TensorFlow: Convolutional Neural Networks for Image Classification


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

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



Expected Duration (hours)
1.4

Lesson Objectives

TensorFlow: Convolutional Neural Networks for Image Classification

  • 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 "
  • Course Number:
    it_sdaidt_04_enus

    Expertise Level
    Intermediate