Discovering Exoplanets with Deep Learning with Chris Shallue - TWiML Talk #117
Analysis
This article summarizes a podcast interview with Chris Shallue, a Google Brain Team engineer, about his project using deep learning to discover exoplanets. The interview details the process, from initial inspiration and collaboration with a Harvard astrophysicist to data sourcing, model building, and results. The article highlights the open-sourcing of the code and data, encouraging further exploration. The conversation covers the entire workflow, making it a valuable resource for those interested in applying deep learning to astrophysics. The article emphasizes the accessibility of the project by providing links to the source code and data.
Key Takeaways
- •The project demonstrates a successful application of deep learning in astrophysics.
- •The article highlights the importance of collaboration between different fields.
- •The open-sourcing of code and data promotes accessibility and further research.
“In our conversation, we walk through the entire process Chris followed to find these two exoplanets, including how he researched the domain as an outsider, how he sourced and processed his dataset, and how he built and evolved his models.”