Travis Oliphant: NumPy, SciPy, Anaconda, Python & Scientific Programming | Lex Fridman Podcast #224

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