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Material for the 2020 fall edition of the course "Introduction to Python and Human Language Technologies" at BME AUT

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python_nlp_2020_fall

Material for the 2020 fall edition of the course "Introduction to Python and Human Language Technologies" at BME AUT

Lab solutions

We do not release the solutions for the lab exercises but we welcome contributions from students.

Contribution guidelines are the following:

  • Each solution must be a standalone .py file with a solution to a single exercise.
  • It must be named LABNUMBER_EXERCISENUMBER_NAMEOFFUNCTION.py. Lab01 exercises weren't numbered, you can skip the number in this case.
  • It must have a copyright notice with your name and email (see my example).
  • It must be submitted as a pull request to the master branch in the this repository. In order to submit pull requests, you need to fork this repository and push to your own fork. You can then submit a pull request to the this repository.
  • It must follow PEP8.
  • It must include and pass all tests for that particular exercise in the notebook.
  • It must have a main entry point (see the example and a separate function for the solution. You can use helper functions.
  • We will provide code review on Github (this is why it needs to be plain text, not a notebook). You are expected to answer our review and fix your mistakes. Accepted solutions will count as extra points towards your final grade.
  • Alternate solutions are fine as long as they significantly differ from other solutions (for example an iterative solution vs a recursive one). Please use a numbered suffix if you submit an alternative solutions. An example would be lab01_get_first_n_primes_2.py
  • We may change the directory and naming conventions if the number of solutions starts to get out of hand. We will update the guidelines accordingly.
  • Here is an example.

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Material for the 2020 fall edition of the course "Introduction to Python and Human Language Technologies" at BME AUT

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