https://github.com/mohammadvhossein/tf-gym

The TF Gym repo shares daily TensorFlow projects on ML/DL, including RL, providing educational resources for beginners and practical examples for experienced users with detailed instructions for applications like image classification and text generation.
https://github.com/mohammadvhossein/tf-gym

ai artificial-intelligence computer-vision data-science deep-learning iris kears machine-learning mnist modeling nlp poetry-generator tensorflow time-series translator

Last synced: 2 months ago
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The TF Gym repo shares daily TensorFlow projects on ML/DL, including RL, providing educational resources for beginners and practical examples for experienced users with detailed instructions for applications like image classification and text generation.

• Host: GitHub
• URL: https://github.com/mohammadvhossein/tf-gym
• Owner: MohammadvHossein
• License: mit
• Created: 2024-08-27T06:11:00.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-23T13:42:00.000Z (3 months ago)
• Last Synced: 2024-09-25T01:04:05.793Z (3 months ago)
• Topics: ai, artificial-intelligence, computer-vision, data-science, deep-learning, iris, kears, machine-learning, mnist, modeling, nlp, poetry-generator, tensorflow, time-series, translator
• Language: Python
• Homepage:
• Size: 2.96 MB
• Stars: 3
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## TF Gym

Welcome to the **TF Gym** repository! This repository is dedicated to publishing one project per day using TensorFlow. Each project will focus on a different aspect of machine learning and deep learning, providing a diverse set of examples and applications.

### Project Topics

The projects in this repository will cover a wide range of topics, including but not limited to:

- **Computer Vision**

- **Natural Language Processing**

- **Generative Adversarial Networks (GANs)**

- **Reinforcement Learning**

- **Time Series Analysis**

- **Anomaly Detection**

- **Recommendation Systems**

Each project will have its own README file, providing a detailed description of the problem, the approach used, and the results obtained. The code will be well-documented and easy to follow, making it suitable for beginners and experts alike.

## Getting Started

To get started with the projects in this repository, you will need to have TensorFlow installed. You can install it using pip:

```bash

pip install tensorflow

```

Depending on the project, you may also need to install additional libraries. The required dependencies will be listed in each project's README file.

## Contributing

If you would like to contribute to this repository by adding your own projects or improving existing ones, please follow these steps:

1. **Fork the repository**

2. **Create a new branch for your changes**

3. **Make your changes and commit them**

4. **Push your changes to your forked repository**

5. **Submit a pull request**

Please make sure that your code is well-documented and follows best practices for TensorFlow and Python.

## License

This repository is licensed under the **MIT License**. You are free to use, modify, and distribute the code in this repository for any purpose, as long as you include the original copyright and license notice. 

Refrences:


[1] https://github.com/tensorflow/agents


[2] https://www.tensorflow.org


[3] https://www.tensorflow.org/tutorials


[4] https://www.tensorflow.org/agents/tutorials/1_dqn_tutorial


[5] https://github.com/tensorflow/tensorflow/actions/runs/6240167953