A List of numpy resources

NumPy stands for Numerical Python. It’s widely used in Linear Algebra applications and has become a de facto library for use in Machine Learning. It uses memory efficiently and is mostly implemented in C, thus is a very efficient option for numerical calculations (see more in Reference #3 by Sebastian Raschka). I’ve made a list of resources for the numpy library to help someone new or someone in need of a good reference later on. It was created by Travis Oliphant in 2005 (also the creator of SciPy). The package lives on GitHub (Link).

Listing. In no special order.

  1. Quickstart tutorial. From the scipy docs. Short, but good starting point. Ref
  2. Introduction to NumPy. A nice whole chapter on numpy by Jake VanderPlas. Ref
  3. Introduction to Numpy. A really nice quick tour as an appendix to a deep learning book by Sebastian Raschka Ref and as a Notebook
  4. Numerical Scientific Computing. Quick tour with exercises by Micheleen Harris. Notebook
  5. NumPy Practice. With some nice notes on Linear Algebra operations in numpy by Tirthajyoti Sarkar. Notebook

A listing of Linear Algebra resources to go along with this

  1. Stanford comprehensive Linear Algebra review document by Zico Kolter. Ref

  2. Linear Algebra Review (Andrew Ng).

    • Matrices and Vectors. Video
    • Addition And Scalar Multiplication. Video
    • Matrix Vector Multiplication. Video
    • Matrix-Matrix Multiplication. Video
    • Matrix Multiplication Properties. Video
    • Inverse And Transpose. Video
  3. Linear Algebra youtube channel by Khan Academy Videos
  4. Coding the Matrix. Book

Exercise: Follow along with these courses by doing things concurrently in numpy.

There are likely many more great resources out there so feel free to create an issue on this GitHub repo letting me know about yours or others.