17
Sep
/
Innovative machine learning with the Apache Kafka Ecosystem
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 article we will in particular discuss new trends and innovations surrounding Apache Kafka and Machine Learning.
10
Sep
/
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.
6
Aug
Neural networks with PyTorch
PyTorch 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.
19
Jul
/
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.
9
Jul
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 the Machine Learning Conference 2019, Eric Reiss examines dark patterns in the ethics of machine learning and looks for a better answer than "My company won’t let me do that."
13
Jun
/
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.
13
Jun
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 made in significantly more classes.
6
May
Deep Learning with Java: Introduction to Deeplearning4j
Deep learning is now often considered to be the "holy grail" when it comes to developing intelligent systems. While fully automatic and autonomous machine learning is on the way, current solutions still require the understanding of a software developer or engineer. Deep learning, by contrast, is a sub-discipline of machine learning that promises deep-reaching learning success without human intervention and is oriented towards the function and operation of neural networks in the human brain.
24
Apr
Tutorial: Introduction to the R programming language
You already have some experience with SQL and are wondering how you could find solutions to problems in R? Then this article is just the thing you need! We’ll start with the basic elements of the language - with lots of specific sample code to help. Then we’ll take a look at how we can deal with data (this is where basic SQL skills are helpful, but not required). And last but not least, we'll look at use cases that can typically be solved with R.
1
Apr
Tutorial: An introduction to the Python programming language
When Guido van Rossum developed Python, he wanted to create a "simple" programming language that bypassed the vulnerabilities of other systems. Due to the simple syntax and sophisticated syntactic phrases, the language has become the standard for various scientific applications such as machine learning.