• Online, Self-Paced
Course Description

Examine how to work with Convolutional Neural Networks, and discover how to leverage TensorFlow to build custom CNN models for working with images.

Learning Objectives

TensorFlow: Convolutional Neural Networks for Image Classification

  • Course Overview
  • compare the working of the visual cortex with a neural network
  • apply convolution to an input matrix and generate a result
  • use scikit-image to read in an image
  • instantiate a convolutional kernel to use with a convolutional layer
  • work with convolutional layers to detect edges in the input image
  • recognize how pooling works and its use in a convolutional neural network
  • recognize how hyperparameters are used to design the convolutional neural network
  • identify the standard structure of a convolutional neural network
  • define an overfitted model and the bias-variance trade-off
  • identify regularization, cross-validation, and dropout as ways to mitigate overfitting
  • describe how to use the CIFAR-10 dataset for image classification
  • demonstrate how to split the dataset into training and test images
  • create placeholders and variables for the convolutional neural network
  • define convolutional and pooling layers programmatically
  • demonstrate how to run training and prediction on the CIFAR-10 dataset
  • describe different kinds of encodings and why they are used


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.