{"id":15,"title":"Semi-Supervised Deep Learning","url":"https://doi.org/10.26180/14176805","description":"Modern deep neural networks require large amounts of labelled data to train. Obtaining the required labelled data is often an expensive and time consuming process. Semi-supervised deep learning involves the use of various creative techniques to train deep neural networks on partially labelled data. If successful, it allows better training of a model despite the limited amount of labelled data available.\r\n\r\nThis workshop is designed to be instructor led and covers various semi-supervised learning techniques available in the literature. The workshop consists of a lecture introducing at a high level a selection of techniques that are suitable for semi-supervised deep learning. We discuss how these techniques can be implemented and the underlying assumptions they require. The lecture is followed by a hands-on session where attendees implement a semi-supervised learning technique to train a neural network. We observe and discuss the changing performance and behaviour of the network as varying degrees of labelled and unlabelled data is provided to the network during training.","doi":"","licence":"CC-BY-4.0","contact":"datascienceplatform@monash.edu","keywords":["deep learning","semi-supervised","machine learning"],"remote_updated_date":null,"remote_created_date":null,"created_at":"2021-08-31T05:47:05.349Z","updated_at":"2021-09-07T10:36:45.295Z","content_provider_id":15,"target_audience":[],"authors":["Titus Tang"],"contributors":[],"subsets":[],"resource_type":[],"other_types":"","version":"","status":"active","date_created":null,"date_modified":null,"date_published":null,"prerequisites":"","syllabus":"","learning_objectives":"","fields":[],"scientific_topics":[],"operations":[]}