813K views · Sep 23, 2021 · 3:05:25 · 2 HN recommenders · 4 mentions · discussed 2021–2024
Lex Fridman interviews Travis Oliphant, creator of NumPy, SciPy and Anaconda, about scientific Python and its history. It runs about three hours.
Why Hacker News points people here: Recommended as a firsthand history of how Python became the language of scientific computing and ML.
“I highly recommend this bit of history from NumPy creator Travis Oliphant”HN comment, 2024
“I highly recommend this bit of history from NumPy creator Travis Oliphant”
History of why Python became the language of ML
Yup, I agree that Python composes like Unix does, which is a reason for its phenomenal growth: Narrow Waists Can Be Interior or Exterior: PyObject vs. Unix Files - link The appendix links to: Why is Python the language of machine learning? link In particular, I highly recommend this bit of history from NumPy creator Travis Oliphant: this video
“This recent interview with NumPy creator Travis Oliphant is great”
NumPy creator on competing abstractions and unifying them
Because Python has NumPy, which implements vectorized math on arrays and matrices. Machine learning algorithms are implemented naturally and efficiently with those primitives. PyTorch, TensorFlow, and I think every other machine learning framework in Python all use NumPy. JavaScript, Ruby, and Perl either don't have this abstraction at all, or they have much weaker versions of it, and many fewer scientific libraries. NumPy started in the early 2000's and continues to this day. It takes decades to build up this infrastructure! This recent interview with NumPy creator Travis Oliphant is great: this video He talks about how there were competing abstractions like "Numeric" and another library, and his goal with NumPy was to unify them. And how there are still some open design issues / regrets. There were multiple people in the nascent Python community who were tired of MATLAB, not just because it's proprietary, but because it's a weak and inefficient language for anything other than its scientific use cases. You won't have a good time trying to write a web app wrapper in MATLAB, for example. The much more recent Julia language is also inspired positively and negatively by MATLAB, and is very suitable for machine learning, though it doesn't have the decades of libraries that Python has. ----- The NumPy extension was in turn enabled by operator overloading in Python (which is actually a very C++ influenced mechanism). JavaScript doesn't have operator overloading; I'm pretty sure Perl
“gives a good overview of how python's scientific computing community developed.”
Overview of how Python's scientific computing community developed
This episode of the lex fridman podcast gives a good overview of how python's scientific computing community developed. this video
“They pronounce the "Py" as "pie".”
Interview with NumPy creator, mentions pronunciation
If in doubt, you can always check out the Lex Fridman podcast with Numpy creator Travis Oliphant: this video They pronounce the "Py" as "pie".
