An overview of 11 probability distributions in Rust — TL;DR Probability theory is crucial in data science, particularly in random sampling. This article explores its principles and applications in Rust for solving statistical problems. It involves studying random events, their outcomes, and probability distributions, which quantify errors in estimates, etc. Moreover, this article delves into the likelihood of different events…

Exploring Rust’s Polars DataFrames, Aggregation Functions, and Beyond — TL;DR The Rust programming language has been making waves in the industry and is now gaining momentum within data science. Its remarkable speed and strong security features are highly sought after by developers who need to manage large datasets effectively. The Polars library takes full advantage of Rust’s capabilities, providing fast…

Exploring The World of Rust’s Polars, Series, and Beyond — TL;DR In the fast-paced world of data science, it can be daunting to keep up with new advancements. However, Rust has proven itself as a reliable and crucial tool in this industry. Presently, Polars is among the most popular libraries for manipulating data in Rust. This write-up acts as an intensive…

An overview of different Rust’s built-in data structures and a deep dive into the Ndarray library — TL;DR Rust has gained immense popularity as a programming language globally, and it’s not without reason. Additionally, when discussing data analysis specifically, Rust stands out from its peers with its exceptional capabilities in this field. The extensive library support coupled with robust tools makes Rust the preferred option for many professionals…

A Contextual Guide for Data Scientists and Analysts — TL;DR Rust stands out as a practical choice in data science due to its exceptional performance and persistent security features. While it may not possess all the bells and whistles that Python does, Rust offers outstanding efficiency when handling large datasets. Additionally, developers can use an array of libraries explicitly designed…

An overview of clustering algorithms, their use cases, and their advantages and disadvantages — “if you want to go quickly, go alone; if you want to go far, go together.” — African Proverb. Quick note: If you are reading this article through a chromium-based browser (e.g., Google Chrome, Chromium, Brave), the following TOC would work fine. However, it is not the case for other…

The following article explains various methods for computing distances and showing their instances in our daily lives. Additionally, it will introduce you to the pydist2 package. — “There is no Royal Road to Geometry.” — Euclid Quick note: Everything written and visualized has been created by the author unless it was specified. Illustrations and equations were generated using tools like Matplotlib, Tex, Scipy, Numpy and edited using GIMP.

⏳ In the following material, we are going to illustrate the mechanism of network sockets (TCP in particular) and build on top of that a face recognition app with a multithreaded server. — 👉 Socket Communication Sockets are a way of communication between processes. The main difference between it and other processes is that it can achieve inter-process communication between two or multiple different hosts, machines, nodes…(Different IP addresses). …

⛳️ The goal of this post is to build a face recognition app as well as introduce you to a very interesting and useful OpenCV library. I am pretty sure that this material will be useful for beginners. — Abstract In this article, we are gonna play around with some builtin methods used in computer vision and machine learning. To detect individuals, the Haar-like feature algorithm was chosen, and for face recognition, a method based on building histograms of local binary patterns already described in my previous work.