Deep Learning concepts and tools
  • Recognize PowerAI features and advantages
  • Identify PowerAI components
  • Recognize DL libraries and frameworks
  • Apply simple use cases for learning PowerAI

Who Can Benefit

This course is for the following audience:

  • Technical person interested in PowerAI
  • Scientist who wants to learn about PowerAI
  • Business owner who has needs for data analytics or deep learning
  • Power System administrators or architects
  • Power system users who work with PowerAI

Prerequisites

  • General skills in commands execution and working with user interfaces are required
  • Power Systems working experiences are assumed but not required

Course Details

Module 1

  • Lecture material: Learn to work with data and analytics

Module 2

  • Lecture material: Hightlights of PowerAI features and advantages
  • Reference: PowerAI product videos from youtube

Module 3

  • Lecture material: Getting started on PowerAI
  • Demo video: Using Jupyter Notebook examples

Module 4

  • Lecture material: Verifying DL frameworks and libraries
  • Demo video: Taking TensorFlow class with PowerAI lab

Module 5

  • Lecture material: The PowerAI Vision Preview Technology and use cases
Course Details
Code: QCC10DG-WBT
Tuition (USD): $99.50 • Self Paced (4 hours)