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A running collection of the books, courses, hands-on practice, and tools I actually use for quantitative finance, deep learning, and working with large datasets. I keep adding to this as I find things worth sharing.

Notes on Andrej Karpathy's micrograd — a ~150-line autograd engine that builds backpropagation, gradient descent, and a full neural net (Value, Neuron, Layer, MLP) from first principles. Still the best free, intuitive explanation of how neural nets actually learn.

A deep dive into building a regime-aware trading system that uses Hidden Markov Models to classify market conditions and dynamically allocate across a strategy pool — from feature engineering to live paper trading.

A detailed walkthrough of how I built a dynamic and customizable Stocks Screener using Python, Streamlit, and real financial data. This project combines finance and data science principles to empower investment research through factor-based strategies.

A detailed exploration of ETL and ELT, their differences, evolution, and when to use each approach in modern data engineering.