ML / DL / AI Notes

A lightweight collection of practical notes from my work and learning journey in:

• Machine Learning
• Deep Learning
• NLP / LLMs (RAG, embeddings, semantic search)

Topics (growing list)

• Model evaluation & validation (bias/variance, CV, metrics)
• Feature engineering patterns
• Hyperparameter optimization
• Neural networks fundamentals (TensorFlow / Keras)
• RAG building blocks and retrieval evaluation

How I use this repo

Short, structured notes with references, experiments, and reproducible snippets.

Status

🚧 This repo is updated progressively.