Editor’s note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. While our implementations are decent enough, they are not optimized enough to work well on large corpora.
read full details at https://ift.tt/2qEbY5L
AskXML >>> from askxml import AskXML >>> conn = AskXML(‘file.xml’) # get an SQL cursor object >>> c = conn.cursor() >>> results = c.execute(“SELECT color FROM fruit WHERE _text LIKE ‘% kiwi'”) >>> for row in results.fetchall(): >>> print(row) [(‘green’), (‘dark green’)] # cleanup >>> c.
read full details at https://ift.tt/2H8SZHt
Craig talked last post about our project to reorganize our whole Python codebase. This entails a lot of architectural challenges – deciding where to put each file, prioritizing which files and classes to split, and so on – which Carter will talk about more in the final post of this series.
read full details at https://ift.tt/2EelTDT
This cheatsheet is intended to run down the typical steps performed when conducting a web application penetration test. I will break these steps down into sub-tasks and describe the tools I recommend using at each level.
read full details at https://ift.tt/2Gizuzg
The Google Ngram viewer is a fun/useful tool that uses Google’s vast trove of data scanned from books to plot word usage over time. Take, for example, the word Python (case sensitive):
read full details at https://ift.tt/2GkQ2U5
dejavu fits the unmet need of being a modern Web UI for Elasticsearch. Existing UIs were either built with a legacy UI and have left much to be desired from a Ux perspective or have been built with server side page rendering techniques (I am looking at you, Kibana).
read full details at https://ift.tt/2CMtrxR
In my previous blog post, I explored some of the early ways of word embeddings and their shortcomings. The purpose of this post is to explore one of the most widely used word representations in the natural language processing industry today.
read full details at http://ift.tt/2FQV66l