Turning his attention to the extremely time-consuming task of machine learning data preparation, Dr. James McCaffrey of Microsoft Research explains how to examine data files and how to identify and ...

With deep learning neural networks, unstructured data can be understood and applied to model training without any additional preparation or restructuring. As deep learning models have continued to ...

It’s no secret that deep learning lets data science practitioners reach new levels of accuracy with predictive models. However, one of the drawbacks of deep learning is it typically requires huge data ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More “Deep learning” has become a hot topic in the general rush to ...

thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to Emergen Research, which expects deep learning to become a $93 billion market by 2028.

While this artificial intelligence (AI) discipline was first conceived in the late 1950s, the recent jump into deep learning and other AI methods is fueled by the recent increase in hardware power, ...

For certain classes of problem, such as vision and natural language processing, the algorithms that are likely to work involve deep learning. There is no such thing as clean data in the wild.

This article explains how to programmatically normalize numeric data for use in a machine learning (ML) system such as a deep neural network classifier ... A reasonable rule of thumb is that data ...