Optimizer Comparison Lab for SGD, Adam, and RMSProp

Added Jun 2025 3 design docs

SGD, Adam, and RMSProp appear in every deep learning tutorial as a dropdown choice, but the differences between them are dynamic — visible only in how training actually unfolds over time. This project builds a playground for racing the optimizers against each other on equal terms.

Working in Jupyter with Keras and TensorFlow, the intern trains identical copies of a simple neural network on a toy dataset, one per optimizer, and renders live loss and accuracy plots that update as training proceeds. The overlaid curves reveal each algorithm's temperament: plain SGD's steady but slow descent, RMSProp's adaptive pace, and Adam's typically rapid early progress. The intern extends the comparison by varying learning rates per optimizer, discovering that rankings between optimizers can flip when their settings change — a nuance most tutorials never mention.

The project replaces cargo-cult optimizer selection with observed understanding. The intern learns what adaptive gradient methods actually adapt, how to design fair model comparisons, and how to present training dynamics visually — foundations for principled experimentation in any deep learning role.

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