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Symbolic vs statistical (deep and machine learning) approach in language understanding - big picture view and practical tips for data scientist and startups. Gniewosz Leliwa, Chief Science Officer, co-founder of fido.ai Left his academic career in Quantum Physics (Quantum Information Theory). The ULB Machine Learning Group (MLG) is a research unit of the Computer Science Department of the Faculty of Sciences, co-headed by Prof. Filtering 100s of megabytes to a few gigabytes of CSV data: RSQLite, queryBuildR, DT and Shiny.
Breeze is the core set of libraries for ScalaNLP, including linear algebra, numerical computing and optimization. Epic is a powerful, state-of-the-art, statistical parser for eight languages backed by a generic framework for building complex systems using structured prediction.

Puck is an insanely fast GPU-powered parser, built on the same grammars produced by the Berkeley Parser.
What are the benefits of using symbolic (rule-based) versus statistical (deep and machine learning) approach in Natural Language Understanding?
We organize and sponsor meetups in Mountain View about hardware, manufacturing and other tech topics. On a mid-range Nvidia GTX 680, it can parse over 400 sentences a second, or over half a million words per minute.
We will provide a big picture view on common approaches in science and industry with practical tips for data scientists and startups in the space of Natural Language Understanding.
We will also take a closer look at specific tasks in which each approach is exceeding and what could be achieved with the hybrid approach.

The activity of the group covers the areas of machine learning, computational modelling and statistics and their applications in data mining, simulation and time series prediction.
In October 2008, Tom Lenaerts joined MLG as a new academic, extending the group's expertise towards computational biology, evolutionary dynamics and complex systems research.

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