Convolutional neural networks (CNNs) are the powerhouse behind modern computer vision. And although they’ve grown in popularity with the rise of deep learning, they long predate it. In fact, CNNs have long been used with hand-crafted filters. These filters are used to extract specific components of an image which can then be used to understand what is in the image. This post demonstrates some of the most popular filters used in traditional computer vision. You’ll notice that in deep learning the word “filter” is sometimes used interchangeably with the word “kernel”. We’ll use both terms here as well.

This post provides a summary of some of the most important overhead imagery datasets for object detection. The aim of this post is to be a living document where I continue to add new datasets as they are released.

When working on complex projects, it’s essential to keep your code organized, maintainable, and reusable. This is where modular programming comes into play. Python provides a powerful way to structure and organize your code through the use of modules and packages.

Path problems are some of the most common and annoying problems machine learning engineers face, especially when frequently switching between operating systems. There are so many different issues ways to have path problems that no post could cover them all, but in this post, I’ll try to provide some background on possible issues and how to resolve them.

Path problems are some of the most common and annoying problems machine learning engineers face, especially when frequently switching between operating systems. There are so many different issues ways to have path problems that no post could cover them all, but in this post, I’ll try to provide some background on possible issues and how to resolve them. Whether you’re running scripts, installing new software, or managing Python projects, understanding how these paths work will save you countless hours of troubleshooting and configuration.

The post aims to show how to create Jupyter environments and how to debug any issues. It also provides some commands that are good for general debugging.

The post aims to show how to create Jupyter environments and how to debug any issues. It also provides some commands that are good for general debugging.