Forgetting the name of a useful library method, or some important options and parameters is a common problem between data scientists. In our post two weeks ago, we introduced some of the best python cheat sheets we found for basic data science and programming objectives. In this week’s post, we’re talking pro. Here we have some fantastic cheat sheets for anyone who is prone to forgetting a myriad of things you need to remember for working with these libraries, from method names and parameters to different ways to access your data and its properties. The list of our favorite small references for pro pythonista is as follows.

  • Deep Learning: We have selected deep learning cheatsheets for the three deep learning libraries dominating the Python world. For a reference to deep learning itself, you can check out the last section of this post.
    • Keras: You may have noticed that we mentioned a few Datacamp cheat sheets in this post and the former. Datacamp provides some of the best learning resources for so many areas of data science, and here’s another one: a brief but sufficient quick reference for doing neural networks using Python and Keras. You can download the pdf version of it from here:
    • Pytorch: The PyTorch documentation itself includes a very nice cheat sheet for this popular deep learning package. This online reference mostly includes code snippets and very small explanations, probably because the code is simply expressive. You can find this resource here:
    • Tensorflow: What makes this very short Tensorflow cheatsheet from ALTOROS so helpful is that it begins from the beginning: “what is TensorFlow?”!. It then briefly says everything you need for installation, and all the main methods and parameters you need to know. All of these in less than one page! You can download it from the link below:*dtOZSuYDonyyBvEULpJALw.png
  • Machine vision:
  • Natural Language Processing:
    • NLTK:  This cheat sheet is from the Cheatography website. If you have a moment, Cheatography is full of other useful cheatsheets and absolutely worth exploring. It’s a quick reference guide for basic (and advanced) natural language processing tasks in Python, using mostly NLTK library (the Natural Language Toolkit package), including POS tagging, lemmatizing, sentence parsing, and text classification. You can view and download this cheat sheet here: