Have you found that your machine learning 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? Are you confused by all the things you’re reading about vectorized operations, and want an easy way to understand them?
In this talk you'll see some simple tricks 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 by exploring an inefficient implementation of a machine learning algorithm that relies heavily on loops and lists. Throughout the talk, we'll iteratively replace bottlenecks in our code 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.
Dr. Jodie Burchell is the Developer Advocate in Data Science at JetBrains, and was previously a Lead Data Scientist at Verve Group Europe. She completed a PhD in clinical psychology and a postdoc in biostatistics, before leaving academia for a data science career. She has worked for 7 years as a data scientist in both Australia and Germany, developing a range of products including recommendation systems, analysis platforms, search engine improvements and audience profiling. She has held a broad range of responsibilities in her career, doing everything from data analytics to maintaining machine learning solutions in production. She is a long time content creator in data science, across conference and user group presentations, books, webinars, and posts on both her own and JetBrain's blogs.
