This repository contains implementations of research papers from scratch using PyTorch. I focus on understanding and reproducing key ideas from various papers, primarily in deep learning and machine learning.
I aim to implement approximately one paper per week, ensuring a balance between accuracy and efficiency while deepening my understanding of the concepts.
Each folder corresponds to a different paper and typically includes:
- Paper Summary: A brief overview of the paper's main contributions.
- Implementation: Code written in PyTorch to reproduce key results.
- Experiments & Results: Notebooks or scripts demonstrating the model's performance.
- References: Links to the original paper and any additional resources used.
- Python
- PyTorch