Category: Learning R

Posts about learning R

ELIZA Chatbot in R: Build Yourself a Shrink


More and more companies use chatbots for engaging with their customers. Often the underlying technology is not too sophisticated, yet many people are stunned at how human-like those bots can appear. The earliest example of this was an early natural language processing (NLP) computer program called Eliza created 1966 at the MIT Artificial Intelligence Laboratory by Professor Joseph Weizenbaum.

Eliza was supposed to simulate a psychotherapist and was mainly created as a method to show the superficiality of communication between man and machine. Weizenbaum was surprised by the number of individuals who attributed human-like feelings to the computer program, including his own secretary!

If you want to build a simple Eliza-like chatbot yourself with R read on!
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Why Gradient Descent Works (and How To Animate 3D-Functions in R)


The workhorse of Machine Learning is Gradient Descent. If you want to understand how and why it works and, along the way, want to learn how to plot and animate 3D-functions in R read on!
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Logistic Regression as the Smallest Possible Neural Network


We already covered Neural Networks and Logistic Regression in this blog.

If you want to gain an even deeper understanding of the fascinating connection between those two popular machine learning techniques read on!
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xkcd Comics as a Minimal Example for Calling APIs, Downloading Files and Displaying PNG Images with R


xkcd webcomics is one of the institutions of the internet, especially for the nerd community. If you want to learn how to fetch JSON data from a REST API, download a file from the internet and display a PNG file in a ultra-simple example, read on!
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Create Bart Simpson Blackboard Memes with R


Everybody knows the Simpsons, everybody loves the Simpsons and everybody can laugh about Bart Simpson writing funny lines on the blackboard! If you want to create your own Bart Simpson Blackboard Meme Generator with R read on!
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The Central Limit Theorem (CLT): From Perfect Symmetry to the Normal Distribution


How can the Normal Distribution arise out of a completely symmetric set-up? The so-called Central Limit Theorem (CLT) is a fascinating example that demonstrates such behaviour. If you want to get some intuition on what lies at the core of many statistical tests, read on!
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“You Are Here”: Understanding How GPS Works


Last week, I showed you a method of how to find the fastest path from A to B: Finding the Shortest Path with Dijkstra’s Algorithm. To make use of that, we need a method to determine our position at any point in time.

For that matter, many devices use the so-called Global Positioning System (GPS). If you want to understand how it works and do some simple calculations in R, read on!
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3.84, or How to Detect BS (Fast)

In From Coin Tosses to p-Hacking: Make Statistics Significant Again! I explained the general principles behind statistical testing, here I will give you a simple method that you could use for quick calculations to check whether something fishy is going on (i.e. a fast statistical BS detector), so read on!
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Network Analysis: Who is the Most Important Influencer?

Networks are everywhere: traffic infrastructure and the internet come to mind, but networks are also in nature: food chains, protein-interaction networks, genetic interaction networks and of course neural networks which are being modelled by Artificial Neural Networks.

In this post, we will create a small network (also called graph mathematically) and ask some question about which is the “most important” node (also called vertex, pl. vertices). If you want to understand important concepts of network centrality and how to calculate those in R, read on!
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