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Description: We want to create a deep Neural Network that can automatically generate comments for code snippets passed to it. The motivation behind this is that in software development and maintenance, developers spend around 59% of their time on program comprehension activities. Having comments that are generated automatically will hopefully cut this time down. In order to do this we will combine the recent paper Code2Vec: Learning Distributed Representations of Code by Alon et al. with the paper Deep Code Comment Generation in order to make a better performing model using the newer Code2Vec encoding that was not used in the Deep Code Comment Generation paper. Dataset: The dataset that we will use is the same dataset used by the Deep Code Comment Generation paper, this is a dataset of more than 500,000 code snippets including comments. This also gives us a baseline against which to compare. Papers: Deep Code: https://xin-xia.github.io/publication/icpc182.pdf Code2Vec: https://arxiv.org/abs/1803.09473conversation_quality
deeplearning_project
Quarter-3, Deep Learning Projectcyberdata_analytics
big_data
In this lab we will put the concepts that are central to Supercomputing with Big Data in some practical context. We will analyze a large open data set and identify a way of processing it efficiently using Apache Spark and the Ama- zon Web Services (AWS). The data set in question is the GDELT 2.0 Global Knowledge Graph (GKG), which indexes persons, organizations, companies, locations, themes, and even emotions from live news reports in print, broad- cast and internet sources all over the world. We will use this data to construct a histogram of the topics that are most popular on a given day, hopefully giving us some interesting insights into the most important themes in recent history.Love Open Source and this site? Check out how you can help us