Unpacking a cornerstone modeling strategy
Convolutional neural networks are a mainstay in computer vision, signal processing, and a massive number of other machine learning tasks. They’re fairly straightforward and, as a result, many people take them for granted without really understanding them. In this article we’ll go over the theory of convolutional networks, intuitively and exhaustively, and we’ll explore their application within a few use cases.
Who is this useful for? Anyone interested in computer vision, signal analysis, or machine learning.
How advanced is this post? This is a very powerful, but very simple concept; great for beginners. This also might be a good refresher for seasoned data scientists, particularly in considering convolutions in various dimensions.
Pre-requisites: A general familiarity of with backpropagation and dense neural networks might be useful, but is not required. I cover both of those in this post:
The first topic many fledgling data scientists explore is a dense neural network. This is the classic neural network consisting of nodes and edges which have certain learnable parameters. These parameters allow the model to learn subtle relationships about the topics they’re trained on.
As the number of neurons grow within the network, the connections between layers become more and more abundant. This can allow complex reasoning, which is great, but the “denseness” of dense networks presents a problem when dealing with images.
Let’s say we wanted to train a dense neural network to predict if an image contains a dog or not. We might create a dense network which looks at each pixel of the image…
This post originally appeared on TechToday.