June meetup: Word Embeddings for NLP; Parallelising PyData With Dask

A good friend of mine and great speaker visiting Munich. So I decided to have an event around that date.

Word Embeddings for Natural Language Processing in Python - MARCO BONZANINI

Word embeddings are a family of Natural Language Processing (NLP) algorithms where words are mapped to vectors in low-dimensional space. The interest around word embeddings has been on the rise in the past few years, because these techniques have been driving important improvements in many NLP applications like text classification, sentiment analysis or machine translation.

In this talk we’ll describe the intuitions behind this family of algorithms, we’ll explore some of the Python tools that allow us to implement modern NLP applications and we’ll conclude with some practical considerations.

Marco is a freelance Data Scientist based in London. Backed by a PhD in Information Retrieval, he specialises in search applications and text analytics applications, and he has enjoyed working on a broad range of information management and data science projects. Active in the PyData community, he helps co-organising the PyData London meet-up. He's the author of "Mastering Social Media Mining with Python", published with PacktPub.

https://marcobonzanini.com/

Parallelizing Pandas, NumPy, and Scikit-Learn with Dask - Ian Stokes-Rees

Dask parallelizes Python libraries like NumPy, pandas, and scikit-learn, bringing a popular data science stack to the world of distributed computing. This talk will provide an overview of how Dask works then demonstrate Dask in action, showing how it can be used to transparently scale from a single thread of execution on a laptop to many parallel processes running across a cluster. Dask has been used on production systems with over 1000 cores and works well for computational units in the millisecond range.

Ian is a computational scientist at Continuum Analytics, the creators of Anaconda. He has been using Python for 15 years on scientific computing problems ranging from CERN's distributed computing infrastructure (PhD at Oxford) to protein structure studies (postdoc at Harvard Medical School). Today he helps organizations adopt Anaconda and Python as a strategic platform for data science.

P.S do not miss out updates on twitter - https://twitter.com/hack_ai and Slack (https://ai-hack.slack.com/)

We are still searching for an additional speaker. Please do send your proposals!