A pattern-matching view on convolutions and neural networks
Deep learning is everywhere nowadays, from your smartphone camera to your smart speaker. However, many people think that deep learning algorithms "think" and make conscious decisions because, after all, they are modeled after the brain. However, the reality is far from it. In the end, a neural network is nothing more than a mathematical function; a VERY VERY VERY complicated function, but a clearly defined function with predictable outputs. In short: "AI" is not intelligent yet, but can produce results that have some semblance of intelligence.
To illustrate this "dumb AI" paradigm, we will explain the intuition behind convolutional neural networks (CNNs) - the foundation for most SOTA (state of the art) models in computer vision. We'll show you how deep learning on images actually works down to its most basic idea while staying away from fancy jargon and complex math. Our goal is to open the black box of how vision AI works for people who don't have a background in machine learning. But also for proven ML-practitioners, it doesn't hurt to reinforce the intuition behind CNNs. By understanding how convolutions work, you'll be aware of vision AI's limitations and understand that this is not a one-size-fits-all, magic bullet solution to every problem.
We'll provide some simple code examples along the way so you can play around with the code and get an even more in-depth understanding of the topic.