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Convolutional Neural Networks
In general, deep networks require more training data available to fully express their
power (see Figure 11):
Figure 11: Accuracy for different amount of data
A list of state-of-the-art results (for example, the highest performance available) for
MNIST is available online (http://rodrigob.github.io/are_we_there_yet/
build/classification_datasets_results.html). As of March 2019, the best
result has an error rate of 0.21% [2].
Recognizing CIFAR-10 images with deep
learning
The CIFAR-10 dataset contains 60,000 color images of 32×32 pixels in 3 channels,
divided in 10 classes. Each class contains 6,000 images. The training set contains
50,000 images, while the test sets provides 10,000 images. This image taken from
the CIFAR repository (https://www.cs.toronto.edu/~kriz/cifar.html)
shows a few random examples from each of the 10 classes:
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