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1. It's extremely easy. Before the so-called revolution and CS people trying to get into it in droves, it was a niche topic dominated by the lifelong-researcher types. They could not be bothered with complex code. Writing code should not get in the way. Then, Lisp dominated the ML/AI scene. Now Python does, for this reason, to some extent. Python being easy is also helpful for non-CS engineering and other science grads to learn quickly. 2. Python has a huge ecosystem. NumPy, SciPy, and now Tensorflow, PyTorch, JAX. These makes lives easier. 3. Python and its ecosystem is FOSS. Students, hobbyists can learn it for free. (Quick anecdote: my uni in India, a very reputed non-IIT one, with sub-optimal funding, two years ago switched to Python + ecosystem for Physics and CS courses- both major and minor. This switch happened directly from C. Before that, Fortran was used. MATLAB, SPSS, etc. was never an option for cash-starved Indian unis. This is pretty much the same all across India. And thus you get a huge talent pool already trained in Python that pass-outs from hard-to-get-into unis.) 4. Python being general purpose also helps vis-a-vis R. R is heavily constrained. You cannot do much in it. R is used in anaalysis and Data Science. I have never seen it being used in ML, DL or RL. You learn Python, you can do non-trivial file manipulation in it. Good luck doing that with R or MATLAB. 5. The amount of people who needs to write code that reaches the metal is very small. I never needed to look under the sheets. I spend my life writing PyTorch, fastai, and TFLite. A friend of mine doing PhD needed to write custom CUDA code and then a wrapper so that it could be accessed from Python. He said that it was a very horrible experience. But the number of such people too little to bring Julia to mainstream. Julia removes the "two-language problem", but most people never need to use anything besides Python. |