A while back I got interested in (baby) surnames. It is interesting to follow how friends name their newly born. There are various naming “strategies”, a) pick a name that has been used in previous generations but now uncommon (e.g. in Germany tha... [Read more...]

In my last posts (here and here) I explored how to use embeddings to represent categorical variables. Furthermore, I showed how to represent categorical variables with embeddings and add other variable to create a more complex model. Both posts focused... [Read more...]

In my last post, I explored how to use embeddings to represent categorical variables. Furthermore, I showed how to extract the embeddings weights to use them in another model. While the concept of embedding representation has been used in NLP for quit... [Read more...]

In order to stay up to date, I try to follow Jeremy Howard on a regular basis. In one of his recent videos, he shows how to use embeddings for categorical variables (e.g. weekdays). First off; what are embeddings? An embedding is a mapping of a catego... [Read more...]

One of the great features of R is the possibility to quickly access web-services. While some companies have the habit and policy to document their APIs, there is still a large chunk of undocumented but great web-services that help the regular data scie... [Read more...]

As most bloggers, I do check my analytics stats on a regular basis. What I do not really look at is social shares. For most blog posts traffic follows a clear pattern; 2 days of increased traffic, followed by a steady decrease to the base traffic volum... [Read more...]

Almost 2 years ago, I wrote a short post on the German data science market by analysing open position on the key job platforms; Stepstone, Monster and Indeed. Recently I received requests to update the post with fresh data. So here it comes. To add a ... [Read more...]

The Internet Movie Database (Imdb) is a great source to get information about movies. Keras provides access to some part of the cleaned dataset (e.g. for sentiment classification). While sentiment classification is an interesting topic, I wanted to see... [Read more...]

The last months, I have worked on brand logo detection in R with Keras. Starting with a model from scratch adding more data and using a pretrained model. The goal is to build a (deep) neural net that is able to identify brand logos in images. Just ... [Read more...]

In my last posts ([here](http://flovv.github.io/Logo_detection_deep_learning/ and here, I described how one can detect logos in images with R. The first results were promising and achieved a classification accuracy of ~50%. In this post i will detail h... [Read more...]

A month ago, I started playing with the deep learning framework Keras for R. As a use-case I picked logo detection in images. While the training of a net worked out fine, the results were mediocre. (Check out the full post to for details on the model a... [Read more...]

In my last post, I tried to train a deep neural net to detect brand logos in images. For that, I downloaded the Flickr27-dataset, containing 270 images of 27 different brands. As this dataset is rather small I turned to google image search and wrote a... [Read more...]

A year ago, I used Google’s Vision API to detect brand logos in images. Since then the DIY deep learning possibilities in R have vastly improved. With the release of Keras for R, one of the key deep learning frameworks is now available at your R fin... [Read more...]

When writing a blog, sooner or later, one measures and consequently tries to improve some kind of success metric. In my case; I have been tracking user behavior (visitors, bounce rate, pageviews, …) for slightly over a year with Google Analytics. Whi... [Read more...]

One of my favorite packages is Rmisc. The package includes the summarySE function which I use on a daily basis. The function provides a concise way to get a data frame with mean and standard errors of the mean. It is a great way in conjunction with ggp... [Read more...]

Predominately data science projects deal with descriptive statistics. The common theme (especially on this blog) is to gather a dataset, visualize and describe it. The toolset consists of a combination of machine learning, descriptive statistics and (... [Read more...]

Currently the news are filled with articles about the rise of machine intelligence, artificial intelligence and deep learning. For the average reader it seems that there was this single technical breakthrough that made AI possible. While I strongly bel... [Read more...]

A while back, I created the small package Roxford to access Microsoft’s Cognitive Services API in order to easily recognize objects in images. Back then Microsoft called the service “Project Oxford”, hence the name “Roxford”. Since then Micro... [Read more...]

Themes are an convenient way to give ggplot charts an individualized, sometimes stylish look. Most of the time, I rely on the ggthemes package and the Economist style. Last week colleagues asked me to change the look of my charts. We joked around and I... [Read more...]

In the last post, I mapped gas stations and gas prices in Germany. After posting it, I started to look at the dataset from a different angle. The starting question was; “How can I model gas prices? What are the influencing factors?” One well know... [Read more...]