Jan
7,
2021
Real-time anomaly detection with Kafka and Isolation Forests
<div style="text-align: justify;">Anomalies - or outliers - are ubiquitous in data. Be it due to measurement errors of sensors, unexpected events in the environment or faulty behaviour of a machine. In many cases, it makes sense to detect such anomalies in real time in order to be able to react...
Dec
16,
2020
Let’s visualize the coronavirus pandemic
<div style="text-align: justify;">Since February, we have been inundated in the media with diagrams and graphics on the spread of the coronavirus. The data comes from freely accessible sources and can be used by everyone. But how do you turn the source data into a data set that can be used...
May
5,
2020
Explainability – a promising next step in scientific machine learning
With the emergence of deep neural networks, the question has arisen how machine learning models can be not only accurate but also explainable. In this article, you will learn more about explainability and what elements it consists of, and why we need expert knowledge to interpret machine learning results to...
Continuous Delivery for Machine Learning
In modern software development, we’ve grown to expect that new software features and enhancements will simply appear incrementally, on any given day. This applies to consumer applications such as mobile, web, and desktop apps, as well as modern enterprise software. We’re no longer tolerant of big, disruptive software deployments. ThoughtWorks...
Feb
11,
2020
Tutorial: Explainable Machine Learning with Python and SHAP
Machine learning algorithms can cause the “black box” problem, which means we don’t always know exactly what they are predicting. This may lead to unwanted consequences. In the following tutorial, Natalie Beyer will show you how to use the SHAP (SHapley Additive exPlanations) package in Python to get closer to...
Jan
28,
2020
Deep Learning: Not only in Python
Although there are powerful and comprehensive machine learning solutions for the JVM with frameworks such as DL4J, it may be necessary to use TensorFlow in practice. This can, for example, be the case if a certain algorithm exists only in a TensorFlow implementation and the effort to port the algorithm...
Nov
19,
2019
How Deep Learning helps protect honeybees
Honey bee colony assessment is usually carried out by manually counting and classifying comb cells. Thiago da Silva Alves explains in this interview how deep learning can help to accomplish this time-consuming and error-prone task.
Oct
29,
2019
Generative Adversarial Networks: “GANs can create new ‘realities’ that never existed”
<div style="text-align: justify;">Generative Adversarial Networks (GANs) have recently sparked an increasing amount of interest, as they can generate images of faces that look convincingly real. What else are they capable of, what risks could they pose in the long run, and what do they have in common with the emerging...
Oct
24,
2019
Machine Learning finds its way into .NET with .NET Core 3
.NET Core is not only including WPF and WinForms in the new open source implementation of .NET - Microsoft now wants to make machine learning usable for everyone. That's why machine learning is now making its way into .NET Core with the ML.NET Framework. In this series of articles, we'll...
Sep
25,
2019
“Tricking an autonomous vehicle into not recognizing a stop sign is an evasion attack”
As machine learning technologies become more prevalent, the risk of attacks continues to rise. Which types of attacks on ML systems exist, how do they work, and which is the most dangerous? ML Conference speaker <a href="https://mlconference.ai/speaker/david-glavas/" target="_blank" rel="noopener">David Glavas</a> answered our questions.