The Final Artefact

Integrating Python-based machine learning models into iOS applications can be challenging, particularly when converting models into a Swift-compatible format. This example will demonstrate a simple image classification task using the Fashion-MNIST dataset and CoreML conversion tools. The goal is to illustrate the effort required to deploy small-to-medium complexity ML models within iOS applications. The demonstration is based on a Convolutional Neural Network (CNN) built with PyTorch, but the concepts apply broadly to other Python-based models as well. ...

One of the key benefits of modern editors like NVim, Vim, or Emacs is the rich plugin ecosystem. After years with Vim, I switched to NVim and was immediately impressed by its plugin landscape. The Lazy plugin manager—available for NVim > 0.8—quickly became my favourite. Lazy simplifies plugin discovery and management. It offers an intuitive interface and powerful commands that make it easy to add, remove, or update plugins. ...

Setting up a robust data science development environment takes time, and it’s a process that’s rarely ever finished. If you’re the type who likes to get the most out of your tools, you’ll likely enjoy tweaking, optimising, and layering your workspace with productivity enhancements. That might mean refining your Python setup to easily manage multiple language versions and dependencies, or expanding your text editor with plugins for linting, code suggestions, unit test execution, and CI/CD integration. ...

What are .dotfiles? Dotfiles are hidden configuration files on Unix-like systems. Their filenames start with a dot (.), making them hidden by default. They store preferences and settings for programs like shells, text editors, and version control systems. Many modern Linux applications follow the XDG Base Directory Specification. This guideline recommends placing user-specific configuration files in ~/.config (or $XDG_CONFIG_HOME). Using this standard reduces clutter in home directories and simplifies managing configurations across systems. ...

Why Swift? Data science is dominated by Python and R, with some usage of Julia, Scala, Java, and C++. While Swift may not be the most popular choice, it offers several notable benefits—especially for developers already invested in the Apple ecosystem. Key Advantages Performance Considerations As a compiled language, Swift often runs faster than languages like Python or R. This can be especially beneficial when handling large datasets or complex computations. ...

Vim provides a wide range of functions for file formatting, starting with basic features such as reindent. VimL Implementation Creating a function within Vim to process the file is likely the most straightforward approach. The primary purpose of this function is to pass the filename to an external command for formatting. Leveraging the rich ecosystem of Python formatting tools available from the command line allows the function to efficiently and consistently format files, tapping into powerful, pre-existing solutions for code aesthetics and standardization. In effect, the role of the function is to pass the filename to the call below: ...

Background For a computer, an image is collection of interpretable instructions that amount to a visual representation. Raster images are composed from using pixels, containing unique colours, whereas vector images keep track of points and equations that join them. In \(\LaTeX\) PGF/TikZ is used to generate vector graphics from algebraic descriptions. TikZ is mostly used to conveniently draw various scientific figures. ChatGPT is capable of generating computer code in majority of popular languages. I wanted to test how far I can get with ChatGPT generating illustrations using TikZ. ...

Types of Projects The term Python project can be somewhat misleading. While languages like Swift are designed for specific purposes such as generating macOS/iOS apps, components, and frameworks, Python is used in a much more versatile manner. A Python project might range from an analytical solution developed across multiple Jupyter notebooks to a standalone script querying a database API and extracting results to an application or package facilitating the deployment of models. Each of these projects has its own key usability requirements. For instance, if end-users will utilize our project through a command-line interface, we will focus on argument parsing and other elements facilitating user-friendly execution. ...

Most frequently, users tend to undertake common R installation and management tasks from within the R session. Frequently making use of commands, like install.packages, update.packages or old.packages to obtain or update packages or update/verify the existing packages. Those common tasks can also be accomplished via the GUI offered within RStudio, which provides an effortless mechanism for undertaking basic package management tasks. This is approach is usually sufficient for the vast majority of cases; however, there are some examples when working within REPL^[REPL stands for Read Eval Print Loop and is usually delivered in a form of an interactive shell. While working in Python users would commonly access REPLY by running python or ipython, more details.] to accomplish common installation tasks is not hugely convenient. ...

Big-O The purpose of this is not to provide yet another primer on the Big-O/$\Omega$/$\Theta$ notation but to share my enduring appreciation for working with R. I will introduce Big-O only briefly to provide context but I would refer all of those who are interested to the linked materials. What is Big-sth notation… When analysing functions, we may be interested in knowing how fast a function grows. For instance, for function \(T(n)=4n^2-2n+2\), after ignoring constants, we would say that \(T(n)\) grows at the order of \(n^2\). With respect to the Big-O notation we would write \(T(n)=O(n^2)\)^[MIT. (2021, December 9). Big O notation. Introduction to Computers and Programming. Retrieved December 26, 2021, from https://web.mit.edu/16.070/www/lecture/big_o.pdf]. Most commonly, in computer science, we would differentiate between Big O, Big Theta \((\Theta)\) and Big Omega \((\Omega)\). In a nutshell, the differences between those common notations can be summarised as follows: ...