Different projects

I’ve explored several intriguing projects, but Open Food Facts stands out for its mission to make food transparency universal. I’m most excited about contributing to a project that has a direct impact on consumer health and well-being. The idea of using my skills to help expand the database and improve the accuracy of nutritional information is motivating. to me and this project aligns with my interests in data analysis. I think I can truly have an impact contributing to this project

However, TensorFlow represents a different kind of challenge. I have not taken classes in ML, only really have briefly touched upon it in my past internship. The complexity of the codebase and the technical depth required for meaningful contributions is daunting to me. The biggest challenge I see with this project is navigating the steep learning curve and finding a niche where I can contribute effectively.

Personal Excitement

I am most excited about actually contributing to a community. I remember I answered in class my motivation for taking open source is to contribute to something bigger than myself. It remains true: I want to be apart of something that would impact and shape the world. The greatest challenge remains in my lack of confidence in my skills to be a meaningful contributor to very complex projects - particularly ML open source projects. TensorFlow represented this fear in that the contributors seem to be industry professionals. I believe in order to build ML models for TensorFlow, one would need an impressive foundation in math and ML.

I plan to start small, more manageable tasks such as bug fixes or documentation improvements. This approach will help me gradually build my understanding of the project’s architecture and contribution processes. Engaging with the community through forums and discussions will also be crucial for gaining insights and guidance from more experienced contributors. I think that is the only way to overcome this challenge and anxiety.