Our Favorite Posts Of Last Week (Dec 16, 2018)

Natural Language Processing (NLP) Breakthrough Imagenet Moment has arrived

By Sebastian Ruder, Insight Centre. Big changes are underway in the world of Natural Language Processing (Natural Language Processing (NLP)).
Link: https://www.kdnuggets.com/2018/12/Natural Language Processing (NLP)-imagenet-moment.html
Word Count: 2880

How to Build a Google Data Studio Community Connector — Tutorial

What is Google Data Studio? Google Data Studio is a data dashboard and reporting tool which provides easy-to-read, shareable and customisable reports from a variety of data sources.
Link: https://www.impression.co.uk/blog/8178/build-google-data-studio-community-connector/
Word Count: 1733

facebookresearch/pytext

PyText is a deep-learning based Natural Language Processing (NLP) modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale.
Link: https://github.com/facebookresearch/pytext
Word Count: 373

pytext open source Natural Language Processing (NLP) framework pytorch

To make it easier to build and deploy natural language processing (Natural Language Processing (NLP)) systems, we are open-sourcing PyText, a modeling framework that blurs the boundaries between experimentation and large-scale deployment. PyText is a library built on PyTorch, our unified, open source deep learning framework.
Link: https://code.fb.com/ai-research/pytext-open-source-Natural Language Processing (NLP)-framework/
Word Count: 1575

Technical SEO in the wild: Real-world issues and fixes

A lot of what is written about technical search engine optimization is pure theory; ideal-world scenarios of how websites should interact with search engine crawlers and indexing systems. In the real world, things get messy.
Link: https://searchengineland.com/technical-seo-in-the-wild-real-world-issues-and-fixes-308578
Word Count: 1871

Neural Reading Comprehension and Beyond

Teaching machines to understand human language documents is one of the most elusive and long-standing challenges in Artificial Intelligence. This thesis tackles the problem of reading comprehension: how to build computer systems to read a passage of text and answer comprehension questions.
Link: https://purl.stanford.edu/gd576xb1833
Word Count: 313