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."
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.
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.
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.
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.
How can AI be turned into a commodity – a cheap, easily available product, that is used by everyone? Will it even be possible to turn AI into a commodity at all? Dr. Pieter Buteneers (Robovision) adresses these questions in this keynote from ML Conference 2018 in Berlin.
If you cannot or do not want to build an AI project from scratch, you have countless choices of ready-made services. But what can you do if the finished services do not fit the project? Customizable AI and ML models in the cloud, which you can train with your own data, provide a remedy.
Deep Learning is all the hype these days, beating another record most every week but writing code for deep learning is not just coding – it really helps if you have a basic understanding of what’s going on beneath. In this session from last year’s ML Conference, Sigrid Keydana offers a quick lesson on deep learning, as well as some tips and tricks for developers who’d like to dip their toes into this topic.