Honey bee colony assessment is usually carried out via the laborious manual task of counting and classifying comb cells. Beekeepers perform this task many times throughout the year to asses the colony’s strength and to track its development. As you can imagine, this is an extremely time-consuming and error-prone task.
At Machine Learning Conference 2019 in Munich, Christoph Henkelmann gave a talk about TensorFlow training on the JVM. We recorded the whole thing, and now you can watch it here (including slides) to learn all about how to combine a TensorFlow model with Java.
Reinforcement learning learns complex processes autonomously. No big data sets with the “right” answers are needed; the algorithms learn by experimenting. By using reinforcement learning, robots learn to walk, beat the world champion in Go, or fly a helicopter.
In his talk at Machine Learning Conference 2019 in Munich, Oliver Zeigermann talked about how chess engines work. Game playing is a classic discipline of AI and had a major break through in the 90s when Deep Blue defeated Kasparov and arguably became the world’s best chess player.
Eric Reiss started working with user experience (UX) long before the term was even known. Over the past 40 years, he has encountered many issues that have disturbed him – from creating purposely addictive programs, sites, and apps, to the current zeitgeist for various design trends at the expense of basic usability.
Chatbots suck. Watch Dr. Pieter Buteneers break down how and why they suck, how they might be improved in the future, as well as some of the failures he and his team experienced and how they learned from them. This is the talk he gave at Machine Learning Conference 2019.
It’s here! Take a sneak peek at the upcoming sessions; there’s a lot for ML developers of all experience levels. Get ready to learn all about the latest innovations in machine learning at ML Conference 2019! Buy your tickets now and save big.
Machine learning may have all sorts of use cases, but forecasting? In honor of the upcoming ML Conference, we talked to Philipp Beer about how data scientists can utilize ML in statistical forecasting. We talk about the advantages and disadvantages of modern vs. classical methods, how can one decide between the two, and where should they turn when they need good predictions for their business KPIs.
