A convolutional neural network (CNN) is described for use in image classification. The CNN contains convolutional, nonlinear, pooling layers and fully connected layers that generate the output classification. Convolutional layers apply filters to image regions to extract features. Nonlinear and pooling layers introduce nonlinearity and downsample the image to reduce parameters. A fully connected layer at the end results in an N-dimensional vector representing the image classes. References are provided for further reading on CNNs and deep learning.

1. CONVOLUTIONAL NEURAL NETWORK (CNN)
FOR
IMAGE CLASSIFIER

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9.  CONVOLUTIONAL, NONLINEAR, POOLING LAYERS AND FULLY
CONNECTED LAYERS, GENERATES THE OUTPUT
 CONVOLUTION
 CONVOLUTION
LINE UP THE FEATURE AND THE IMAGE
MULTIPLY EACH IMAGE PIXEL BY CORRESPONDING
FEATURE PIXEL
ADD THE VALUES AND FIND THE SUM
DIVIDE THE SUM BY THE TOTAL NUMBER OF PIXELS
IN THE FEATURE

10.  THE NONLINEAR LAYER

11.  THE POOLING LAYER
 THIS MEANS THAT IF SOME FEATURES HAVE ALREADY BEEN IDENTIFIED IN THE PREVIOUS CONVOLUTION
OPERATION, THAN A DETAILED IMAGE IS NO LONGER NEEDED FOR FURTHER PROCESSING, AND IT IS COMPRESSED
TO LESS DETAILED PICTURES.
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A FULLY CONNECTED LAYER
 A FULLY CONNECTED LAYER TO THE END OF THE NETWORK RESULTS IN AN N DIMENSIONAL VECTOR,
WHERE N IS THE AMOUNT OF CLASSES FROM WHICH THE MODEL SELECTS THE DESIRED CLASS.

12. CONVOLUTION NEURAL NETWORK

13. 1. HTTP://WWW.DATAMIND.CZ/CZ/VAM-NA-MIRU/UMELA-INTELIGENCE-A-
STROJOVE-UCENI-AI-MACHINE-LEARNING
2. HTTPS://EN.WIKIPEDIA.ORG/WIKI/ARTIFICIAL_NEURAL_NETWORK
3. HTTPS://EN.WIKIPEDIA.ORG/WIKI/DEEP_LEARNING
4. HTTPS://EN.WIKIPEDIA.ORG/WIKI/CONVOLUTIONAL_NEURAL_NETWORK
5. HTTPS://ADESHPANDE3.GITHUB.IO/ADESHPANDE3.GITHUB.IO/A-
BEGINNER%27S-GUIDE-TO-UNDERSTANDING-CONVOLUTIONAL-NEURAL-
NETWORKS/
6. HTTPS://WWW.LYNDA.COM/GOOGLE-TENSORFLOW-TUTORIALS/BUILDING-
DEEP-LEARNING-APPLICATIONS-KERAS-2-0/601801-2.HTML
7. HTTPS://BLOG.KERAS.IO/BUILDING-POWERFUL-IMAGE-CLASSIFICATION-MODELS-
USING-VERY-LITTLE-DATA.HTML
8. HTTPS://KERAS.IO

14. FOR YOUR ATTENTION