The rapid development of deep learning in recent years is largely due to the rapid increase in the scale of data. The availability of large amounts of data is revolutionary for model training by the ...

Active learning for multi-label classification addresses the challenge of labelling data in situations where each instance may belong to several overlapping categories. This paradigm aims to enhance ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Self-supervised models generate implicit labels from unstructured data rather than relying on labeled datasets for supervisory signals. Self-supervised learning (SSL), a transformative subset of ...

Forbes contributors publish independent expert analyses and insights. Greg Licholai writes and teaches about innovation in healthcare. Today software does not come with information labels that clearly ...

Machine learning and deep learning are both parts of artificial intelligence, but they work in different ways — like a smart student versus a super-specialised ...