Using the Monte Carlo method to visualize the behavior of observations with very large numbers of features — Think of a dataset, made of some number of observations, each observation having N features. If you convert all features to a numeric representation, you could say that each observation is a point in an N-dimensional space. When N is low, the relationships between points are just what you would…

How to train an off-the-shelf image segmentation model to respond to user feedback — This is Part 2 of a series of articles about training image segmentation models so that the models respond to user feedback and adjust their predictions based on the feedback (mouse clicks). In Part 1 we’ve described the general strategy for training off-the-shelf image segmentation models to respond to user…

How to train an off-the-shelf image segmentation model to respond to user feedback — (The second part of this series is here.) Image segmentation is a popular topic in machine learning, with many practical applications. Vision models can be trained to partition images based on some criteria, usually following the contour of a familiar type of object. When the model can not only segment…

Feature selection for scikit-learn models, for datasets with many features, using quantum processing — Feature selection is a vast topic in machine learning. When done correctly, it can help reduce overfitting, increase interpretability, reduce the computational burden, etc. Numerous techniques are used to perform feature selection. …

How to solve an optimization problem using quantum computing, compared to a traditional solution — There are many problems that require finding the maximum or minimum of a function, called the objective function, that depends on several (or many) variables; certain constraints may or may not need to be applied to the variables. The variables could be binary, integer, elements in sets, floating point, etc…

Let’s say your infrastructure is a fleet of M identical machines, each processing a single task at a time. Each new customer is placed in a queue waiting for a machine to become idle. You know how long each processing task takes to complete, but it’s not a fixed number…

Visualize periodic components using the Fourier transform. — You’re working with timeseries data, and you’re at the initial discovery stage. You’re trying to figure out all the different ways that the data varies in time. There may be trends, whereby the data is constantly increasing or decreasing in time. There may be completely random variations. …

How do neural networks really work? I will show you a complete example, written from scratch in Python, with all the math you need to completely understand the process. I will explain everything in plain English as well. You could just follow along, read just the text and still get…

You have a data sample. From it, you want to calculate a confidence interval for the population mean value. What’s the first thing you think about? It’s usually a t-test. But the t-test has several requirements, one of which is that the sampling distribution of the mean is nearly normal…