Semi-supervised learning is a machine learning technique that combines supervised and unsupervised learning to leverage datasets that contain few labelled examples and many unlabelled examples.
In semi-supervised learning, unsupervised learning algorithms are used to extract relevant features and useful representations from the unlabeled data, and then these insights are used to improve the quality of the supervised learning model. The supervised learning model is trained on both the labelled data and the unlabelled data, allowing information from the unlabelled data to be exploited to improve the accuracy of the model.
Semi-supervised learning is particularly useful in applications where the collection of labelled data is costly or difficult, but where a large amount of unlabelled data is available. Semi-supervised learning has been shown to significantly improve the accuracy of machine learning models in speech recognition, computer vision and natural language processing applications.
We often wonder what examples of AI we can find in our environment, and the fact is that artificial intelligence is a concept that in English has [...]
Read More »Today we are going to talk about the generation of qualified leads for the acquisition of new customers through AI. At Gamco, we develop software based on [...]
Read More »Today, consumers of any type of product or service have become demanding. It has been a long time since they were served anything [...]
Read More »Artificial intelligence (AI) and machine learning (ML) are two of the most popular technologies used to build intelligent systems for the [...]
Read More »
