Introduction Machine learning models, particularly in Natural Language Processing (NLP), are becoming increasingly powerful. Yet, they suffer from a critical limitation: they forget. When trained on new tasks or domains, models often lose their ability to perform previously learned tasks—a phenomenon known as catastrophic forgetting. This problem becomes more pressing as NLP systems are expected to evolve alongside the ever-changing nature of human language. In my recent bachelor’s thesis, I explored how to mitigate catastrophic forgetting in NLP through continual learning....

LoRA (Low-Rank Adaptation) LoRA, short for Low-Rank Adaptation, is one of the most popular parameter-efficient fine-tuning (PEFT) methods. It was first proposed by Hu et al., 2021, and has become a go-to technique when adapting large pretrained models to new tasks. Why do we need PEFT methods in the first place? Finetuning large language models in the traditional way—updating all of their billions of parameters—is simply too expensive in terms of compute, memory, and storage....

Build a Neural Network from Scratch In this post, we’ll walk through how to build a simple neural network from scratch using just NumPy. No high-level libraries like TensorFlow or PyTorch, just the fundamentals. What is a Neural Network? A neural network is a set of interconnected layers of simple computational units called neurons. Each neuron receives inputs and returns an output value. It does this by multiplying each input by a learned weight and adding adds a bias term....

I love working in the terminal, there’s nothing quite like it. Once you’ve set up your workflow, navigating your system becomes second nature. Flying through the filesystem, quickly editing files, batch-moving specific file types, writing quick bash scripts, or chaining commands together with pipes — it’s all possible without ever lifting your hands from the keyboard. Learning to use the terminal effectively might be the highest ROI you can make as a developer....

Introduction Programming is all about problem-solving, and sometimes the best way to improve is by challenging yourself. In my latest side project, I set out to write a 3D spinning cube entirely in C—without looking anything up. No tutorials, no external help—just raw problem-solving and deep diving into low-level programming. This project, hosted on GitHub (itsferguson/spinning-cube), was an opportunity to practice my C skills, experiment with complex linear algebra, and implement convex hull algorithms in a self-contained, pure C environment....