░ ░▒▒▒▓▓█████▌░░ University of OXford & University of camBRiDGE ░░▐████▓▓▒▒▒░ ░
▄▄██▓▄ ▐███▌ ████▐███████▓▄ █████████▄ ▐███▌██████▓▄▄ ▄▄▓████▓ ▐████████
▄████████████ ▐███▌▐███▌▀████▌████▌ ▀███▓ ▀▀▀ ███▓▀▀████ ▄▓███▀ ▐███▓▀▀▀
▐███▀ ▀███▀███▓▄███▀ ▐███ ▄███▀▐███▓ ▄████▀▐███▌███▓ ███▓████ ▓███▌
███▌ ▐██▌ ▀████▀ ▓███████▄▄▐████████▓▄ ▐███▓███▓ ▐██████▌ ▀█████▓██████▓
████▄ ▄███▄▄███▀███▓ ████ ▀███▓███▓ ▀████▌████▐███▌ ███████▓ ▓██████▌
▐████████▀████▌ ███████▌ ▄███████▌ ████████▓███▌▄▄███▓▀█████▄▄████▓███▄▄▄▄▄
▀▀▓██▀▀ ▐████ ▐███▓████████▓▀████▌ ▐███████████████▓▀▀ ▀▀▓████▀▀░▓███████▌
...is a collective term for characteristics that the two institutions share.
█ JupyterCon 2017
█ New York, NY
▄
█ by O'Reilly Media, Inc.
█ Publisher: O'Reilly Media, Inc.
▓ Release Date: September 2017
▓ ISBN: 9781491985304
▒ Running time: 4:16:25
▒ Topic: Analytics
░ Scale: 74x200mb
░
■ Those Keynotes are available as
░ different resolutions, we decide to
░ release the keynotes as best 640x360,
░ everything else would be too big!
░
░ Lecture Date: 09/2017
░ Lecture Link: https://goo.gl/tPWFwy
░
:
| WeÆll make the Keynotes available here as soon as possible after
| they happen. Video of the sessions and tutorials will be available
| a few weeks after the end of the conference.
|
| JupyterCon New York 2017 was a powerful gathering of the data
| science and AI community that has gathered around Project Jupyter
| over the past 15 years. Its purpose: to share how the world's most
| data-driven organizations use Project Jupyter to analyze their
| data, share their insights, and create dynamic, reproducible data
| science. A sampling of the featured speakers at this inaugural
| conference included: Fernando Perez (Lawrence Berkeley National
| Laboratory); Lorena Barba (George Washington University); Demba Ba
| (Harvard University); Safia Abdalla (nteract); Brett Cannon
| (Microsoft); Jeremy Freeman (Chan Zuckerberg Initiative); Rachel
| Thomas (fast.ai); and Nadia Eghbal (GitHub). If you're looking for
| an understanding as to why Jupyter has become the new frontend for
| data science and AI, this video compilation of JupyterCon's live
| presentations will provide the insights you need.
|
| Highlights include:
|
| A front row view at each of JupyterCon's 55 sessions, 15 keynote
| addresses, and eight tutorials, including complete access to all
| of the conference's SOLD OUT talks, such as "Jupyter Widgets:
| Interactive controls for Jupyter" and "Deploying interactive
| Jupyter dashboards for visualizing hundreds of millions of data
| points in 30 lines of Python."
| Illuminating talks by 101 of the world's top Jupyter experts
| working at Harvard University, Continuum Analytics, UC Berkeley,
| DataScience.com, Bloomberg LLP, University of Pittsburgh, IBM,
| CUNY, Domino Data Lab, Cal Poly San Luis Obispo, Two Sigma,
| University of British Columbia, Civis Analytics, Columbia
| University, R-Brain, Lawrence Berkeley National Laboratory,
| Microsoft, and more.
| A thought provoking set of keynote addresses, such as Fernando
| Perez's (the progenitor of Jupyter) predictions for Project
| Jupyter's future; Wes McKinney's (Two Sigma Investment) vision for
| seamless computation and data sharing across languages; William
| Merchan (DataScience.com) outlining the three movements driving
| enterprise adoption of Jupyter; and Peter Wang (Continuum
| Analytics) describing the coevolution of Jupyter and Anaconda, two
| major players in the new open data science ecosystem.
| Four beginner-level Jupyter tutorials, including primers on using
| JupyterLab and creating JupyterLab extensions; using Jupyter
| widgets to build user interfaces; using Jupyter with visualization
| and analysis tools such as pandas, seaborn, Matplotlib, and
| scikit-learn; and an explanation of how Jupyter technology
| empowers research, engineering, and data science teams.
| Four intermediate level tutorials, including a walk-through of how
| UC Berkeley deployed a JupyterHub campus-wide for its students and
| researchers; a workflow for building interactive dashboards
| visualizing billions of data points interactively within a Jupyter
| notebook using just a few dozen lines of code; a detailed study on
| how to do interactive natural language processing using SpaCy,
| Jupyter Notebooks and tools like TensorFlow, NetworkX, and LIME;
| and a demonstration of high-level polyglot data analysis combining
| Jupyter notebooks with SQL, Python, and R.
| 21 sessions on breakthrough applications of Jupyter in research,
| education, and industry, including "Leveraging Jupyter to build an
| Excel-Python bridge," a talk about a democratizing data science
| Jupyter app that allows those who understand Excel, but know
| nothing about Python, to easily access machine learning models and
| advanced interactive visualizations; "A billion stars in the
| Jupyter Notebook," a talk about astronomers using the vaex and
| ipyvolume libraries to visualize and explore large, high-
| dimensional datasets within a Jupyter notebook; "Enhancing data
| journalism with Jupyter," a presentation that describes how
| Jupyter notebooks enable data journalism powered by input from the
| general public; and a talk on the Anaconda Project, an open source
| library that delivers lightweight, efficient encapsulation and
| portability of data science projects.
| Nine "reproducible research" sessions that examine the problems of
| sharing research results in an open and reproducible manner; five
| sessions about large scale JupyterHub deployments; five sessions
| on Jupyter extensions and customizations; four sessions on
| building the Jupyter community; four programmatic sessions; three
| core architecture sessions; three sessions on kernels that use the
| Jupyter architecture and clients for different programming
| languages; and three sessions on Jupyter subprojects and
| documentation.
| More than 65 hours of video presentations in total...and all of it
| on Safari.
|
,