The first issue of image analysis is classification. The problem is that we get a picture at the input and we want to get information at the output. The classification is defined by assigning an object to a group of objects called a class. It assumes that we have a group of objects and assign them to classes at the same time. Each object should be characterized by appropriate features: shape, color, proportions of different characteristic sizes. In order to assign an object to a particular class, it is necessary to find a set of features that can be found automatically in the image.

According to Wikipedia, apophenia is "the tendency to mistakenly perceive connections and meaning between unrelated things" . It is also used as "the human propensity to seek patterns in random information". Whether it's a scientist doing research in a lab, or a conspiracy theorist warning us about how "it's all connected", I guess people need to feel like we understand what's going on, even in the face of clearly random information. Deep Neural Networks are usually treated like "black boxes" due to their inscrutability compared to more transparent models, like XGboost or Explainable Boosted Machines. However, there is a way to interpret what each individual filter is doing in a Convolutional Neural Network, and which kinds of images it is learning to detect.

A gentle guided tour of Convolutional Neural Networks. Come lift the curtain and see how the magic is done.

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Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories even better than humans in some cases. If there's one method out there that justifies the hype, it is CNNs. What's especially cool about them is that they are easy to understand, at least when you break them down into their basic parts.

Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. Also called CNNs or ConvNets, these are the workhorse of the deep neural network field. They have learned to sort images into categories even better than humans in some cases. If there's one method out there that justifies the hype, it is CNNs. What's especially cool about them is that they are easy to understand, at least when you break them down into their basic parts. I'll walk you through it.