Lab
Papers, algorithms, and concept implementations I'm working through.
Notebooks and scripts where I break down ideas by hand before using libraries. For everything else, visit my GitHub.
- Notebook
Linear Regression from Scratch
NumPy implementation with gradient descent, compared against scikit-learn. Covers batch, stochastic, and mini-batch variants.
PythonNumPyMatplotlib - Code
Graph Traversal Algorithms
BFS, DFS, Dijkstra's, and A* in Python with step-by-step execution traces and complexity analysis.
Python - Notebook
Probability Distributions Visualized
Interactive exploration of common distributions — their PDFs, CDFs, moments, and relationships to one another.
PythonSciPyMatplotlib - Code
Feature Pipeline Skeleton
Minimal feature store setup with transformation logic, versioning, and a simple serving endpoint.
PythonFastAPISQLite - Notebook
K-Means Convergence Analysis
Implementing K-Means from scratch with visualizations of convergence behavior across different initializations.
PythonNumPyMatplotlib - Algorithm
Consistent Hashing Implementation
Building a consistent hashing ring with virtual nodes, used to understand distributed cache partitioning.
Python
Open to Discussion
If you're working through similar topics or have recommendations on resources or problems worth studying, I'm happy to talk.