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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. 

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.

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.

It is well-known in the Developer Scene that there is no better machine learning language than Python. One of the reasons why this programming language is so popular is the fact that it has a huge collection of great libraries, that makes the life of a developer a lot easier.

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.

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.

ML Conference-Speaker Christoph Henkelmann says machine learning is basically nothing more than a numbers game. We’ve taken a closer look at what he means by that and and asked him to explain the principles of word processing from the point of view of a machine in more detail.

The countdown to the Machine Learning conference in Berlin keeps ticking. We spoke with ML conference speaker and ML6 head of Applied Research Xander Steenbrugge about the “black box problem” in neural networks. Catch more of AI expert Xander Steenbrugge during his keynote talk, session, and workshop.

Our first ML Conference will debut in December in Berlin. Until then, we’d like to give you a taste of what’s to come. We talked with, Dr. Katleen Gabriels, Assistant Professor at Eindhoven University of Technology about how algorithms influence our daily lives and why ethics are essential to the development of machine learning.