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Understanding TensorFlow
TensorFlow and Monetizing Intellectual Property | Stratechery
Google created a lot of headlines this week when they announced that they were open-sourcing their machine learning system called TensorFlow. Ben Thompson offered up his point of view on why this decision makes sense. He pointed out that machine learning is essentially about three things: a software system, a ton of data, and an infrastructure that can process that data. Google is clearly in a dominant position when it comes to the amount of data and the infrastructure. So, when it comes to the software system, Thompson asked: “Why not, then, leverage the collective knowledge of machine learning experts all over the world to make TensorFlow better? Why not make a move to ensure the machine learning experts of the future grow up with TensorFlow as the default? And why not ensure that the industry’s default machine learning system utilizes standards set in place by Google itself, with a design already suited for Google’s infrastructure?”
For more on TensorFlow:
- Why does TensorFlow matter? | Medium
- Introduction to TensorFlow | Stanford NLP Group
- How TensorFlow works | Bay Learn Keynote – Jeff Dean of Google Brain
- TensorFlow vs. Theano vs. Torch | Kenneth Tran of Microsoft Research
Big Data Meets Big Science
Big science problems, big data solutions | O’Reilly
O’Reilly looks at how the team at Lawrence Berkeley National Lab’s supercomputing center is tackling 10 of the biggest data analytics challenges, “ranging in scale from astronomical to organismal, from molecular all the way down to subatomic physics.” The research team, which is a part of the National Energy Research Scientific Computing Center (NERSC), recognizes that they have “state-of-the-art computational and storage resources to handle the logistics,” however they believe that “the real challenge is in determining scalable analytics methods and software frameworks.”
Data Games
How have video games influenced our view of data? | Open Data Science News
At a recent Boston Data Mining meetup, Gerard Dwan, of Knowledgent, presented a tour through a wide range of video games, from classics to new releases, to show how these video games influenced our methods and practices of data utilization.
Women in Data Science
Needed: More women in data science | Stanford News
Dan Stober reports on the inaugural Women in Data Science conference, which included 400 women from 80 companies, 30 academic institutions, and a number of national laboratories. You can watch the full slate of speakers here. Persis Drell, Dean of Stanford’s School of Engineering, pointed out that while diversity is currently the talk of Silicon Valley, why it is important often gets left out of the convesation. “When there is a difficult challenge to address, and our world is full of difficult challenges, we need a diversity of thought, a diversity of approaches, a diversity of styles to get to the solutions – and that’s why we need diverse teams.”
Jupyter Notebook Tricks – Part II
Building Interactive Dashboards with Jupyter – Part II | Domino
Last week the team at the Domino Data Lab shared a few advanced Jupyter notebook tricks. In Part II they walk through how to use interactive widgets to build interactive dashboards.
Data Science for Brick & Mortar
Machine Learning in Retail: Consumer Privacy implications | Fast Forward Labs
The team at Fast Forward Labs looks at how digital companies like Warby Parker, Rent the Runway, Gilt, and Etsy are exploring new ways to engage customers offline, and warns that even though they were “built upon testing every detail affecting their website performance,” it’s important that retailers “consider the consumer experience risks associated with new technologies” and physical retail.
Nine Years of Hacker News
Looking back at 9 years of Hacker News | Debarghya Das
Deedy Das analyzed nine years of Hacker News data to look at things like the most upvoted contributors, the most commonly upvoted words, domains with the highest average upvotes, and post volume. Das’ analysis (which used datasets that were curated by Google engineer, Felipe Hoffa), reveals insights into the trends the tech community has been talking and caring about over the last nine years.
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