Personal deep learning study and research notes, organized for experimentation, review, and long-term reuse.
This repository is a personal workspace for self-directed deep learning study and research. It is intended to keep experiments, learning notes, implementation practice, and research references organized in one place.
- Build a deeper understanding of deep learning by implementing and testing ideas directly.
- Track lectures, papers, tutorials, and coding experiments in a single repository.
- Keep experiments and observations in a reproducible, reviewable format.
- Separate study notes from research notes as the project grows.
research_proj/
└── deep_learning/
├── CNN.ipynb
└── MLP.ipynb
deep_learning/CNN.ipynb: notes and experiments related to convolutional neural networks.deep_learning/MLP.ipynb: notes and experiments related to multilayer perceptrons.
- Keep notebooks focused on a single topic or experiment.
- Record the goal, core idea, results, and follow-up questions together.
- Prefer code that is easy to rerun and results that are easy to reproduce.
- Avoid committing large datasets, temporary files, or generated artifacts.
As the repository expands, a structure like the following will help keep it maintainable:
research_proj/
├── deep_learning/
├── notes/
├── experiments/
└── references/
Based on the current .gitignore, the repository excludes:
- PDF documents
- CSV files
- Excel spreadsheets
- Word documents
- macOS metadata files
- Choose a topic or research question.
- Create or update the relevant notebook under
deep_learning/. - Document the experiment setup, key results, and interpretation in the notebook.
- Keep the work reproducible by recording important parameters and assumptions.
- Move broader summaries or references into dedicated folders as the repository grows.
This repository is currently for personal learning and research. Add a license later if you plan to share or publish the work.