• Online, Self-Paced
Course Description

This 11-video course explores the concepts of computational graphics, interfaces for programming graphics processing units (GPUs), and TensorFlow Extended and its pipeline components. Learners discover features and elements that should be considered for machine learning when building deep learning (DL) models, as well as hyperparameters that can be tuned to optimize DL models. Begin by examining the concept of computational graphs and recognize essential computational graph operations used in implementing DL. Then learn to list prominent processors with specialized purpose and architectures used in implementing DL. Recall prominent interfaces for programming GPUs with focus on Compute Unified Device Architecture (CUDA) and OpenCL, and then take a look at TensorFlow Extended (TFX) and TFX pipeline components for machine learning pipelines. Discover how to setup the TFX environment; use the ExampleGen and StatisticsGen TFX pipeline components to build pipelines; work with TensorFlow Model analysis; and explore the practical considerations for DL build and train. Finally, recall essential hyperparameters of DL algorithms that can be tuned to optimize DL models. The concluding exercise involves optimizing DL applications.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.