• Online, Self-Paced
Course Description

Due to recent advancements in processing, neural networks have become easier to train, which made them extremely popular. In this course, you will learn about neural networks and how to use them.

Learning Objectives

Introducing Neural Networks (NNs)

  • start the course
  • describe neural networks and their capabilities
  • describe how different neural networks are structured
  • describe how cost functions are used to train neural networks
  • describe activation functions and list different types of commonly used activation functions
  • describe feedforward neural networks and the intuition behind calculating gradients in neural networks
  • describe how to use backpropagation for more efficient neural network training
  • describe batch learning and why it makes neural network training easier

TensorFlow (TF)

  • describe TensorFlow and its high-level architecture
  • set up TensorFlow for use on a CPU
  • import data into TensorFlow using built-in data sources and external data sources
  • build and train a single-layer neural network in TensorFlow
  • build and train a multilayer neural network in TensorFlow

Practice: Neural Networks

  • describe neural networks, network layers, cost functions, activation functions, and gradient descent

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.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.