Study Notes on Han's XYZ

NLP and LLM Study Note

tabularasa8931@gmail.com (Han) — Tue, 07 Oct 2025 00:00:00 +0000

NLP and LLM

👉 Repository: NLP Note

These are my working notes on NLP and large language models: intuitive math, minimal proofs, and practical recipes.

I welcome all comments and suggestions—and I’d be happy to improve and grow this note together with you.

Deep Statistical Learning

tabularasa8931@gmail.com (Han) — Sun, 07 Sep 2025 00:00:00 +0000

📖 Deep Statistical Learning

👉 Full repository: deep_statistical_learning

I started writing these notes during my master’s degree as a way to organize and clarify my understanding of machine learning and deep learning. Over time, the notes have grown into a comprehensive study resource that combines theoretical foundations with practical insights.

I enjoy organizing knowledge and writing things down, and these notes reflect that process — carefully breaking down concepts, connecting ideas, and recording them in a way that can be revisited and built upon.

DL System Design

tabularasa8931@gmail.com (Han) — Sun, 07 Sep 2025 00:00:00 +0000

🏗️ Deep Learning System Design

👉 Repository: DL System Design

This note is about the practical design of deep learning and LLM-based systems. It focuses on bridging research with production: covering concepts such as scalable architectures, model deployment, monitoring, and system reliability. The aim is to capture design patterns and lessons that make ML/DL services both effective and maintainable.

I welcome all comments and suggestions—and I’d be happy to improve and grow this note together with you.

Matrix Methods

tabularasa8931@gmail.com (Han) — Sun, 07 Sep 2025 00:00:00 +0000

📐 Matrix Methods

👉 Repository: Matrix Methods

This note collects my study materials on linear algebra and matrix methods, focusing on the concepts most relevant to machine learning and deep learning. It includes explanations, worked examples, and connections between theory and practical applications.

I welcome all comments and suggestions—and I’d be happy to improve and grow this note together with you.

Reinforcement Learning

tabularasa8931@gmail.com (Han) — Sun, 07 Sep 2025 00:00:00 +0000

I welcome all comments and suggestions—and I’d be happy to improve and grow this note together with you.

📘 Reinforcement Learning Notes

👉 You can check out the full notes here: reinforcement_learning_note

There are already a number of excellent tutorials and lectures on Reinforcement Learning (RL), but I often find that many of them do not provide enough detail or explanation of the formulas behind the concepts. In many cases, key ideas are assumed to be obvious or straightforward—which can be a challenge for someone like me, who has a weaker background in mathematics but still wants a comprehensive understanding of RL.

Statistics

tabularasa8931@gmail.com (Han) — Sun, 07 Sep 2025 00:00:00 +0000

📊 Statistics

👉 Repository: Statistics

This note collects my study materials on probability and statistics, with a focus on the foundations needed for data science, machine learning, and deep learning. It combines key definitions, derivations, and examples, aiming to make abstract ideas easier to understand and apply.

I welcome all comments and suggestions—and I’d be happy to improve and grow this note together with you.

Programming / Coding Note

tabularasa8931@gmail.com (Han) — Sat, 06 Sep 2025 00:00:00 +0000

💻 Coding Note

⚠️ This section is temporarily closed while I reorganize and refine the notes.
It will be updated and re-opened later.

This is a collection of tutorial-style study notes on programming. The aim is to make concepts clear, practical, and applicable, with examples that can be adapted to real coding tasks.

📚 Topics

Agile & Software Development — workflows, methodologies, and best practices
Algorithms & Computer Science — problem-solving patterns, data structures, and theory
Programming Languages — C, Go, Python, Rust, SQL
Machine Learning & Deep Learning — theory notes, implementations, and experiments
Natural Language Processing (NLP) — applied tutorials and coding exercises
DevOps & Linux — shell scripts, automation, system tools, and environment setup
Git & Vim — version control workflows and editor productivity
Regular Expressions (RegEx) — pattern matching and cheat sheets
Web Scraping — techniques and scripts for data collection
Reading Notes (ToReads, KS-Study) — study references and knowledge summaries