Common Table Expressions vs Subqueries vs Views vs Temp Tables (Materialized vs Non-Materialized)

Optimizing SQL Performance - Advanced Techniques for Faster Processing

How Retrieval-Augmented Generation works under the hood, and a practical walkthrough building a RAG-based LLM Q&A app with LangChain, OpenAI, and Pinecone for a graduate ML course.

Building a RAG-Based LLM Q&A App - LangChain, OpenAI, and Pinecone

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.

Resources for Quant, Deep Learning & Large-Scale Data — A Living List

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.

Micrograd, Line by Line — How Andrej Karpathy's Tiny Autograd Engine Explains Neural Nets

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.

RegimeSense - Building an Adaptive Market Regime Detection System

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.

Building a Stock Screener Using Python and Streamlit

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

All Posts

Optimizing SQL Performance - Advanced Techniques for Faster Processing

Building a RAG-Based LLM Q&A App - LangChain, OpenAI, and Pinecone