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Articles related to "pretty"


You Don’t Have to be Struck by Lightning to Win the Lottery

  • They had found the game they wanted to bet on, calculated the odds, and played their way to millions of dollars in profit.
  • So, for example, if a roll down was triggered at $2 million with no jackpot winners, the lottery would break down that $2 million, and pay it out to players who matched 3, 4, or 5 numbers on top of the winnings they were already entitled to.
  • A roll down from a $2,000,000 jackpot divvied up proportionally among the lower tiers would result in payouts of 13 to 11 thousand times the initial cost of your ticket depending on how many picks you matched!
  • But by controlling the odds to a 2, 3, 4, or 5 pick in a game with better than normal payouts, the MIT students were able to make certain that they got better than 1 in 45 odds for a win, even if it wasn’t a jackpot.

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Blair Witch interview — How Lionsgate and Bloober created a game after 2 decades

  • Lionsgate, the owner of the film property, and Bloober Team, the maker of horror games like Layers of Fear, made it happen through a happy collaboration.
  • After I played the preview, I did an interview with Barbara Kciuk, writer for the game at Bloober.
  • Above: Barbara Kciuk, the writer of the Blair Witch game.
  • That’s one of the things that makes it scary, and it’s also something we want to use in our game.
  • It doesn’t feel like Blair Witch.” We need to be very careful with what we’re adding because we want to expand it, but we want to expand it in the right direction.
  • Kciuk: I can’t tell you much more because it would spoil things, but in general, his past and exploring that past plays a big role in this game.

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You Don’t Have to be Struck by Lightning to Win the Lottery

  • They had found the game they wanted to bet on, calculated the odds, and played their way to millions of dollars in profit.
  • So, for example, if a roll down was triggered at $2 million with no jackpot winners, the lottery would break down that $2 million, and pay it out to players who matched 3, 4, or 5 numbers on top of the winnings they were already entitled to.
  • A roll down from a $2,000,000 jackpot divvied up proportionally among the lower tiers would result in payouts of 13 to 11 thousand times the initial cost of your ticket depending on how many picks you matched!
  • But by controlling the odds to a 2, 3, 4, or 5 pick in a game with better than normal payouts, the MIT students were able to make certain that they got better than 1 in 45 odds for a win, even if it wasn’t a jackpot.

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Save up to $400 on MacBooks at Best Buy — and 7 other sales and deals happening now

  • We rounded up the eight best sales and deals happening online today, with savings on Apple MacBooks at Best Buy, outdoor gear at Patagonia, Chicco baby gear at Target, and Purple mattresses.
  • For even more deals and savings across the web, check out Business Insider Coupons.
  • Visit Business Insider Coupons for other great deals at Best Buy. If you've been waiting for an excuse to splurge on new clothes from Patagonia, the time has come.
  • Visit Business Insider Coupons to potentially save more at Patagonia.
  • While the brand's shirts are pretty affordable year-round, the ongoing summer clearance sale makes them an even better deal.
  • Labor Day is usually a great time to find a great deal on a new mattress, but Purple is making it so that you don't have to wait for the holiday.
  • Visit Business Insider Coupons to potentially save more at Purple.

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Blair Witch interview — How Lionsgate and Bloober created a game after 2 decades

  • Lionsgate, the owner of the film property, and Bloober Team, the maker of horror games like Layers of Fear, made it happen through a happy collaboration.
  • After I played the preview, I did an interview with Barbara Kciuk, writer for the game at Bloober.
  • Above: Barbara Kciuk, the writer of the Blair Witch game.
  • That’s one of the things that makes it scary, and it’s also something we want to use in our game.
  • It doesn’t feel like Blair Witch.” We need to be very careful with what we’re adding because we want to expand it, but we want to expand it in the right direction.
  • Kciuk: I can’t tell you much more because it would spoil things, but in general, his past and exploring that past plays a big role in this game.

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I fed a bot eight hundred burgers, and this is what it threw up

  • For such a simple algorithm, it produces surprisingly coherent results in this instance, but it’s ignorant of several important rules of burger construction: A burger should have just one bun, one patty (typically), and at most two or three other ingredients.
  • This employs two or more “layers” of algorithms to first distill the raw words of the burger descriptions into meaningful data, and then makes a prediction from this distilled “embedding”.
  • The second innovation is that this model can account for varying importance of words in a sentence — the model recognises that some details of the burger — like the bun and the patty — should show up only once.
  • The model can choose to “hold on” to words that occurred early in the burger description, and use those important words to inform its choices of words later in the text.

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Visualizing NYC Bike Data on interactive and animated maps with Folium plugins

  • In this article, we wrangle with the publicly available data of New York’s bike-sharing service to introduce some plugins that can help us make animated, interactive maps in Jupyter notebooks with Python’s Folium library.
  • This plugin allows us to draw data on an interactive folium map and animate it depending on a time variable (year, month, day, hour, etc) and supports any kind of geometry (points, line strings, etc).
  • After transforming the data frame to the format we need, we have to define a function that takes the values from it and creates the geojson with the right properties to use in the plugin.
  • After running the same code for the other data frame we can now create a dual map that shows the activity towards Grove St PATH in the morning peak and the activity from Grove St PATH in the afternoon peak.

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I fed a bot eight hundred burgers, and this is what it threw up

  • For such a simple algorithm, it produces surprisingly coherent results in this instance, but it’s ignorant of several important rules of burger construction: A burger should have just one bun, one patty (typically), and at most two or three other ingredients.
  • This employs two or more “layers” of algorithms to first distill the raw words of the burger descriptions into meaningful data, and then makes a prediction from this distilled “embedding”.
  • The second innovation is that this model can account for varying importance of words in a sentence — the model recognises that some details of the burger — like the bun and the patty — should show up only once.
  • The model can choose to “hold on” to words that occurred early in the burger description, and use those important words to inform its choices of words later in the text.

save | comments | report | share on


Visualizing NYC Bike Data on interactive and animated maps with Folium plugins

  • In this article, we wrangle with the publicly available data of New York’s bike-sharing service to introduce some plugins that can help us make animated, interactive maps in Jupyter notebooks with Python’s Folium library.
  • This plugin allows us to draw data on an interactive folium map and animate it depending on a time variable (year, month, day, hour, etc) and supports any kind of geometry (points, line strings, etc).
  • After transforming the data frame to the format we need, we have to define a function that takes the values from it and creates the geojson with the right properties to use in the plugin.
  • After running the same code for the other data frame we can now create a dual map that shows the activity towards Grove St PATH in the morning peak and the activity from Grove St PATH in the afternoon peak.

save | comments | report | share on


I fed a bot eight hundred burgers, and this is what it threw up

  • For such a simple algorithm, it produces surprisingly coherent results in this instance, but it’s ignorant of several important rules of burger construction: A burger should have just one bun, one patty (typically), and at most two or three other ingredients.
  • This employs two or more “layers” of algorithms to first distill the raw words of the burger descriptions into meaningful data, and then makes a prediction from this distilled “embedding”.
  • The second innovation is that this model can account for varying importance of words in a sentence — the model recognises that some details of the burger — like the bun and the patty — should show up only once.
  • The model can choose to “hold on” to words that occurred early in the burger description, and use those important words to inform its choices of words later in the text.

save | comments | report | share on