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Articles related to "search"


This is how you put the data in Data Science!

  • Google’s vertical search engines like Google Images and Google Scholar wouldn’t last long if no one used them, so their varieties tell you a little something about what people tend to look for on the internet.
  • (You know those invisibility potions don’t work, right?) You know that quality varies and it’s up to you to think critically about the source before you believe everything you read.
  • Data providers use schema.org to tell us there’s a dataset on their page and describe some metadata about it.
  • We let data providers use schema.org to tell us there’s a dataset on their page and describe some metadata about it.
  • Sharing data (without an intermediary telling you to get lost) means that people can find and provide great resources even if they’ve got niche tastes… or obscure high school websites.

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Investigating Differentiable Neural Architecture Search for Scientific Datasets

  • We will compare DARTS to random search (which is actually quite good, see the table below) and state-of-the-art, hand-designed architectures such as ResNet. Most NAS studies, including the original DARTS paper, report experimental results using standard image datasets such as CIFAR and ImageNet. However, we believe that deep learning shows promise for scientific studies including biology, medicine, chemistry, and various physical sciences.
  • If we examine the architecture weights and random search performance (below), we see that DARTS learned a much more sparse cell than on the Graphene task.
  • Here we found that continuous DARTS modestly outperforms ResNet. Examining architecture weights and random search performance (shown below), we see a similar story to Galaxy Zoo. From the random search performance plot, there appears to be some architectures that perform much better than others (again note the log scale).

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This is how you put the data in Data Science!

  • Google’s vertical search engines like Google Images and Google Scholar wouldn’t last long if no one used them, so their varieties tell you a little something about what people tend to look for on the internet.
  • (You know those invisibility potions don’t work, right?) You know that quality varies and it’s up to you to think critically about the source before you believe everything you read.
  • Data providers use schema.org to tell us there’s a dataset on their page and describe some metadata about it.
  • We let data providers use schema.org to tell us there’s a dataset on their page and describe some metadata about it.
  • Sharing data (without an intermediary telling you to get lost) means that people can find and provide great resources even if they’ve got niche tastes… or obscure high school websites.

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Hyperparameter Tuning Explained — Tuning Phases, Tuning…

  • Grid search is an approach where we start from preparing the sets of candidates hyperparameters, train the model for every single set of them, and select the best performing set of hyperparameters.
  • Repeat the random selection, model training, and evaluation by the designated number of times we want to search the hyperparameters.
  • The approaches we take in hyperparameter tuning would evolve over the phases in modeling, first starting with a smaller number of parameters with manual or grid search, and as the model gets better with effective features taking a look at more parameters with randomized search or Bayesian optimization, but there’s no fixed rule how we do.
  • We have to worry the data folding not to overfit the model, then it is a must to change the fold splits from hyperparameter tuning to model selection cross-validation.

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Hyperparameter Tuning Explained — Tuning Phases, Tuning…

  • Grid search is an approach where we start from preparing the sets of candidates hyperparameters, train the model for every single set of them, and select the best performing set of hyperparameters.
  • Repeat the random selection, model training, and evaluation by the designated number of times we want to search the hyperparameters.
  • The approaches we take in hyperparameter tuning would evolve over the phases in modeling, first starting with a smaller number of parameters with manual or grid search, and as the model gets better with effective features taking a look at more parameters with randomized search or Bayesian optimization, but there’s no fixed rule how we do.
  • We have to worry the data folding not to overfit the model, then it is a must to change the fold splits from hyperparameter tuning to model selection cross-validation.

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Uni: Query the Unicode database from the CLI, with good support for emojis

  • It includes full support for Unicode 12.1 (May 2019) with full Emoji support (a surprisingly large amount of emoji pickers don't deal with emoji sequences very well).
  • Doesn't support emojis sequences (e.g. MAN SHRUGGING is PERSON SHRUGGING + MAN, FIREFIGHTER is PERSON + FIRE TRUCK, etc); quite slow for a CLI program (emoj smiling takes 1.8s on my system, sometimes a lot longer), search results are pretty bad (shrug returns unamused face, thinking face, eyes, confused face, neutral face, tears of joy, and expressionless face ...
  • Grouping could be better, doesn't support emojis sequences, only interactive TUI, feels kinda slow-ish especially when searching.
  • Uses Gnome interface/window decorations and won't work well with other WMs, doesn't deal with emoji sequences, I don't like the grouping/ordering it uses, requires two clicks to copy a character.
  • Only works in Firefox; takes a tad too long to open; doesn't support skin tones.

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Google Gives Feds 1,500 Phone Locations in Unprecedented ‘Geofence’ Search

  • To investigators, this kind of “geofence” demand is useful, allowing them to go through the data trove provided by Google, look for devices of interest such as a known suspect’s phone and ask for more personal information on the user of that mobile.
  • The police then look through the list, decide which devices are of interest to the investigation and ask for subscriber information that includes more detailed data such as name, email address, when they signed up to Google services and which ones they used.
  • Forbes obtained another search warrant that indicates Google is trying to fight back against overly broad government requests, but still appears to be handing over innocent people’s information as well as legitimate suspect data.
  • The government asked for personal details of the individual users for all six, which the tech giant duly provided, including name, email and other Google account use data.

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Everything you need to know about e-girls and e-boys, teen gamers who have emerged as the antithesis of Instagram influencers

  • The e-girl's emergence this year has corresponded with the rise of TikTok, the short-form video app Generation Z turns to for its latest viral memes and internet entertainment.
  • The e-girl (and e-boy) are just the latest iteration of mainstream counterculture, similar to the emo and scene kids who posted grainy pictures on Tumblr in the 2000s.
  • These e-teens live on the internet and are fluent in the latest video games, and their goal is to push the boundaries, in spite of what parents and older generations may think.
  • The popularity of e-girls and e-boys has become such a staple of 2019 culture that they were among the most popular Google search terms for fashion- and outfit-related queries, according to Google's annual "Year in Search" report.
  • Another Urban Dictionary user-submitted definition of e-girl from 2014 referred to her as an "internet slut" who flirts with guys online for attention.

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How Artificial Intelligence Assists the P.R Industry

  • Instead of relying on imprecise keyword searches, or clunky Boolean strings, P.R. professionals are now utilizing entities — search terms created using A.I. But what makes entities so much better than absolutely everything else?
  • Unlike keywords, entities show you precisely for what you’re looking.
  • Boolean combines keywords — which in themselves have reduced precision — with operators (words such as “AND,” “NOT,” “OR”) in an attempt to maximize the accuracy of your search.
  • The search string could go on forever and ever, as you include more and more terms you want to omit or add.
  • For this reason, Boolean searches are inefficient and simply not appropriate for monitoring media coverage.
  • Not only are you forced to build long, messy search strings, but they are almost impossible to maintain over time.

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Tana Mongeau's Coachella outfit was the top trending female celebrity look on Google in 2019

  • According to Google's Year in Search roundup, "Tana Mongeau Coachella outfit" was also the number one trending search term for female celebrity looks in the US in 2019.
  • Mongeau attended this year's Coachella in May like many other influencers, and she sported several outfits over the two weekends.
  • Mongeau's day three look consisted of a Jaded London swimsuit and pink and green accessories.
  • She toned things down the following weekend and channeled her idol, Billie Eilish, with ripped jeans, a white crop top, and bandana.
  • Before this year, Mongeau was a relative unknown in the wider celebrity industry outside of YouTube.
  • In November, she spoke candidly about how she and her manager Jordan Worona turned down a $2 million sponsorship deal from drinks company Bang Energy.
  • But she also suggested she relies on brand deals, like many YouTubers do, because she is "cripplingly demonetized" by the platform's algorithm.

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