Digests » 9

this week's favorite

K-Means Clustering: All You Need to Know

In machine learning, we are often in the realm of “function approximation”. That is, we have a certain ground-truth (y) and associated variables (X) and our aim is to use identify a function to wrap our variables in that does a good job in approximating the ground-truth. This exercise in function approximation is also known as “supervised-learning”.  

Foundations of Machine Learning

Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals.

Intro to optimization in deep learning: Busting the myth about batch normalization

Recognize these people? If not, these people call themselves The Myth Busters. Heck, they've even got a show of their own on discovery channel where they try to live up to their name, trying to bust myths like whether you can cut a jail bar by repeatedly eroding it with a dental floss. (Warning: Do not try this during your sentence).

Reproducing Japanese Anime Styles With CartoonGAN AI

Anime has distinct aesthetics, and traditional manual transformation techniques for real world scenes require considerable expertise and expense, as artists must painstakingly draw lines and shade colours by hand to create high-quality scene reproductions.

The Beginner's Guide to Dimensionality Reduction

Explore the methods that data scientists use to visualize high-dimensional data.