Credit: Deep Learning A-Z™: Hands-On Artificial Neural Networks
The brain processes a certain feature on an image and it classifies that assertion. Depends on the feature that you see, you categorize things in certain ways.
CNN works similar to our brain processing.
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There are several types of pooling: mean pooling, max pooling, sum pooling...
Applying Max Pooling
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Summary of 3 steps
In this example above, why do we have 2 outputs but not one?
The process in this ANN works as normal:
How two output neurons work?
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The output of a single prediction output will be 2 values indicate the probability of a dog and a cat, and why these two values add up to 1?
Cross-Entropy function: $\large L_i = - \log (\large \frac{e^{f_{y_i}}}{\sum_j e^{f_i}}) $
Compare different loss functions when compare 2 neural networks
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