A couple of notebooks require to be run to see the results because
(pythreejs, vega, brython) or does not work at all because
it involves a server (bqplot).
Not covered by this presentation
Dig into building a Jupyter extension
From others presentations
The presentation which follows showed how to use
d3.js. It was amazingly easy and understandable:
Building Visualisations in d3.js for Python Programmers by Thomas Parslow.
The talk on software-carpentry was also quite interesting
as they developed strong experience in animating workshop.
Surprising to see so many initiatives to educate people on programming.
Some links taken from presentations:
The presentation by Nexedis was quite impressive. They introduced their
stack to process data mostly based on open source projects:
Fluentd: a software which collects and sends data
from your laptop. Acccording to the speaker (Jean-Paul Smets),
it loses 1 byte out of 10 millions,
even if you close your laptop at anytime.
Re6st: Resilient, Scalable, IPv6 Network,
find routes between two locations in Internet. According to the speaker, it is much more reliable
than standard routing which always takes the same paths.
It is like taking small roads instead of highways.
NEO is a distributed, redundant and scalable implementation of ZODB API.
NEO stands for Nexedi Enterprise Object.
Erp5: written in Python,
see Python Success Stories.
ERP5 is a full featured high end Open Source / Libre Software solution published under
GPL license and used for mission critical ERP / CRM / MRP / SCM / PDM applications
by industrial organisations and government agencies.
SlapOS is a decentralized Cloud Computing technology that can automate the
deployment and configuration of applications in a heterogeneous environment.
MariaDB is designed as a drop-in replacement of MySQL(R) with more
features, new storage engines, fewer bugs, and better performance.
Out-of-core NumPy arrays. ZBigArray
can cope with any size of data from any container (memory, file, data base, …)
and should work with sikit-learn (to be continued).
The most interesting part of the talk was about the way the company decided
to base their processes on a particular libraries, especially for Fluentd.
No connection but I heard the following in presentations.
Github added a new features which allows users to edit directly from the browser.
It is very useful to fix typos and documentation:
Editing files in your repository.
Somebody would to add
which makes it easy to call a server to run Python code from a web page,
kind of simplified notebook to build documentation
Sparse pairwise Markov model learning for anomaly detection in heterogeneous data.
The MIT proposes a pretrained CNN (Convolution Neural Network) for places: