How did Python become dominant in ML?


Python became dominant in machine learning for several reasons, as discussed by various experts on the Lex Fridman Podcast:

  1. Extensibility and Open Source: Python's extensibility allowed third-party packages to efficiently handle large arrays and operations. Guido van Rossum noted that scientists could create and share Python libraries, which facilitated a collaborative environment. Additionally, Python's open-source nature promoted widespread use and innovation, unlike more expensive, closed-source alternatives like MATLAB 1 2.

  2. Community and Ecosystem: The growth of essential libraries like NumPy, SciPy, TensorFlow, and PyTorch played a significant role. Travis Oliphant highlighted the importance of community contributions in making NumPy a dependency for Matplotlib, which catalyzed Python's adoption in scientific computing 3. Similarly, Chris Lattner emphasized Python's integration with notebooks and the creation of intuitive APIs, which enhanced its usability and appeal in machine learning 4.

    Python's Open Source Success

    Guido and Lex discuss the success of Python in the open source community, highlighting how its extensibility and affordability have allowed for the development of libraries and packages that solve specific problems. They contrast this with the expensive and closed-source MATLAB, which failed to spread in the same way. The egalitarian nature of Python's open source community is also emphasized, with a focus on community fostering rather than just development.

    Lex Fridman Podcast

    Guido van Rossum: Python and the Future of Programming | Lex Fridman Podcast #341
  3. Educational Adoption: Python's simplicity and readability made it an ideal language for teaching programming, which led to its widespread adoption in educational institutions. This created a new generation of programmers familiar with Python 4.

  4. Interoperability and Efficiency: Scientists could use Python to tie together existing high-performance C or Fortran libraries, making it a flexible tool for various applications. This interoperability allowed Python to be used across different domains, further solidifying its position 2.

  5. Industry Influence: The decision by significant projects and companies to adopt Python helped cement its status. For instance, Google's adoption of Python for TensorFlow and other internal libraries influenced many in the machine learning community to follow suit 1.

These factors collectively contributed to Python's rise as the dominant language in machine learning and data science.