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.
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 most recent addition to Facebook AI’s open source projects is Blender, a state-of-the-art chatbot. What sets it apart from other chatbots is its novel blending of skills, including empathy and personality. Let’s take a closer look.
Accidents at sea happen all the time. Their costs – in terms of lives, money and environmental destruction – are huge. Wouldn’t it be great if they could be predicted and perhaps prevented? Dr. Yonit Hoffman’s Machine Learning Conference session discusses new ways of preventing sea accidents with the power of data science.
The deep learning company Dessa has open sourced Atlas, a deep learning platform. Though currently still in beta mode, it is designed to make running, evaluating and deploying deep learning projects easier. It works on macOS, Linux and Windows, and offers TensorBoard integration. Let’s take a closer look.
It’s not a secret that deep learning already made a revolution in several perception fields as vision, language and speech understanding and keeps pushing the frontiers. Take a tour of the final frontier for time series analysis in this advanced development session from the Machine Learning Conference.
PyTorch3D is the latest deep learning tool by Facebook AI. The open source tool is designed to integrate with PyTorch to make 3D deep learning easier. Along with it, the codebase of the 3D shape prediction method Mesh R-CNN, which was built with the help of PyTorch3D, has been released as well.
The AI research organization OpenAI has declared to standardize which deep learning framework to use in its projects, and the winner is PyTorch. Let’s take a closer look and see not only why OpenAI selected PyTorch, but also what benefits the standardization itself should offer.
ML Conference 2019 had lots of exciting talks and insights to offer. How can we make chatbots smarter and provide machines with abilities such as ethical values or emotional intelligence—and how can deep learning help save lives? We’ve collected 10 takeaways to share some highlights of our Berlin conference.
How can we detect deepfakes that have been created with deep learning methods such as GANs? Facebook, AWS and Microsoft joined forces to launch the Deepfake Detection Challenge (DFDC) that should encourage developers to research this issue. Winners can receive up to $500,000 USD.
In October, the machine learning library Apache SINGA graduated from the Apache Incubator. Apache SINGA was built with a focus on deep learning and its features make it suitable for a variety of use cases—from healthcare to industrial application. Its maintainers also have some further projects for deep learning in mind.
Honey bee colony assessment is usually carried out via the laborious manual task of counting and classifying comb cells. Beekeepers perform this task many times throughout the year to asses the colony’s strength and to track its development. As you can imagine, this is an extremely time-consuming and error-prone task.
Keras version 2.3.0 is here, and it is the last major multi-backend release. Going forward, users are recommended to switch their code over to tf.keras in TensorFlow 2.0. This release brings API changes and a few breaking changes. Have a look under the hood and see what it includes, as well as what the plans are going forward.
Most people reading this are likely familiar with machine learning and the relevant algorithms used to classify or predict outcomes based on data. However, it is important to understand that machine learning is not the answer to all problems. Given the usefulness of machine learning, it can be hard to accept that sometimes it is not the best solution to a problem.
