#Machine Learning

Optimization is hard. Often, all that's known about a challenge is the desired outcome, but not the way to get there. In this post, we'll look into solving non-trivial problems automatically with the use of the hyperopt (...)

Controlling the quality of large volumes of training data is a complex and time-consuming task. In this post, we'll look into the problem of using noisy data in machine learning from an unconventional perspective - by ma (...)

Calibration is important, albeit often overlooked, aspect of training machine learning classifiers. It gives insight into model uncertainty, which can be later communicated to end-users or used in further processing of t (...)

Artificial Intelligence and Machine Learning are commonly used in advanced analysis of graphic materials. Autonomous cars, face recognition and detection of objects are some of many examples that those methods can be use (...)

The demand for machine learning has been growing constantly over the years. Despite more and more people getting interested in this field, the supply of experts seems to still be lagging behind. In this blogpost, we'll u (...)

Azure Cognitive Services help to build intelligent applications without having AI or data-science knowledge. What if you want to take it even further? Building a custom image classification model and fitting your unique (...)