#machine learning

Watch Chi Nhan Nguyen's Machine Learning Conference session

Deep probabilistic modelling with Pyro

The success of deep neural networks in diverse areas as image recognition and natural language processing has been outstanding in recent years. However, classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty.

The next step in ML/AI

The implication of AI & ML on the marketing landscape

Artificial intelligence and machine learning are changing how marketing campaigns work for many companies. They make it possible to transform datasets that allow better decision-making. However, it is not a magic solution, and there are still a few things you should not do when implementing AI and ML.

Machine learning is now seen as a silver bullet for solving all problems, but sometimes it is not the answer.

The Limitations of Machine Learning

Most people reading this are likely familiar with machine learning and the relevant algorithms used to classify or predict outcomes based on data. However, it is important to understand that machine learning is not the answer to all problems. Given the usefulness of machine learning, it can be hard to accept that sometimes it is not the best solution to a problem.

Watch Dr. Pieter Buteneers' Machine Learning Conference 2019 session

Chatbots suck

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.

Interview with Dr. Christian Hidber

Reinforcement learning: A gentle introduction and industrial application

Machine learning can be implemented in different ways, one of which is reinforcement learning. What exactly is reinforcement learning and how can we put it to use? Before the upcoming ML Conference, we spoke to Dr. Christian Hidber about the underlying ideas and challenges of reinforcement learning, and why it can be suited for application in an industrial setting.

Column - Stropek as a Service

AI-as-a-Service: AI cloud services should make the technology suitable for everyday use

In his column “Stropek as a Service”, SaaS expert Rainer Stropek talks about exciting aspects of the implementation, monetization and use of software as a service offerings. Today’s focus is on the connection between Software as a Service and Artificial Intelligence. How do SaaS projects benefit from Machine Learning?