The purpose of this series of articles is to provide a complete guide (from data to predictions) to machine learning, for .NET developers in a .NET ecosystem, and that is possible now using Microsoft ML.NET and Jupyter Notebooks. Even more, you don’t have to be a data scientist to do machine learning.
Justin Reock, Chief Architect for OpenLogic at Perforce Software, describes what is software fuzzing and why it is needed. Justin is the author on a chapter about fuzzing in a new book from Perforce Software: “Accelerating Software Quality: Machine Learning & Artificial Intelligence in the Age of DevOps”.
Michael Larsen, Head of Customer Success & Academy at BotXO.ai, takes us through the essential steps of building a chatbot. A chatbot project should be easy to carry out, easy to maintain and edit, and bring results pretty quickly. It should meet the needs of the business and potential problems to solve, proper flow and content, and integrations.
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.