{"page":"\n\n
\n
\n
\n
\n
\n\nVideo\n
\n

\nConvolutional Neural Network Image Classification\n

\n
\n
\nGet Free Access for 10 Days!\n
\n
\n
\n
\n
\n
×
\n\n\n
\n\nPublisher:\n\nAPMonitor\n\n\n
\n
\nDeep Learning (DL) is a subset of Machine Learning that uses Neural Network inspired architecture to make predictions. Convolutional Neural Networks (CNN) are a type of DL model that is effective in learning patterns in 2-dimensional...\nDeep Learning (DL) is a subset of Machine Learning that uses Neural Network inspired architecture to make predictions. Convolutional Neural Networks (CNN) are a type of DL model that is effective in learning patterns in 2-dimensional data such as images. Images of drill bit types are used to train a classifier to identify common drill bit types for Oil & Gas Exploration and Horizontal Directional Drilling (HDD).

This exercise demonstrates the use of image classification to distinguish between objects in photos. Although applied to bit types, the same methods and code can be used for any type or number of objects. Include train and test photos in folders that are named with the object type. The code automatically takes the name of the folder as the photo label for training the classifier.

0:00 Number Classification
0:52 Classification Introduction
1:58 Import Packages
3:25 Download Data
6:09 Import Photos
10:08 Build CNN
19:28 Model Testing
24:09 Crack Detection\n
\n
\n\n
\n
\n
\n
\n

Resource Details

\n
\n
\n
\n
Curator Rating
\n
\n
\n
\n
\n
\n
Educator Rating
Not yet Rated
\n
\n
\n
\n
\n
Media Length
\n
40:34
\n
\n
\n
\n
\n
Grade
10th - Higher Ed
\n
\n
\n
\n
\n
Subjects
STEM
3 more...
\n
\n
\n
\n
\n
Media Type
Instructional Videos
\n
\n
\n
\n
\n
Source:
\n
\nAPMonitor.com\n\n
\n
\n
\n
\n
\n
Date
\n
2022
\n
\n
\n
\n
\n\n
Language
English
\n
\n
\n
\n
\n
Audiences
For Teacher Use
2 more...
\n
\n
\n
\n
\n
Usage Permissions
Fine Print: Educational Use
\n
\n
\n
\n
\n
\n

Additional Materials

\n
\n\nSign in or Join Now
\" data-title=\"Get Full Access\" data-container=\"body\" rel=\"popover\" tabindex=\"0\" href=\"/subscription/new\">Video Transcript\n
\n
\n\nSign in or Join Now
\" data-title=\"Get Full Access\" data-container=\"body\" rel=\"popover\" tabindex=\"0\" href=\"/subscription/new\">Video Preview\n
\n
\n
\n

Concepts

\n
\n
model building, classification
\n
\n\n\n
\n
\n
\n
\n

Additional Tags

\n
\n
apmonitor, learn, machine learning, image, cnn, conv2d, convolution layer, flip horizontally, dense layers, roller cone, neural network, google colab, target size, bit classification, bit higher, input layer, input shape, randomly select, layer size, justice run, bit type, pdc bits, sequential model, model variable, major iterations, test set, current directory, activation function, loss function, h5 file, spoon bits, output values, image classification, train processor, validation accuracy
\n
\n\n\n
\n
\n
\n
\n

Classroom Considerations

\n
Best For: Explaining a topic
Video is ad-free
\n
\n
\n

Educator Ratings

\n
\n
\n
\n
\n
\n\nShow resource details\n
\n
\n\" data-title=\"Get Full Access\" data-container=\"body\" rel=\"popover\" tabindex=\"0\" aria-label=\"Play video: Convolutional Neural Network Image Classification\" href=\"/subscription/new\">\"Convolutional\n\n
\n
\n
\n\n\n\n"}