Welcome to ml-experiments – a curated collection of small-scale machine learning experiments inspired by research papers across various subfields of ML.
Each sub-folder in this repository contains:
- 📄 The original paper that inspired the experiment
- 🧠 A short report summarizing key takeaways, results, and learnings
- ✏️ A brief note on what I implemented, tweaked, or explored
Due to limited compute resources, many of these are small-scale reproductions or simplified adaptations of the original work – but they aim to capture the core insights and mechanics behind cutting-edge ML ideas.
This repo is both a learning journal and a hands-on playground. By implementing ideas from research papers, I aim to:
- Deepen my understanding of state-of-the-art ML concepts
- Build a habit of reading and implementing academic work
- Share learnings with others in a structured, open way
Many of these experiments were done in collaboration with my twin brother, Shrenik – a fellow ML enthusiast and partner-in-crime for weekend hacks and coding sprints.
- 🧠 LLM Safety & Alignment
- 🤖 Model Architecture
- 🧩 Datasets & Instruction Tuning
- 📊 Inference & Scaling
- 🔍 Computer Vision
- 🧬 Functional Genomics
- ...and more to come!
These experiments are for educational purposes only. Most are not full replications due to time and compute constraints – they’re meant to convey the essence of the method rather than claim reproduction accuracy.
If you’re working on similar projects or just want to chat ML, feel free to reach out (daivik.d.patel@gmail.com) or drop an issue!
Thanks for stopping by!
Daivik