Machine-Learning
Neural Networks: Building Intuition Beyond the Math
··2391 words·12 mins
AI/ML
Tutorials
Machine-Learning
Deep-Learning
Tutorial
Neural-Networks
Abstract
This piece explains neural networks from the intuition upward: what a neuron is, why layers help, how gradient descent changes weights, and why backpropagation is less mystical than it sounds. It is written for readers who want the concepts to feel concrete before diving deeper into the math.
5 ML Mistakes I Made So You Don't Have To
··2135 words·11 mins
AI/ML
Tutorials
Machine-Learning
Best-Practices
Lessons-Learned
Debugging
Abstract
This post is a checklist of failure modes that quietly ruin ML projects: bad data inspection, leakage, weak evaluation, class imbalance, and irreproducible experiments. The point is not to be dramatic about mistakes, but to make the debugging habits explicit before they cost days of work.
Build Your First ML Model: A No-BS Guide
··1603 words·8 mins
AI/ML
Tutorials
Machine-Learning
Tutorial
Hands-On
Pytorch
Beginner-Friendly
Abstract
This post walks through a first end-to-end ML workflow using a real image classification task. The goal is not just to train a model, but to build the habits that matter in practice: checking data, splitting correctly, choosing a simple baseline, and evaluating results without fooling yourself.