Hello World! Lately, I have been open sourcing some computer science resources that have been useful throughout my master and PhD studies. They have just been sitting in my Dropbox folder for quite some time and I think it’s time for other fellow researchers and programmers to benefit from it too.
Today, I am releasing my cheat-sheet of deep learning notations. They have been very useful for preparing papers, blogs, and presentations. I have uploaded it on GitHub in the form of a Jupyter notebook to make it more accessible to everyone. Initially, I just had it as a Latex file, which you can also easily obtain with the Jupyter notebook if you prefer it in that format. Below is a preview of the notebook.
The notebook contains useful notations widely used in deep learning papers and educational materials found online. I used similar notations as in the Deep Learning book written by Ian Goodfellow, Yoshua Bengio and Aaron Courville. I also provide sample code using PyTorch to show the type of data structures and concepts these notations may represent.
You can reuse the notations in this notebook to assist you in writing your research papers, presentations, and blogs. It’s also a good resource for reviewing important mathematical notations used widely in deep learning research and other related fields. I provide example code in PyTorch, but as an exercise, you can try generating similar code using Numpy or Tensorflow. (The code shouldn’t be too different.) Enjoy!
