• Online, Self-Paced
Course Description

Some tasks aren't suitable for traditional neural networks and require specialized neural networks. In this course you will learn about convolutional and recurrent neural networks and the types of problems they can solve.

Learning Objectives

Convolutional Neural Networks

  • start the course
  • describe convolutional neural networks, how they are different from regular neural networks, and how they are used
  • describe the high level architecture of convolutional neural networks
  • describe how convolution layers are set in convolutional neural networks
  • describe how pooling layers work in convolutional neural networks
  • describe some training considerations for convolutional neural networks and how training can differ from traditional neural networks
  • describe regularization and how it applies to convolutional neural networks
  • implement and train a convolutional neural network in TensorFlow
  • perform regularizing to a convolutional neural network in TensorFlow

Recurrent Neural Networks

  • describe recurrent neural networks, how they are different from regular neural networks, and how they are used
  • describe the architecture of a recurrent neural network
  • implement an LSTM network in TensorFlow
  • use RNNs to perform time-series analysis in TensorFlow

Practice: CNNs in TensorFlow

  • use TensorFlow to create a CNN that classifies images

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.