Fine-Tuning Llama 3.2 3B on Medical QA: Week 4 - When Lower Loss Meant a Worse Model
What Happened This Week Week 3 produced a working fine-tuned model: one epoch, one dataset, a clear improvement over the base model. This week 4 was s…
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Flash Attention: what it does and why it matters
Flash Attention: what it does and why it matters Your training job is paying for an A100 at $3/hour. The loss is going down, gradients are flowing, an…
How to Become a Data Scientist in 2026
How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day …
The Machine Learning Engineering Series
Part 1: From Scratch to Systems . This machine learning series will be a real ride. It’s an interactive journey where I’ll be sharing and raising lots…
VLA or IL? A Controlled Dataset for Testing Whether Finetuning Turns Your VLA into a Fancy Imitation Learner
Motivation Robot manipulation is the ability of a robot to interact with and manipulate objects in the physical world, such as grasping objects, movin…
The Math Behind Neural Networks — Explained Like Nobody Did for Me 🧨
How does a neural network actually learn to be less wrong? Not the hand-wavy version. The real one. The one with the derivative, the chain rule, and t…
AlphaEvolve: Google DeepMind's Gemini-Powered Evolutionary Coding Agent
Inside AlphaEvolve: How Neural Networks and Evolutionary Algorithms Are Self-Optimizing Software For several years, the role of Artificial Intelligenc…
Handling Non-Stationary Time Series: Building a Probabilistic Engine with XGBoost & Python
If you have ever tried to apply Machine Learning to financial time series, you know the heartbreak of the "perfect backtest." You build a model, train…
Chain-of-Thought and Beyond: How LLMs Actually Learn to Reason
"The ability to reason step-by-step is not just a feature. It might be the difference between a language model that sounds intelligent and one that ac…
XGBoost: When Gradient Boosting Meets Regularization
1. The Problem It Solves Imagine you’re a loan officer at a bank. You have thousands of past loan applications with features like income, credit score…
Stop Guessing Which Weights Your Neural Network Actually Learned: Deterministic Initialization That Tracks Every Change
The Problem Nobody Talks About You've spent hours training your neural network. The loss converged, metrics look good, and you're ready to deploy. But…
The Paper That Taught Neural Networks to Learn Backwards
Last week I read the 1958 Rosenblatt paper. The one that started everything. The Perceptron, the first learning machine, the idea that memory lives in…
Removing PER From Rainbow DQN Set a New Snake AI World Record
Greetings all! Quick context: this is part of an ongoing series where I'm building Rainbow DQN one component at a time on Snake and measuring what eac…
🔬 AI for Scientific Discovery in the Real World: What Gemma 4 Changes The Moment AI Leaves the Chat Window
This is a submission for the Gemma 4 Challenge: Write About Gemma 4 🔬 AI for Scientific Discovery in the Real World: What Gemma 4 Changes The Moment A…
Did My LoRA Learn Tenacious Style—or Just Memorize Augmented Patterns?
In Week 11 Tenacious-Bench, we trained a LoRA adapter on Tenacious-style B2B sales emails using Supervised Fine-Tuning (SFT). We got a real performanc…