📚 Tech Notes
Why this? I had this all in word docs when I was going through interviews, so figured I'd put it in here somewhere
It's a mix of Data Structures and Algorithms (DSA), System Design, Machine Learning concepts, and more.
It's a structured dump of stuff I've pulled out of old word files and tweaked into linkable artifacts
Highlights​
Some of the better displays on here:
- Typical reusable resources which are examples of architecture components built from ground up
- In memory implementations
- Distributed implementations
- In memory node + some manager and scaling
- Pregel Graph Processing and how I've used it in large analytics based product companies
- Video Recommendations which showcases a specific example of what's talked about in Generic Search Systems
- Spark Notes
- Outside of these it's mostly a dump of notes around DSA, Systems Design, Leetcode, and other junk
Architecture and Systems Design Examples​
This section focuses on building specific systems, often inspired by technical interviews or real-world use cases. Examples include URL shorteners, YouTube Search, Top K Heavy Hitters, and more.
Architecture Components​
To implement these systems, you'll find notes under Architecture Components about databases, messaging, calculations, and other essential components.
There's also an entire section on Typical Resources where we build all of the typical components from scratch such as:
- Distributed Key-Value Stores
- Front-end Load Balancers with Metadata
- Distributed Queues
- Distribued Logging
- etc..
- Some includes Kubernetes (K8s) and Terraform configurations to demonstrate how to create these systems natively
Data Structures & Algorithms​
This section contains:
- General Data Structures and Algorithms.
- Their implementations, time/space complexities, and use cases.
Other Concepts​
This section includes generic concepts that don't fit into DSA or System Design, such as:
- Parallel Training Designs.
- Pregel Graph Traversals.
These are useful but don't belong in the other categories.
Implementations​
A collection of generic implementations, including:
- A Key-Value Store with Transactions.
- A Balanced Binary Search Tree from Scratch.
- ML Feature Store Architecture Diagram.
Leetcode​
This is a dump of LC problems and comments
Arxiv​
These are just papers I've read marked up with highlighter - my old iPad died and brought these here
References​
These are some of the resources I frequently use and reference throughout this repo:
- Leetcode: Great for DSA and Systems Design problems. I probably used this the most.
- 3Blue1Brown: Amazing visual explanations for Linear Algebra, Probability, and Deep Neural Networks.
- Jay Alammar's Visual Transformer Paper: A fantastic resource for understanding Attention and Transformers.
- Yuan Meng Embeddings Post
- Useful for seeing the actual topological interpretations of embeddings, and why geomtric / metric space operations are allowed
- Google ML Rec Systems: Useful for search, recommendation systems, and embeddings.
- Hello Interview: Great for Systems Design interview preparation.
- Programiz: Excellent for DSA implementations and complexity analysis.
- [Medium Articles]
- [Spark]
- Spark Streaming
- Udemy AWS Solution Architect Course
- [Cassandra]
- [Jay Alammar Illustrated]
- LayerNorm
- Another persons blog - AmitNess
- TRYEXCEPT - ML Systems Design Interviews
- Succinct data structures
- Duke Stats 732 Paper on Choosing Priors
- Sebastian Raschka LLM Reading List
- Encord - Contrastive Learning
- MinHash
- MinHash + LSH
- Google Two Tower AI Blog
- Multi Model Blog
- Vinija Jain Blog