Click to learn more about author Rosaria Silipo. Sometimes when you talk to data scientists, you get this vibe as if you’re talking to priests of an ancient religion. Obscure formulas, complex algorithms, a slang for the initiated, and on top of that, some new required script. If you get these vibes for all projects, […]
Automated Machine Learning: Just How Much?
Click to learn more about author Rosaria Silipo. An interview with three data scientists and guided automation experts There is currently a lot of talk about automated machine learning. There is also a high level of skepticism. I am here with data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz.. to ask a few questions […]
Fraud Detection Using a Neural Autoencoder
Click to learn more about author Rosaria Silipo. The co-authors of this column were Kathrin Melcher and Maarit Widmann. The Fraud Detection Problem Fraud detection belongs to the more general class of problems — the anomaly detection. Anomaly is a generic, not domain-specific, concept. It refers to any exceptional or unexpected event in the data, […]
Neural Machine Translation with Sequence to Sequence RNN
Click to learn more about author Rosaria Silipo. The co-authors of this column were Kathrin Melcher and Simon Schmid Automatic machine translation has been a popular subject for machine learning algorithms. After all, if machines can detect topics and understand texts, translation should be just the next step. Machine translation can be seen as a […]
Data Dimensionality Reduction in the Age of Machine Learning
Click to learn more about author Rosaria Silipo. Machine Learning is all the rage as companies try to make sense of the mountains of data they are collecting. Data is everywhere and proliferating at unprecedented speed. But, more data is not always better. In fact, large amounts of data can not only considerably slow down […]
Practicing Data Science
Click to learn more about author Rosaria Silipo. There are many declinations of Data Science projects: with or without labeled data; stopping at data wrangling or involving Machine Learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples at all of one of the classes; with […]