Have you found that your code works beautifully on a few dozen examples, but leaves you wondering how to spend the next couple of hours after you start looping through all of your data? Are you only familiar with Python, and wish there was a way to speed things up without subjecting yourself to learning C?

In this talk, you'll see some simple tricks, borrowed from linear algebra, which can give you significant performance gains in your Python code, and how you can implement these in NumPy. We'll start exploring an inefficient implementation of an algorithm that relies heavily on loops and lists. Throughout the talk, we'll iteratively replace bottlenecks with NumPy vectorized operations.

At each stage, you'll learn the linear algebra behind why these operations are more efficient so that you'll be able to utilize these concepts in your own code. You'll see how straightforward it can be to make your code many times faster, all without losing readability or needing to understand complex coding concepts.