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.
Sep
17,
2019
Innovative machine learning with the Apache Kafka Ecosystem
<div style="text-align: justify;">Machine Learning (ML) allows applications to obtain hidden knowledge without the need to explicitly program what needs to be considered in the process of knowledge discovery. This way, unstructured data can be analyzed, image and speech recognition can be improved and well-informed decisions can be made. In this...
Sep
10,
2019
4 arguments to convince your boss
You can't see the forest for the trees anymore, and you need new inspirations urgently? Then ML Conference is the place to be. Connect with like-minded people, widen your horizon while gaining deep insights and practical knowledge of the latest trends and technologies.
Aug
6,
2019
Neural networks with PyTorch
<a href="https://pytorch.org/">PyTorch</a> is currently one of the most popular frameworks for the development and training of neural networks. It is characterized above all by its high flexibility and the ability to use standard Python debuggers. And you don’t have to compromise on the training performance.
Jul
19,
2019
How UX can demystify AI: “We need more than just technical transparency”
Can UX demystify AI? Ward Van Laer answers this question in his session at the ML Conference 2019. We invited him for an interview and asked him how to solve the black box problem in machine learning by merely improving the user experience.
Jul
9,
2019
The Ethics of AI – dealing with difficult choices in a non-binary world
In the field of machine learning, many ethical questions are taking on new meaning: On what basis does artificial intelligence make decisions? How can we avoid the transfer of social prejudices to machine learning models? What responsibility do developers have for the results of their algorithms? In his keynote from...
Jun
13,
2019
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...
Jun
13,
2019
Machine Learning with Python: Train your own image classification model with Keras and TensorFlow
Image classification models are intended to classify images into classes. We usually want to divide them into groups that reflect what objects are on a picture. For example, we can train an image classification model that can distinguish "dog" from "cat," but of course, even more complex classifications can be...