• 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
  • define the role of convolutional and pooling layers in a convolutional neural network


Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Software Development