julienokumu/Solving-ML-Papers
Attempts at solving machine learning papers(9/_)✅
This project offers clear, step-by-step implementations of foundational machine learning models like Mistral 7B, BERT, and LLaMA 2. It helps machine learning practitioners understand the underlying architecture and mechanics of these models. Input is the problem of understanding complex ML papers, and the output is working, simplified code examples.
No commits in the last 6 months.
Use this if you are a machine learning student or practitioner looking for practical, implemented examples of influential AI models to deepen your understanding.
Not ideal if you are looking for a plug-and-play solution to deploy an ML model for a specific business task, or if you need to compare model performance metrics.
Stars
10
Forks
2
Language
Python
License
—
Category
Last pushed
Jul 27, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/julienokumu/Solving-ML-Papers"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
SwanHubX/SwanLab
⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports...
mdsrqbl/omnihuman
AI model that understands text & humanoids.
stas00/ml-engineering
Machine Learning Engineering Open Book
labmlai/annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including...
analyticalrohit/AI-ML-Cheatsheets
All Stanford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine...