Machine Learning - csen2025.org

Activation functions compared in neural networks.

Without a Nonlinearity the Whole Network Collapses to One Layer

I remember sitting in a windowless lab three years ago, watching a training loss curve flatten into a perfectly straight, useless line. I had spent forty-eight hours tuning hyperparameters, only to realize I had blindly defaulted to a Sigmoid function…

Diagram of backpropagation explained via chain rule.

The Chain Rule Applied Very Carefully and Very Often

I spent three years in academia watching brilliant students stare at chain-rule derivations until their eyes glazed over, all because they were taught that backpropagation is some mystical, impenetrable sorcery. I’ve seen too many tutorials treat it like a black…

Visualizing neural network fundamentals through functions.

A Stack of Simple Functions That Becomes a Complicated One

I spent three years in academia watching brilliant researchers build increasingly baroque mathematical proofs for things that could be explained with a simple diagram and a bit of intuition. It frustrates me how often people treat neural network fundamentals as…

Implementing class imbalance strategies via resampling.

Resampling Fixes the Metric and Sometimes Nothing Else

I remember sitting in a windowless server room during my first industry role, staring at a training log that boasted 99.4% accuracy while the model failed to catch a single actual fraud case. It was a gut punch. We had…

Calibration of probabilities in predictive modeling.

A Confident Model Is Not Necessarily a Correct One

I remember sitting in a windowless conference room three years ago, watching a lead researcher present a model that boasted a near-perfect accuracy score, only to watch our entire deployment strategy crumble because the system was fundamentally overconfident. We had…