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2 materials found

Difficulty level: Intermediate  or Advanced 


VOSON Lab Code Blog

The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages.

Keywords: visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics

Resource type: tutorial, other

VOSON Lab Code Blog https://dresa.org.au/materials/voson-lab-code-blog The VOSON Lab Code Blog is a space to share methods, tips, examples and code. Blog posts provide techniques to construct and analyse networks from various API and other online data sources, using the VOSON open-source software and other R based packages. robert.ackland@anu.edu.au visualisation, Data analysis, data collections, R software, Social network analysis, social media data, Computational Social Science, quantitative, Text Analytics researcher support phd masters
Semi-Supervised Deep Learning

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...

Keywords: Deep learning, Machine learning, semi-supervised

Resource type: presentation, tutorial

Semi-Supervised Deep Learning https://dresa.org.au/materials/semi-supervised-deep-learning 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. This 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. datascienceplatform@monash.edu Deep learning, Machine learning, semi-supervised