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My last post demonstrated a simple process for evaluating a set of face pairs to determine whether or not the two are blood relatives. Several snippets were breezed over like black boxes. Let us look at one of those snippets, a simple 1-liner: a Python Pandas feature I recently learned and now use frequently.
We next embark on a critical aspect of the library, STL Containers. By the end of this tutorial, you will understand the following.
How to choose correct containers and the importance of doing so.
Types of STL containers, along with their benefits and drawbacks.
The best data structure (i.e., container) choice for a given algorithm.
As a developer, especially in a vastly matured and stable SW language like C++, it is essential to acquire a solid foundation for its core library. This is the standard library, having been in continuous integration for major releases spanning from C++98 to C++23 .
After many requests, some planning, and getting the time to deliver a practical summary — I am pleased to share a guide that will allow you to start using parallel processing in your Python code!
TL;DR Parallel processing speeds up your code and handles many jobs to execute per sample of datasets. Following this paradigm, Multiprocessing in Python allows you to run multiple processes simultaneously. We discuss four vital components of the Multiprocessing package in Python: Process, Lock, Queue, and Pool.
Our perception of depth is essential for creating the 3D world around us. This knowledge has been prevalent for centuries, and one man who knew this well was Leonardo da Vinci. He used his expertise to help him create art that would be famous across history pieces such as "The Last Supper" or "Salvatore Schizzera. Technically, an understanding of binoculars traces back to 280 A.D. when Euclid realized our depth perception as humans focused on the same objects with two eyes. Still, today, stereo vision is quite an interesting problem. My goal was to learn the topic in-depth. Now, I transform my notes into a blog series.