Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

Many scientific problems entail labeling data items with one of a given, finite set of classes based on features of the data items. For example, oncologists classify tumors as different known cancer ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

This webpage is a benchmark data set for keystroke dynamics. It is a supplement to the paper "Comparing Anomaly-Detection Algorithms for Keystroke Dynamics," by Kevin Killourhy and Roy Maxion, ...

The company is combining human-reviewed AI tools with interactive, gamified learning to improve safety training, incident ...

As energy companies push AI deeper into industrial operations, success increasingly depends on governance, trusted data, and ...

Tom Fenton moves from local AI concepts to hands-on tools for matching LLMs to hardware, running local chatbots with Ollama and benchmarking AI performance.

Prompt engineering tools help optimize AI-generated responses. Discover the best tools, compare features, and find the right ...

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of ...

LazyLLM is a low-code development tool for building multi-agent large language model applications. It assists developers in creating complex AI applications at very low costs and enables continuous ...

Built for More: Uniform Coverage, Higher Yields, and Confident Variant Characterization.

For example, AI applications to medical diagnosis should be regulated very differently from AI applications to self-driving cars. U.S. National Academies report on AI and the Future of Work, study ...