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Articles related to "information"


Nicholas Carr on deep reading and digital thinking

  • In this conversation on The Ezra Klein Show, Carr and I discuss how speaking, reading, and now the internet have each changed our brains in different ways, why “paying attention” doesn’t come naturally to us, why we’re still reading Marshall McLuhan, how human memory actually works, why having your phone in sight makes you less creative, what separates “deep reading” from simply reading, why deep reading is getting harder, why building connections is more important than absorbing information, the benefits to collapsing the world into a connected digital community, and much more.
  • When we adapt to a new medium — whether printed page or television or, more recently, the internet and social media and so forth — more and more neurons get recruited to the particular brain processes that you’re using more often thanks to the different information technologies.

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Mastering Query Plans in Spark 3.0

  • It has a structure of a tree and each node represents an operator that provides some basic details about the execution.
  • The motivation for this article is to provide some familiarity with the physical plans, we will take a tour of some of the most frequently used operators and explain what information they provide and how it can be interpreted.
  • The cost mode will show besides the physical plan also the optimized logical plan with the statistics for each operator so you can see what are the estimates for the data size at different steps of execution.
  • Even though we do not select specific fields in our query, there is a ColumnPruning rule in the optimizer that will be applied and it makes sure that only those columns that are actually needed will be selected from the source.
  • The physical plans in Spark SQL are composed of operators that carry useful information about the execution.

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Mastering Query Plans in Spark 3.0

  • It has a structure of a tree and each node represents an operator that provides some basic details about the execution.
  • The motivation for this article is to provide some familiarity with the physical plans, we will take a tour of some of the most frequently used operators and explain what information they provide and how it can be interpreted.
  • The cost mode will show besides the physical plan also the optimized logical plan with the statistics for each operator so you can see what are the estimates for the data size at different steps of execution.
  • Even though we do not select specific fields in our query, there is a ColumnPruning rule in the optimizer that will be applied and it makes sure that only those columns that are actually needed will be selected from the source.
  • The physical plans in Spark SQL are composed of operators that carry useful information about the execution.

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The Monty Hall Problem: Naive Bayes explained!

  • Let’s make a deal!” If you are interested in machine learning, then it is very plausible that you have heard of Bayes Theorem and the Naïve Bayes classifier.
  • After the selection is made, Monty will reveal what was behind one of the 2 unfavourable doors and then ask whether the contestant would like to stick with their initial selection or switch to the remaining closed door.
  • Well, both the Monty Hall Problem and the Naïve Bayes classifier are rooted in Bayes Theorem and they highly depend on the likelihood of an event occurring.
  • Bayes Theorem is applicable whenever there exists a hypothesis, evidence relating to the hypothesis, and the question being asked is “what is the probability of this hypothesis, given that the evidence is true”.
  • So, now you are familiar with Bayes Theorem, you understand simple email classification, and you know how to win on average at Let’s Make A Deal!

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Show HN: The Book of Modding

  • Welcome to the one place where you will learn how to develop your very own minecraft mod.
  • Time to bring your dream to reality.
  • The creation of this book is mainly motivated by the lack of centralized and up-to-date information with regard to minecraft modding with forge.
  • By documenting my modding journey, I hope to make the lives of other beginner modders just a little bit easier.
  • We will be using minecraft forge and targeting minecraft 1.16.1 in this book.
  • If you have any suggestions or questions, feel free to shoot me a message on twitter.

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The Monty Hall Problem: Naive Bayes explained!

  • Let’s make a deal!” If you are interested in machine learning, then it is very plausible that you have heard of Bayes Theorem and the Naïve Bayes classifier.
  • After the selection is made, Monty will reveal what was behind one of the 2 unfavourable doors and then ask whether the contestant would like to stick with their initial selection or switch to the remaining closed door.
  • Well, both the Monty Hall Problem and the Naïve Bayes classifier are rooted in Bayes Theorem and they highly depend on the likelihood of an event occurring.
  • Bayes Theorem is applicable whenever there exists a hypothesis, evidence relating to the hypothesis, and the question being asked is “what is the probability of this hypothesis, given that the evidence is true”.
  • So, now you are familiar with Bayes Theorem, you understand simple email classification, and you know how to win on average at Let’s Make A Deal!

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Facebook still won’t take down politicians’ misleading posts, but it’s trying to register 4 million new voters

  • The company plans to show this Voting Information Center to 160 million people in total and aims to help 4 million Americans register to vote — that’s twice the number of voters the company says it helped register in the lead-up to the 2016 presidential election, using similar efforts.
  • Instead, they want Facebook to consider a “non-binary” action that would label those posts as misleading or contentious, similar to what Twitter did with Trump’s mail-in ballot statements in May. Facebook did not respond to a request to answer follow-up questions about the voter information center in time for publication.
  • Considering Zuckerberg’s long-standing ethos that Facebook should not be an “arbiter of truth” on contentious political speech, his decision not to intervene with world leaders on controversial posts about voting but instead surface more seemingly objective information makes sense.

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The Monty Hall Problem: Naive Bayes explained!

  • Let’s make a deal!” If you are interested in machine learning, then it is very plausible that you have heard of Bayes Theorem and the Naïve Bayes classifier.
  • After the selection is made, Monty will reveal what was behind one of the 2 unfavourable doors and then ask whether the contestant would like to stick with their initial selection or switch to the remaining closed door.
  • Well, both the Monty Hall Problem and the Naïve Bayes classifier are rooted in Bayes Theorem and they highly depend on the likelihood of an event occurring.
  • Bayes Theorem is applicable whenever there exists a hypothesis, evidence relating to the hypothesis, and the question being asked is “what is the probability of this hypothesis, given that the evidence is true”.
  • So, now you are familiar with Bayes Theorem, you understand simple email classification, and you know how to win on average at Let’s Make A Deal!

save | comments | report | share on


The Monty Hall Problem: Naive Bayes explained!

  • Let’s make a deal!” If you are interested in machine learning, then it is very plausible that you have heard of Bayes Theorem and the Naïve Bayes classifier.
  • After the selection is made, Monty will reveal what was behind one of the 2 unfavourable doors and then ask whether the contestant would like to stick with their initial selection or switch to the remaining closed door.
  • Well, both the Monty Hall Problem and the Naïve Bayes classifier are rooted in Bayes Theorem and they highly depend on the likelihood of an event occurring.
  • Bayes Theorem is applicable whenever there exists a hypothesis, evidence relating to the hypothesis, and the question being asked is “what is the probability of this hypothesis, given that the evidence is true”.
  • So, now you are familiar with Bayes Theorem, you understand simple email classification, and you know how to win on average at Let’s Make A Deal!

save | comments | report | share on


The Monty Hall Problem: Naive Bayes explained!

  • Let’s make a deal!” If you are interested in machine learning, then it is very plausible that you have heard of Bayes Theorem and the Naïve Bayes classifier.
  • After the selection is made, Monty will reveal what was behind one of the 2 unfavourable doors and then ask whether the contestant would like to stick with their initial selection or switch to the remaining closed door.
  • Well, both the Monty Hall Problem and the Naïve Bayes classifier are rooted in Bayes Theorem and they highly depend on the likelihood of an event occurring.
  • Bayes Theorem is applicable whenever there exists a hypothesis, evidence relating to the hypothesis, and the question being asked is “what is the probability of this hypothesis, given that the evidence is true”.
  • So, now you are familiar with Bayes Theorem, you understand simple email classification, and you know how to win on average at Let’s Make A Deal!

save | comments | report | share on