A conversation with Laurent Mazare about how your choice of programming language interacts with the kind of work you do, and in particular about the tradeoffs between Python and OCaml when doing machine learning and data analysis. Ron and Laurent discuss the tradeoffs between working in a text editor and a Jupyter Notebook, the importance of visualization and interactivity, how tools and practices vary between language ecosystems, and how language features like borrow-checking in Rust and ref-counting in Swift and Python can make machine learning easier.
You can find the transcript for this episode along with links to things we discussed on our website.
