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From 0.76 to 0.78: A 48-Hour Hackathon Journey in Data Science
How a 48-hour team sprint on the PISA math-score challenge moved from R² 0.76 to 0.78, and what hardware limits and CatBoost tuning taught us.
Mathematics / Machine learning / Systems
9 articles · 3 projects
More signal.
Less noise.
Working through the mathematics of machine learning and the systems that turn theory into reliable software.
Writing9
All archives Mathematics
The Causal Inference Series : Part I
Part I of a causal inference series: Simpson's paradox, Pearl's ladder of causation, confounding, selection bias, and why correlation is not enough.
Machine Learning
CatBoost : Hype or Not ?
A deep dive into CatBoost's ordered target statistics, oblivious trees, and feature combinations, and why they beat XGBoost on categorical data.
Machine Learning
Byte Pair Encoding: Explained
How Byte Pair Encoding turns text into tokens, how vocabulary size and merge rules affect compression, and the traps to avoid when implementing BPE.
Mathematics
Neural Tangent Kernel: A theoretical view of Deep Learning Theory
A theoretical overview of the Neural Tangent Kernel and what it reveals about training dynamics and the behavior of wide neural networks.
Mathematics
Demystifying Linear Regression: From Basic Equations to Loss Functions
Linear regression from first principles: the model, its assumptions, and how squared error, absolute error, Huber loss, and likelihood shape what it learns.
Machine Learning
A Deep Dive into Attention Mechanisms: The Secret Ingredient of Modern NLP
A practical tour of attention mechanisms, from Bahdanau soft attention and hard attention to self-attention and multi-head transformers.
Machine Learning
Choosing the Right Metrics: How to Actually Know if Your Model is Any Good
A guide to choosing evaluation metrics for classification, regression, and language models, and why accuracy alone can hide a failing model.
Journal
Diving Deep: My First Public Project - Training a GPT-2 Level LLM from Scratch!
A from-scratch account of training a GPT-2-scale language model: the architecture, data pipeline, compute constraints, and lessons from my first public project.