This blog explains t-SNE (t-Distributed Stochastic Neighbor Embedding) by a story of programmers joining forces with musicians to create the ultimate drum machine (if you are here just for the fun, you may start playing right away). Kyle McDonald, Manny Tan, and Yotam Mann experienced difficulties in pinpointing to what extent sounds are similar (ding, dong) … Continue reading t-SNE, the Ultimate Drum Machine and more
Geographical maps using Shazam Recognitions
Shazam is a mobile app that can be asked to identify a song by making it "listen"’ to a piece of music. Due to its immense popularity, the organization's name quickly turned into a verb used in regular conversation ("Do you know this song? Let's Shazam it."). A successful identification is referred to as a Shazam recognition. Shazam users can opt-in … Continue reading Geographical maps using Shazam Recognitions
Digitizing the Tour de France 2017 – II
A few weeks back, I gave some examples of how data, predictive analytics, and visualization are changing the Tour de France experience. Today, I came across another wonderful example visualizing the sequences of geospatial data (i.e., the movement) of the cyclists during the 11th stage of the Tour de France (blue dots). Moreover, the locations of … Continue reading Digitizing the Tour de France 2017 – II
Google Facets: Interactive Visualization for Everybody
Last week, Google released Facets, their new, open source visualization tool. Facets consists of two interfaces that allow users to investigate their data at different levels. Facets Overview provides users with a quick understanding of the distribution of values across the variables in their dataset. Overview is especially helpful in detecting unexpected values, missing values, unbalanced … Continue reading Google Facets: Interactive Visualization for Everybody
Generating images from scratch: Parallel Multiscale Autoregressive Density Estimation
A while ago, I blogged about this new algorithm, pix2code, which takes in pictures of graphical user interfaces and outputs the underlying code. Today, I discovered another fantastic algorithm, by Scott Reed and his colleagues at Google Deepmind. txt2pix would be a catchy name for this algorithm, as it can take in a fairly complex sentence (e.g., "a … Continue reading Generating images from scratch: Parallel Multiscale Autoregressive Density Estimation
Digitizing the Tour de France 2017
Combining two of my favorite things, Dimension Data elaborates on how they are using data, machine learning and predictive modeling to take the Tour de France experience to the next level in 2017. https://www.youtube.com/watch?v=FKx6jrEoEnQ Eurosport already jumped on the bandwagon in 2016 with some amazing visualizations of common Tour scenarios. Here is one on how to … Continue reading Digitizing the Tour de France 2017
pix2code: teaching AI to build apps
Last May, Tony Beltramelli of Ulzard Technologies presented his latest algorithm pix2code at the NIPS conference. Put simply, the algorithm looks at a picture of a graphical user interface (i.e., the layout of an app), and determines via an iterative process what the underlying code likely looks like. Please watchUlzard's pix2code demo video or the third-party summary at the … Continue reading pix2code: teaching AI to build apps
R learning: Neural Networks
Artificial neural networks (ANNs) are computing systems inspired by the human brain. They can teach themselves to do tasks, simply by considering examples of the tasks' outcome. For example, they can learn to identify images that contain cats by analyzing example images that have been tagged "cat" or "no cat". When given enough examples, the … Continue reading R learning: Neural Networks
Keras: Deep Learning in R or Python within 30 seconds
Keras is a high-level neural networks API that was developed to enabling fast experimentation with Deep Learning in both Python and R. According to its author Taylor Arnold: Being able to go from idea to result with the least possible delay is key to doing good research. The ideas behind deep learning are simple, so … Continue reading Keras: Deep Learning in R or Python within 30 seconds
TACIT: An open-source text analysis, crawling, and interpretation tool
Click here for the original PDF: TACIT 2017 The first programs for (scientific) text mining are already over 50 years old. More recent efforts, such as the Linguistic Inquiry Word Count (LIWC; Tausczik & Pennebaker, 2010), have greatly improved our text analytical capabilities. Moreover, several single-purpose programs have been developed, which also consider syntactic text structures … Continue reading TACIT: An open-source text analysis, crawling, and interpretation tool
