https://github.com/armandfs/basic-cnn-with-keras

In reference to Eijaz Allibhai's CNN model, (I tweaked it a bit since there were errors with his previous outdated code, like changing the pre-processing code).
https://github.com/armandfs/basic-cnn-with-keras

Last synced: about 2 months ago
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In reference to Eijaz Allibhai's CNN model, (I tweaked it a bit since there were errors with his previous outdated code, like changing the pre-processing code).

• Host: GitHub
• URL: https://github.com/armandfs/basic-cnn-with-keras
• Owner: ArmandFS
• Created: 2023-04-04T15:44:54.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-07-18T14:15:53.000Z (almost 3 years ago)
• Last Synced: 2025-03-23T17:15:24.446Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 66.4 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# basic-cnn-with-keras

#### In reference to Eijaz Allibhai's CNN model, (I tweaked it a bit since there were errors with his previous outdated code, like changing the pre-processing code).

#### This is an example of a machine learning model of using the keras library and its datasets to use to build a simple convolutional neural network. A great way to use deep learning to classify images is to build a convolutional neural network (CNN). The Keras library in Python makes it pretty simple to build a CNN.Computers see images using pixels. Pixels in images are usually related. For example, a certain group of pixels may signify an edge in an image or some other pattern. Convolutions use this to help identify images. A convolution multiplies a matrix of pixels with a filter matrix or "kernel" and sums up the multiplication values. Then the convolution slides over to the next pixel and repeats the same process until all the image pixels have been covered. That is the base of convolution process