TensorFlow: Convolutional Neural Networks for Image Classification Part 2 from Skillsoft

Online, Self-Paced

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

Design and Development

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