Compress the model for your edge device without losing the accuracy — As the popular cliché has it, data scientists spend 80% of their time preparing the data, and only 20% developing the models. While there might be some truth to it, we should never underestimate the effort needed for the remaining 20%. Choosing the architecture, training, fine-tuning, and evaluating the model…

The why, the how, and the when of feature selection, plus some handy tricks and tips — This article was first published on Neptune AI’s blog. Have you ever found yourself sitting in front of the screen wondering what kind of features will help your machine learning model learn its task best? I bet you have. Data preparation tends to consume vast amounts of data scientists’ and…

Improve the simple Bayesian classifier by releasing its naive assumption — Despite being very simple, naive Bayes classifiers tend to work decently in some real-world applications, famously document classification or spam filtering. They don’t need much training data and are very fast. As a result, they are often adopted as simple baselines for classification tasks. What many don’t know is that…

The BG/NBD model explained. — Knowing how much value each particular customer adds to your business is crucial, not least because it helps define the customer acquisition and retention budgets. Customer Lifetime Value models are a useful tool for gaining such insights. There seems to be, however, a lot of confusion about them, and apparently…

How to make probabilistic comparisons that embrace uncertainty — Comparing things is hard. Even more so, when the information the comparison is based on is only partial or uncertain. Beware those who offer single numbers which, they claim, harness and capture all the uncertainty. Luckily, Bayes’ got you covered. …

Always know what to expect from your data — This article was first published on Neptune AI’s blog. You are a part of a data science team at a product company. Your team has a number of machine learning models in place. Their outputs guide critical business decisions, as well as a couple of dashboards displaying important KPIs that…

Because GANs are not all you need — When you hear about computer-generated images, you probably think about deep fakes, the cats that don’t exist, or a horse turned zebra. And this, quite reasonably, brings up GANs to your mind. But even though these models have seen great success in generating realistic images, they come with their shortcomings…