The document introduces convolutional neural networks and how they are used for image recognition through a series of examples using simple arithmetic operations on matrices to represent images and applying filters. It explains how convolutional neural networks use convolution layers to apply filters to images to extract features, pooling layers to downsample images, and fully connected layers to classify images. The networks are trained on labeled image data using gradient descent to minimize errors and improve the ability of the network to accurately classify new images.

1. Luis Serrano
A friendly introduction to
Convolutional Neural Networks
and Image Recognition

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6. Image recognition software
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Image Recognition Classifier
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16 choices

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Slightly More Complex World
Person Alphabet
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Computer
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Convolutional Neural Network
Convolution Layer
Pooling Layer
Fully Connected
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31. Convolution Layer
Pooling Layer
Fully Connected Layer

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Convolution Layer Pooling Layer Fully Connected Layer
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41. X O
Artificial Intelligence
?

42. Gradient Descent
X O
Lots of
errors
Few
errors

43. People Computer Alphabet
Advanced World

46. http://serrano.academy
https://www.linkedin.com/in/luisgserrano
https://www.youtube.com/c/LuisSerrano
Twitter: luis_likes_math
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