easy and uneasy riddles

[This article was first published on R – Xi’an’s Og, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. On 15 January, The Riddler had both a straightforward and … Read more

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Discover the link between math and data science problems

Randomness : Before we jump into Random Numbers, lets understand randomness and its importance. None of the future events in our daily and professional lives are absolutely certain; meeting your friends next weekend, call scheduled with your boss day after tomorrow, plans to go for jogging tomorrow early morning, nothing is certain. And when it … Read more

HoloLens 2 Industrial Edition brings mixed reality to regulated environments

It’s been an energizing first year, in-market, for HoloLens 2. We loved seeing the ways HoloLens 2 has empowered customers across industries to increase productivity and innovate with purpose. Lockheed Martin is using HoloLens 2 to build the Orion spacecraft and reduce touch labor—what used to require an eight-hour shift can now be completed in … Read more

Computer Vision Object Detection in R with YOLO Pre-trained Models

In this R Tutorial, We’ll learn how to perform a very popular Computer Vision task which is Object Detection in R with YOLO (pre-trained Models). For this we’re going to use the image.darknet package from https://github.com/bnosac. The good thing about this package is that it doesn’t require neither reticulate nor Python. It’s ported from the … Read more

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A Bayesian Take On Model Regularization

In this article, we explore how we can, and do, regularize and control the complexity of the models we learn through Bayesian prior beliefs. I’m currently reading “How We Learn” by Stanislas Dehaene. First off, I cannot recommend this book enough to anyone interested in learning, teaching, or AI. One of the main themes of … Read more

Cross Validation: K Fold vs Monte Carlo

Let us try and illustrate the difference in the two Cross-Validation techniques using the handwritten digits dataset. Instead of choosing between different models, we will use CV for hyperparameter tuning of k in the KNN(K Nearest Neighbor) model. For this example, we will subset the handwritten digits data to only contain digits 3 and 8. … Read more

NLP Basics: Data Mining Vs. Text Mining

Both are an essential step in data science, but they are different. Photo by Dan Counsell on Unsplash Data science is an interdisciplinary field. It is a combination of different fields working together to create something awesome. It has some maths, some statistics, a punch of programming, and not so little business. Because of this … Read more

Confessions of a DevOps Engineer: Sprints

How to improve your sprints and common rookie mistakes to avoid Photo by William Iven on Unsplash A Story On Sprint days we reflect on our accomplishments (or lack thereof) for the previous 2 weeks. It’s about 20 minutes before we jump on zoom and share our thoughts and reflect. I look at our board. … Read more

How to create a quick election data visualisation: Portuguese Presidential race 2021 as an example

Elections seem to spit out endless data, but you need to find the right angle and do it at speed, which explains why in this election we chose to focus on the candidate that came third Don’t worry I will translate this! (chart by Oliver Carrington & Joao Silva using Datawrapper) The first European election … Read more

Talking About Missing Data

If the probability of being missing is the same for all cases, then the data are said to be missing completely at random (MCAR). This effectively implies that causes of the missing data are unrelated to the data. We may consequently ignore many of the complexities that arise because data are missing, apart from the … Read more