https://github.com/danibcorr/papers-with-code
📃 This repository contains implementations of various scientific papers related to deep learning and machine learning. Most of the projects are developed using Keras and TensorFlow frameworks.
https://github.com/danibcorr/papers-with-code
deep-learning keras open-source papers papers-collection papers-with-code python tensorflow
Last synced: 4 months ago
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📃 This repository contains implementations of various scientific papers related to deep learning and machine learning. Most of the projects are developed using Keras and TensorFlow frameworks.
- Host: GitHub
- URL: https://github.com/danibcorr/papers-with-code
- Owner: danibcorr
- License: mit
- Created: 2025-01-05T22:02:35.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-03-12T17:54:59.000Z (4 months ago)
- Last Synced: 2025-03-12T18:38:43.602Z (4 months ago)
- Language: Python
- Homepage: https://danibcorr.github.io/papers-with-code/
- Size: 809 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Papers with Code
This repository contains implementations of various artificial intelligence papers and
publications. These implementations have either been developed by me or adapted from
other repositories with additional changes or improvements. The aim is to consolidate the
most relevant and interesting implementations in one place.
Most of the code is implemented using TensorFlow in conjunction with Keras, but there are
plans to include implementations using PyTorch as well.
## Features
In addition to categorizing implementations based on their respective publications, I've
strived to make this repository as simple and user-friendly as possible. Comprehensive
documentation is available, which you can access
[here](https://danibcorr.github.io/papers-with-code/).
## Contributing
We welcome contributions to this repository! To facilitate the process of setting up and
contributing, a `Makefile` is provided for quick installation. Please follow the steps
below to set up your environment:
1. **Clone the Repository**
Begin by cloning the repository to your local machine:
```bash
git clone https://github.com/your-username/repository-name.git
```
2. **Update System Packages and Install Dependencies**
For Linux users, update your system and install the necessary build tools. For
Ubuntu/Debian systems, use the following commands:
```bash
sudo apt-get update
sudo apt-get install build-essential make
```
3. **Create and Activate a Virtual Python Environment**
It is recommended to use a virtual environment to manage project dependencies. The
steps may vary depending on your operating system, so please refer to appropriate
online resources for guidance.
4. **Install Dependencies**
Once the virtual environment is activated, install all necessary dependencies by
executing:
```bash
make install
```
This command will automatically install all Python packages required for the project.
## License
This project is licensed under the MIT License.