A curated collection of my learning notes, deep dives, and technical writeups across AI Engineering, Machine Learning, Backend Development, System Design, and Computer Science.
This repository serves as my public engineering notebook—a place where I document concepts I study, design decisions I make, and lessons learned while building real-world software.
Unlike tutorial repositories, these notes focus on understanding how things work, not just how to use them.
I believe the best way to truly understand a concept is to explain it.
Every writeup in this repository is written after studying, experimenting, or implementing a topic in one of my projects.
The goal is to build a long-term knowledge base that is useful for:
- 📖 Revision
- 💼 Interview preparation
- 🚀 Project development
- 🤝 Sharing knowledge with the community
engineering-notes/
├── AI/
│ ├── embeddings.md
│ ├── vector-databases.md
│ ├── rag.md
│ ├── chunking.md
│ ├── prompt-engineering.md
│ └── agents.md
│
├── Machine-Learning/
│ ├── linear-regression.md
│ ├── gradient-descent.md
│ ├── logistic-regression.md
│ ├── decision-trees.md
│ └── neural-networks.md
│
├── Backend/
│ ├── authentication.md
│ ├── caching.md
│ ├── redis.md
│ ├── repository-pattern.md
│ └── dependency-injection.md
│
├── PostgreSQL/
│
├── Linux/
│
├── System-Design/
│
└── README.md
- Retrieval-Augmented Generation (RAG)
- Embeddings
- Vector Databases
- pgvector
- LangGraph
- AI Agents
- Prompt Engineering
- Semantic Search
- Hybrid Search
- Re-ranking
- Linear Regression
- Logistic Regression
- Gradient Descent
- Decision Trees
- Random Forest
- Support Vector Machines
- K-Means Clustering
- PCA
- Neural Networks
- Transformers
- REST APIs
- Authentication
- Authorization
- Repository Pattern
- Dependency Injection
- Redis
- Docker
- API Design
- Caching
- Performance Optimization
- PostgreSQL
- SQL
- Query Optimization
- Indexing
- Transactions
- ACID
- pgvector
- Shell Commands
- Bash
- File System
- Processes
- Permissions
- Networking
- Load Balancing
- API Gateway
- Caching
- Message Queues
- Scaling
- Database Sharding
- CAP Theorem
- Distributed Systems
Every note follows a consistent structure:
- 📌 Problem Statement
- 💡 Intuition
- ⚙️ How It Works
- 🧮 Mathematical Explanation (where applicable)
- 💻 Code Examples
- 🌍 Real-world Applications
- ⚖️ Advantages & Limitations
- ❓ Interview Questions
- 📚 References
This repository is not intended to be a collection of copied tutorials.
Every note reflects my own understanding after studying documentation, research papers, books, courses, or building real-world projects.
As my understanding evolves, these notes will continue to improve.
If you find an error, have suggestions, or would like to discuss a topic, feel free to open an issue or start a discussion.
Constructive feedback is always welcome.
If these notes help you learn something new, consider giving the repository a ⭐.