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Introduction and Application of Convolutional Neural Networks

Convolutional neural network has been popular in the field of image processing and widespread real-world applications are now using CNN.

From the second low point in development in 1990s to 2006, neural networks once again entered the consciousness of the masses. And in 2012, the convolutional neural networks (CNN) model experienced a major breakthrough in the form of ImageNet in the field of image classification.

There are two core concepts to Convolutional Neural Networks. One is convolution and the other is pooling. At this point, some may ask why we don't simply use feed-forward neural networks rather than convolutional neural networks. Taking a 1000x1000 image for example, a neural network would have 1 million nodes on the hidden layer. A feed-forward neural network, then, would have 10^12 parameters. At this point it's nearly impossible for the system to learn since it would require an absolutely massive number of estimations.

However, a large number of images have characteristics like this. If we use convolutional neural networks to classify images, then because of the concept of convolution, each node on the hidden layer only needs to connect and scan the features of one location of the image. If each node on the hidden layer connects to 10*10 estimations, then the final number of parameters is 100 million, and if the local parameters accessed by multiple hidden layers can be shared, then the number of parameters is decreased significantly.

Another operation is pooling. A convolutional neural networks will, on the foundation of the principle of convolution, form a hidden layer in the middle, namely the pooling layer. The most common pooling method is Max Pooling, wherein nodes on the hidden layer choose the largest output value. Because multiple kernels are pooling, we get multiple hidden layer nodes in the middle.

These two characteristics of CNN have made it popular in the field of image processing, and it has become a standard in the field of image processing. And CNN has widespread real-world applications, for example in investigations, self-driving cars, Segmentation, and Neural Style. Neural Style is a fascinating application. For example, there is a popular app in the App Store called Prisma, which allows users to upload an image and convert it into a different style. For example, it can be converted to the style of Van Goh's Starry Night. This process relies heavily on CNN.

For more details about deep learning, particularly convolutional neural networks (CNN) and recursive neural networks (RNN), please go to All You Need to Know About Neural Networks – Part 2.

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