Data Science, Machine Learning, Deep Learning, Reinforcement Learning, and Open Source are all closely related, each building on the foundation of the previous concept.
Data Science involves the use of statistical and computational methods to analyze and interpret complex data sets. Open-source tools and libraries like Python and R, along with their respective ecosystems of libraries, have been critical to the democratization of data science, making it easier and more accessible for researchers, businesses, and individuals to analyze and make sense of data.
Machine Learning is a subfield of data science that focuses on the development of algorithms and models that can learn from data and make predictions or decisions without being explicitly programmed. Open-source tools like scikit-learn, TensorFlow, and Keras have been instrumental in the growth and innovation of machine learning, making it easier for researchers and developers to build and train models, and deploy them into real-world applications.
Deep Learning is a subfield of machine learning that focuses on building and training neural networks, which are capable of learning and making predictions from very large and complex data sets. Open-source libraries like TensorFlow and PyTorch have been instrumental in the development and democratization of deep learning, providing a vast array of tools and algorithms for building and training neural networks, and enabling researchers and businesses to develop cutting-edge AI applications.
Reinforcement Learning is a subfield of machine learning that involves training agents to make decisions in an environment to maximize a reward signal. Open-source libraries like OpenAI's Gym and Stable Baselines have made it easier for researchers and developers to experiment with and develop reinforcement learning algorithms and models, and deploy them into real-world applications.
Open Source has played a critical role in the growth and success of data science, machine learning, deep learning, and reinforcement learning. The collaborative nature of open source allows for faster development and innovation, enables customization and extension of existing tools and libraries, and fosters a supportive community of users and contributors. Additionally, open source promotes transparency and accountability, making it easier for researchers and developers to share their work and reproduce their results, advancing the field as a whole. Overall, open source has been essential to the democratization of AI, enabling more people to participate in its development and benefit from its applications.
- If you have a new idea, create an issue and wait for it to be assigned before starting work on it.
- If you want to submit an improvement to an existing algorithm, create an issue describing your improvement in detail to facilitate analysis by others.
- Issues will be assigned on a first come, first serve basis, and you can ask to be assigned by commenting on the issue. It is preferred that you work only on the issue assigned to you.
- Check out the reference code in the relevant directory and write your own code basis that.
- Fork the repository (first time) and push your code to create a pull request (PR).
- All pull requests must be made from a branch. Create a separate branch for each issue you are working on and make the PR once it is complete.
- The files should be uploaded directly into the corresponding folder (eg. Machine Learning, Deep Learning, etc.) and linked in the README.md file of the respective folder. Do not create new folders within the concept folders unless instructed to do so.
- Please be courteous to the reviewers as they will always be polite to you.
You can find our Code of Conduct here.
This project follows the MIT License.