Welcome to the Basic Deep Learning Repository! This repository serves as a collection of basic DL code examples to help you learn and understand Deep Learning.
This repository contains a variety of basic TensorFlow code examples and some deep learning models from Keras in a simple Jupyter notebook (ibynb) format designed to provide you with hands-on experience and understanding of concepts. Whether you are new to machine learning or already have some experience, these examples will help you explore and experiment with various deep learning models.
To run the code examples in this repository, you need to have TensorFlow and keras installed on your machine. You can install TensorFlow using pip:
pip install tensorflowYou can install Keras using pip:
pip install kerasFor detailed installation instructions and additional requirements, please refer to the official TensorFlow documentation.
Each code example is organized in its own directory and contains a standalone script or Jupyter Notebook. You can navigate through the repository and explore the different examples based on your learning goals and interests.
To run a specific code example, make sure you have TensorFlow and Keras installed and execute the script or open the Jupyter Notebook in your preferred environment.
Please note that these examples are intended for learning purposes, and you are encouraged to modify and experiment with the code to deepen your understanding of deep learning.
Contributions to this repository are welcome! If you would like to contribute a new code example, fix a bug, or improve the documentation, please follow these steps:
- Fork the repository.
- Create a new branch for your contribution.
- Make your changes and test thoroughly.
- Submit a pull request, describing your changes and the motivation behind them.
Please ensure that your contributions adhere to basic style and guidelines.
This repository is licensed under the MIT License. You are free to use, modify, and distribute the code examples for educational and personal purposes. However, please note that the code examples provided are without warranties or support.
If you use any code from this repository in your own projects, attribution is appreciated but not required.
Start exploring the fascinating world of deep learning with this repository! Feel free to reach out if you have any questions, feedback, or suggestions.
Happy learning!