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.
Sometimes I find it useful to be able to run Python commands right from the command line (without entering a Python console). Here are some ways I’ve found it useful.
The most frequently used evaluation metric for object detection is “Average Precision (AP)”. But, despite the attempts of well-intentioned researchers to create a single metric for comparing models, no single metric is the right one for all the cases. Thus the landscape of metrics has become filled with small variations on the idea of average precision. This post aims to clarify those variations.
