Spring

Deep neural networks have had huge success in recent years. However training them efficiently is still a research area. In this project we investigated a type of neural network, autoencoder, and its variations. Autoencoder is an ANN that tries to reconstruct its input as output. While doing this, it learns features of data as a byproduct. It is also used as a pre-training tool for deep neural networks.

Annotation is way of associating information with resources. Recently, World Wide Web Consortium (W3C) published annotation data model[23 February 2017]. Purpose of this project was to find a use case for
annotation data model. The use case was Crowdmap. CrowdMap is an application that allows users to collaboratively add meta information or relations between subjects. It is reminiscent of the detective walls with pictures of suspects and maps that are annotated via detectives.