Injecting random values during neural network training can help you get more from your categoricals Continue reading on Towards Data Science » Source link

Six simple techniques you can apply in real life The first part of this series discussed the growing need for problem-solving skills. As more of our world is automated with AI these skills are more important than ever. This article continues to outline practical strategies for solving any problem. Three more techniques are outlined below…

The data estate is evolving, and data quality management needs to evolve right along with it. Here are three common approaches and where the field is heading in the AI era. Image by author.Are they different words for the same thing? Unique approaches to the same problem? Something else entirely? And more importantly — do…

Simple stream processing using Python and tumbling windows Image by authorIn this tutorial, I want to show you how to downsample a stream of sensor data using only Python (and Redpanda as a message broker). The goal is to show you how simple stream processing can be, and that you don’t need a heavy-duty stream…

Guide to using the standardized syntax within Tidymodels to build and compare various models and metrics When I first learned to build models, eons ago, there were many approaches to constructing models across different packages with different parameter names. Then… Source link

How I became proficient in SQL to help land my first data science job Photo by Windows on UnsplashSo, you want to learn SQL? Well, in this article, I will run through how I learned SQL in just 2 weeks, which helped me land my first entry-level data science role. Source link

How Transformer architecture has been adapted to computer vision tasks Photo by kyler trautner on UnsplashIn 2017, the paper “Attention is all you need” [1] took the NLP research community by storm. Cited more than 100,000 times so far, its Transformer has become the cornerstone of most major NLP architectures nowadays. To learn about… …

Discussing the basic principles and methodology of data validation Photo by Vardan Papikyan on UnsplashAlthough it may not be the most glamorous aspect of data work, data validation is crucial to any data-related task. Data validation can be tedious. When we think of validation of data, what is the first thing that comes into your…

Choosing between frequentist and Bayesian approaches is the great debate of the last century, with a recent surge in Bayesian adoption in the sciences. Number of articles referring Bayesian statistics in sciencedirect.com (April 2024) — Graph by the authorWhat’s the difference? The philosophical difference is actually quite subtle, where some propose that the great bayesian…

A deep dive into biases in machine learning, with a focus on historical (or social) biases. Humans are biased. To anyone who has had to deal with bigoted individuals, unfair bosses, or oppressive systems — in other words, all of us — this is no surprise. We should thus welcome machine learning models which can…