Machine learning is becoming an important tool in many industries and fields of science. However when it comes to research and development, there are less clear paths. Tech Talks brought this topic to us in their article, “The dos and don’ts of machine learning research.”
In a paper recently published on the arXiv preprint server, Michael Lones, Associate Professor in the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, provides a list of dos and don’ts for machine learning research.
We classify your data within the context of your particular industry and unique data situation, so that the enriched content can be used for analysis and a deeper understanding of your knowledge base. Although aimed at academic researchers, the paper’s guidelines are also useful for developers who are creating machine learning models for real-world applications.
Your company’s semantic model is a reflection of your business assets and their relationships to your industry and the world. Finding concepts takes artificial intelligence (AI) to drive through your document collection to identify and classify concepts and allows you to expand your semantic model to create meaning and relationships.
We help you turn unstructured content into usable data objects that allow you to work with your knowledge base in meaningful and valuable ways.
Melody K. Smith
Sponsored by Access Innovations, changing search to found.
