I want to go deep with you on exactly how I work with stakeholders ahead of launching a hypothesis test. This step is crucial to make sure that once a test is done running, we’ll actually be able to analyze it. This includes: A well-defined hypothesis A solid test... Read more

In the mad rush to find new ways of teasing apart labeled data, we often forget about everything we can do with unsupervised learning. Unsupervised machine learning can be very powerful in its own right, and clustering is by far the most common expression of this group of problems.... Read more

Suppose that you wish to access values in an array of size n, but instead of having indexes in [0,n), you have arbitrary non-negative integers. This sort of problems happens when you build a hash table or other array-backed data structure. The naive approach to this problem is to... Read more

Our processors benefit from “SIMD” instructions. These instructions can operate on several values at once, thus greatly accelerating some algorithms. Earlier, I reported that you can multiply the speed of common (fast) random number generators such as PCG and xorshift128+ by a factor of three or four by vectorizing... Read more

As you encounter various data elements you should come across categorical data. Some individuals simply discard this data in their analysis or do not bring it into their models. That is certainly an option, however many times the categorical data represents information that we would typically want to bring in to... Read more

PyTorch is a flexible open source framework for Deep Learning experimentation. In this post, you will learn how to train PyTorch jobs on Amazon SageMaker. I’ll show you how to: build a custom Docker container for CPU and GPU training, pass parameters to a PyTorch script, save the trained model. As usual, you’ll find my code... Read more