Hi guys! To ease the dark evenings of the Finnish autumn, we are bringing you the next Turku.ai meetup! The next event is on the Nov 2 in Werstas, starting at 1800. Our host will be Silo AI.
The speakers for Nov 2 will be Prof. Filip Ginter (University of Turku) and Samuel Rönnqvist (Åbo Akademi University / THM University of Applied Sciences). See below for more details. Hope to see you all in Werstas!
"Forget words! Modelling language with neural networks character at a time" by Prof. Filip Ginter (University of Turku)
Filip is a top-of-the-line NLP researcher with thousands of citations. For someone working on the coolest technology (NLP, deep learning, LSTM, autoencoders, word embeddings and what not), he's a modest man: "I am a researcher at the Department of Information Technology, University of Turku. My research is in the area of natural language processing. See bionlp.utu.fi, the pages of our research group, for more details. I was born in 1978 in Ostrava, Czech Republic (Czechoslovakia back then) In 2001, I got a M.Sc. in computer science at the computer science department of VSB - Technical University Ostrava. My major subject was artificial intelligence. I gained a PhD in computer science in 2007. The title of my thesis is Towards Information Extraction in the Biomedical Domain: Methods and Resources. As of 2016, I am an assistant professor of language technology."
"A tutorial on deep learning for NLP" by Samuel Rönnqvist (Åbo Akademi University / THM University of Applied Sciences)
Samuel is a PhD candidate on his way to Germany for a postdoc, with interests ranging from networks to deep learning to visual analytics: "My research centers around text mining, NLP, machine learning and artificial intelligence, particularly knowledge-lean (resource-lean) approaches to text mining, applied toward the study of financial risk and stability among other areas. It builds upon and combines:
• natural language processing: distributional semantics, semantic role labeling, discourse parsing, topic modeling, sentiment analysis, etc.
• machine learning: neural networks, deep learning, NLP applications, data and dimension reduction
• visual analytics: interactive visualization of models to support reasoning
• network analysis: studying phenomena as complexly connected systems, quantitatively and visually
The areas of application include financial risk, business analytics, computational biology and history."