Neural Network Rap Battles - Generating Hip Hop Lyrics with Vector Space Models
Tuesday, 18 October 2016 from 19:00 to 21:30 (HKT)
Google just announced a line-up of familiar devices - just made better, with AI. As all aspects of life are slowly mediated by machine learning, what’s left for us humans to do? Are creative endeavours the last hope for human supremecy over machines?
Karteek Addanki, Data Scientist Quant at Eclipse Trading, will have his neural networks face off with humans in a rap battle to the death. Can we use neural networks to surface lyrical patterns and use those to generate hip hop lyrics? In this talk, he shares his experiences, from scraping a hip hop database, setting up a ML pipeline, to what motivated his model choices. Come closer, form a circle around the battle entrants and see who comes up with the sickest verse!
19:00 - 19:15 : Data Science Community Welcome
19:15 - 20:15 : Neural Networks talk by Karteek Addanki, Data Science Quant at Eclipse Trading
20:15 - 20:30 : Q & A with Karteek Addanki
20:30 - 21:30 : Social hour, light refreshments provided.
Karteek Addanki works for Eclipse Trading as a Data Science Quantitative Analyst. He completed his PhD in Natural Language Processing at HKUST under the supervision of Dr. Dekai Wu. He is interested in Machine Learning, Natural Language Processing and Financial market modeling. He also enjoys building software and is passionate about writing efficient and reusable libraries.
19F, 299 Queen’s Road Central,
Hong Kong, Hong Kong
NOT 33 Des Voeux Road Central
Symbol & Key
Symbol & Key is a series of talks organised to support exchanges and foster connections in Hong Kong’s Data Science community. The talks are entry-level, but at the vanguard of the discipline, so it should be accessible to aspiring data scientists and experts alike.
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Symbol & Key
Symbol & Key is a series of talks organised to support exchanges and foster connections in Hong Kong's Data Science community. The talks are entry-level, but at the vanguard of the discipline, so it should be accessible to aspiring data scientists and experts alike.