Peter Song 
Jupyter notebooks are phenomenal for exploration, prototyping, and communicating results. But when it’s time to move your work to production, that beautifully interactive notebook becomes a liability. Production systems need reliable, testable, modular code that can run without a browser interface—and notebooks simply weren’t designed for that. I’ve seen too many teams struggle with this … Read more
PyTorch has revolutionized deep learning for images and text, but many data scientists still hesitate to use it for tabular data. The common wisdom suggests sticking with gradient boosting methods like XGBoost or LightGBM for structured data. While those tools are excellent, PyTorch offers unique advantages when you know the right tricks. With proper techniques, … Read more
A well-organized machine learning project can mean the difference between a smooth path to production and a chaotic mess that nobody wants to maintain. I’ve seen countless ML projects that started with brilliant ideas but became unmaintainable nightmares because of poor structure. The code worked—at least initially—but when it came time to add features, retrain … Read more
Multicollinearity is one of those statistical challenges that can quietly sabotage your regression models without you even realizing it. If you’ve ever built a predictive model only to find inexplicably large standard errors, wildly fluctuating coefficients, or coefficients with counterintuitive signs, multicollinearity might be the culprit. Understanding how to detect and solve this problem is … Read more
High cardinality categorical features represent one of the most challenging aspects of machine learning preprocessing, particularly when working with gradient boosting frameworks like XGBoost. These features, characterized by having hundreds or thousands of unique categories, can significantly impact model performance, training time, and memory consumption if not handled properly. Understanding how to effectively manage these … Read more
Fine-tuning TinyLlama opens up exciting possibilities for creating specialized AI models tailored to your specific needs, all while working within the constraints of consumer-grade hardware. TinyLlama, with its compact 1.1 billion parameters, strikes an ideal balance between capability and accessibility, making it the perfect candidate for custom fine-tuning projects. This comprehensive guide will walk you … Read more
The world of large language models has evolved dramatically over the past few years, but running them on your personal computer once seemed like a distant dream reserved for those with server-grade hardware. That’s changed with the emergence of “tiny” language models—compact yet capable AI systems that can run smoothly on everyday laptops and desktops. … Read more
Large language models have transformed how we interact with technology, powering everything from chatbots to content generation tools. But what exactly are these models, and how do they work? This guide breaks down the fundamentals of large language models in a way that’s accessible whether you’re a curious beginner or looking to deepen your technical … Read more
Retrieval-Augmented Generation (RAG) systems have become the go-to architecture for building LLM applications that need to reference specific knowledge bases, documents, or proprietary data. Unlike standalone language models that rely solely on their training data, RAG systems retrieve relevant information from external sources before generating responses. This added complexity means evaluation requires assessing not just … Read more
The transformer architecture revolutionized the field of deep learning when it was introduced in the seminal 2017 paper “Attention Is All You Need.” Understanding the layer architecture of transformers is essential for anyone working with modern natural language processing, computer vision, or any domain where these models have become dominant. At its core, the transformer’s … Read more
