• Online, Self-Paced
Course Description

Discover how to apply deep learning techniques to images, and how to leverage TensorFlow estimators in building image classification models.

Learning Objectives

TensorFlow: Deep Neural Networks and Image Classification

  • Course Overview
  • distinguish between traditional machine learning and deep learning
  • recognize the architecture and design of a neural network
  • identify what is meant by model weights or model parameters
  • identify the precise operations performed by a neuron
  • recognize gradient descent as the training process in a neural network
  • distinguish between the operations in the forward and backward passes during training
  • describe how images are fed into a machine learning algorithm
  • configure TensorFlow and use Jupyter notebooks
  • load and explore the MNIST dataset for image classification
  • train a deep neural network estimator for image classification
  • use an estimator to predict image labels
  • describe why deep neural networks don't work well with images
  • Define how neural networks work
  • recall basics of image classification using neural networks
  • define the role of convolutional and pooling layers in a convolutional neural network


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.