The Python numerical computation library called NumPy provides many linear algebra functions that may be useful as a machine learning practitioner. In this tutorial, you will discover the key functions for working with vectors and matrices that you may find useful as a machine learning practitioner. This is a cheat sheet and all examples are short and assume you are familiar with the operation being performed. You may want to bookmark this page for future reference. Linear Algebra Cheat Sheet for Machine Learning Photo by Christoph Landers, some rights reserved.

I;m rewriting a project to use Node.js. I;d like to keep using MySQL as the DB (even though I don;t mind rewriting the schema).

While participating in Jeremy Howard's excellent deep learning course I realized I was a little rusty on the prerequisites and my fuzziness was impacting my ability to understand concepts like backpropagation. I decided to put together a few wiki pages on these topics to improve my understanding. Here is a prettier version of my linear algebra page. In the context of deep learning, linear algebra is a mathematical toolbox that offers helpful techniques for manipulating groups of numbers simultaneously. It provides structures like vectors and matrices (spreadsheets) to hold these numbers and new rules for how to add, subtract, multiply, or divide them.