Python Crash Course to kickstart anything.

This guide is more inclined for using python in academia, in particular machine learning, computer vision and deep learning with small bits of neuroscience.

I am trying to make it super packed and short but clean and clear ( just like list comprehensions ) so it might go fast for beginners. I suggest you have a good understanding of python or any other object oriented language such as C++, Java etc.

Thank you and have fun!

1. Introduction, Release: 13th August, 2018.
2. Data structures,
3. Pythonic Auras:,
   • List Comprehensions
   • Closures
   • Iterators
   • Generators
   • Decorators
4. Classes,
   • Inheritance
   • Multiple Inheritance
   • super stuff.
   • Classes with the "class"
5. Remembering exceptions, etc,
6. Strategy Patterns for Academia.
7. Quick Projects
8. Libraries without the Librarian,
   • numpy
   • scipy
   • matplotlib
   • scipy

1. Your first neural network,
   • The initial math.
   • The initial neuroscience.
   • The initial code...
2. Advanced Stuff,
3. Libraries without the Librarian, Part II:
   • opencv
   • scikit-learn
   • tensorflow
   • keras
   • pytorch
   • pymc

You wanna contribute? Sure! Donate me some cash for a cup of tea.

You thought writing this?

Hhahhahhahahah. Okay sure, send a PR.

Unless noted, everything is under the MIT license.

This might actually become a book! Lol.