#machine learning

Every month, we take a look back at our top ten most clicked topics. Last month was packed full of exciting news such as more info on Java 16 with its upcoming migration to Git and GitHub. Other top news include interviews on the programming language Julia, the visualization platform Grafana and the Node alternative Deno. In May, we also learned how to analyze big data using Java and saw C pass Java in the monthly TIOBE Index.

The financial sector is a late adopter of machine learning. Top applications include fraud detection, customer care, and risk hedging. See how the mass adoption of machine learning can apply even to the most conservative sectors.

In simple terms, AI helps companies by employing ML (machine learning) and problem-solving in the recruitment process. This way, this technology allows companies to find potential clients for a position.

Data science and machine learning in Jupyter Notebooks can lead to complicated code, making it hard to improve your projects. In this article, you will learn how to reduce complexity in your code, why it’s important to get your code out of Jupyter Notebooks as soon as possible, and how to keep your code clean.

BERT models in Danish, Swedish and Norwegian have been released by the Danish company BotXO. We spoke to Jens Dahl Møllerhøj, Lead Data Scientist at BotXO, to find out more. See how these open source models differ from Google’s multilanguage BERT model, what can make creating NLP models for Nordic languages difficult, and where these models can be used.

It is time to stop “collecting” the data into a central repository and start “connecting” to the data at the sources. A new architecture—logical data fabric—facilitates this approach by gaining a virtual view of the data.

Artificial intelligence could help us fight the coronavirus crisis. AI can, for example, already identify pneumonia on a CT scan in seconds with a high degree of accuracy. See what other things it can do to help flatten the curve.

TensorFlow 2.2.0 has been released, nearly four months after v2.1.0. The TensorFlow team has been keeping busy: In the latest version of the machine learning platform, they have added lots of new features and breaking changes, and have also fixed several bugs.

The initial release of Elyra AI Toolkit has been announced. This toolkit developed by IBM consists of different extensions for Jupyter Notebooks. They are designed to extend its capabilities for developing artifical intelligence models, so let’s take a closer look.

The initial release of the Julia programming language was eight years ago, in 2012. We spoke to the four creators of the language, Dr. Viral B. Shah, Dr. Jeff Bezanson, Stefan Karpinski and Prof. Alan Edelman, to find out whether Julia has been able to live up to their high expectations. They also went into detail about the various use cases Julia is applied to today, how the language compares to Python, and where it is headed in the future.

The second PyTorch release of the year has landed. PyTorch 1.5 brings some of the deep learning library’s previously experimental features into stable mode, including the C++ frontend API. Let’s take a closer look and see what that means—and what else has been updated in this release.

AI and ML technologies can make an impact by reducing emissions and maximizing production efficiency. The energy sector has lavish amounts of data to manage, AI is a perfect fit for this purpose. Let’s look at how machine learning can benefit the energy sector.

In this talk from the Machine Learning Conference, Kamil Kaczmarek and Jakub Czakon focus on practical guidelines and tips on how to set-up and maintain smooth collaboration in data science projects. Discover how to best track and collaborate in data science.

When artificial algorithms are biased, this can create unethical results, which in turn can lead to PR disasters for businesses. In this article, you will learn about three different types of AI bias – algorithmic, technical, and emergent – and what measures can be used to limit them.