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Articles related to "number"


Top 5 Data Center Stories of the Week: June 25, 2020

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  • HPE Unveils Ezmeral, Its Answer to Tanzu and OpenShift, but With Hardware - It’s a kind of instant hybrid cloud, a uniform, pay-as-you-go way to deploy Kubernetes anywhere.
  • Hackers Use Java to Hide Malware on the Data Center Network - Code written in Java typically goes undetected by antivirus software, allowing for crippling attacks.
  • A Huge Week for Arm – in the Data Center Too - Ampere is bringing to market a 128-core Arm server chip, and Bamboo is ready to launch a server based on its own “PANDA” Arm architecture.
  • Digital Realty and Vapor IO Pave the Roads to the Edge - The core-to-edge system is designed to fast-track deployment of highly distributed, latency sensitive workloads.

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Venmo begins piloting 'Business Profiles' for small sellers

  • The mobile-payments app announced today it’s piloting a new feature called Business Profiles, which offers small sellers and other sole proprietors the opportunity to have a more professional profile page on its platform.
  • By adopting a Business Profile, sellers will be able to raise awareness about their business through Venmo’s social feed and search, as well as keep their personal transactions separate from those for their businesses for accounting purposes.
  • As Venmo users pay these small sellers, the payment is published to the Venmo social feed where friends or even the public can view the transaction, depending on the user’s privacy settings.
  • In an online FAQ about the new feature, Venmo notes that in the future, business owners would be changed a per-transaction fee of 1.9% + $0.10 on every payment made to their profile.

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An Intuitive Explanation of the Bayesian Information Criterion

  • Going back to our example, you could imagine a model that has as many clusters as there are data points.
  • We have to balance the maximum likelihood of our model, L, against the number of model parameters, k.
  • We seek the model with the fewest number of parameters that still does a good job explaining the data.
  • The BIC balances the number of model parameters k and number of data points n against the maximum likelihood function, L.
  • We seek to find the number of model parameters k that minimizes the BIC.
  • Computing the maximum likelihood function is the hard part, but there exist analytical functions for most common models.
  • It also tells us that a larger number of clusters would also fit the data fairly well, but at the cost of having to introduce more parameters.

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An Intuitive Explanation of the Bayesian Information Criterion

  • Going back to our example, you could imagine a model that has as many clusters as there are data points.
  • We have to balance the maximum likelihood of our model, L, against the number of model parameters, k.
  • We seek the model with the fewest number of parameters that still does a good job explaining the data.
  • The BIC balances the number of model parameters k and number of data points n against the maximum likelihood function, L.
  • We seek to find the number of model parameters k that minimizes the BIC.
  • Computing the maximum likelihood function is the hard part, but there exist analytical functions for most common models.
  • It also tells us that a larger number of clusters would also fit the data fairly well, but at the cost of having to introduce more parameters.

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Britain throws opens doors to 3m Hong Kongers

  • London | The British government has confirmed it will give almost 3 million Hong Kong residents the option to resettle in Britain, as Prime Minister Boris Johnson looks to step up the response to China's imposition of a national security law on the city.
  • Mr Johnson echoed Mr Raab's comments, saying Beijing's new national security law for the province was "a clear and serious breach of the Sino-British Joint Declaration" - the treaty that governs the 1997 British handover of Hong Kong to China as well as the "one country, two systems" aftermath.
  • Boris Johnson makes good on a promise to allow eligible HK residents to resettle in Britain after Beijing's security law comes into force.
  • China's legislators on Tuesday passed national security laws for Hong Kong aimed at silencing the city's pro-democracy activists and protesters.

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Making Sense of Text Clustering

  • It may be overwhelming for people who haven’t known about text data processing but stay with me, I won’t go into many complex details and just cover the important points for easy understanding.
  • We’ll attempt text clustering using this labeled dataset.
  • There are one-hot encoding methods like CountVectorizer and TF-IDF, but we’ll specifically use word embedding on this experiment.
  • To apply word embedding to our dataset, we’ll use the fastText library.
  • They provide the pre-trained model for Indonesian language, but instead, we’ll try to train our own word embedding model using the available 150,000+ tweets as our corpus.
  • Now that we have created the word vectors, how can we cluster similar tweets together?
  • But, how can we compare which word embedding model can cluster similar tweets the better?
  • With more dimensions, the word embedding model can capture more information and generates better cluster grouping.

save | comments | report | share on


An Intuitive Explanation of the Bayesian Information Criterion

  • Going back to our example, you could imagine a model that has as many clusters as there are data points.
  • We have to balance the maximum likelihood of our model, L, against the number of model parameters, k.
  • We seek the model with the fewest number of parameters that still does a good job explaining the data.
  • The BIC balances the number of model parameters k and number of data points n against the maximum likelihood function, L.
  • We seek to find the number of model parameters k that minimizes the BIC.
  • Computing the maximum likelihood function is the hard part, but there exist analytical functions for most common models.
  • It also tells us that a larger number of clusters would also fit the data fairly well, but at the cost of having to introduce more parameters.

save | comments | report | share on


Making Sense of Text Clustering

  • It may be overwhelming for people who haven’t known about text data processing but stay with me, I won’t go into many complex details and just cover the important points for easy understanding.
  • We’ll attempt text clustering using this labeled dataset.
  • There are one-hot encoding methods like CountVectorizer and TF-IDF, but we’ll specifically use word embedding on this experiment.
  • To apply word embedding to our dataset, we’ll use the fastText library.
  • They provide the pre-trained model for Indonesian language, but instead, we’ll try to train our own word embedding model using the available 150,000+ tweets as our corpus.
  • Now that we have created the word vectors, how can we cluster similar tweets together?
  • But, how can we compare which word embedding model can cluster similar tweets the better?
  • With more dimensions, the word embedding model can capture more information and generates better cluster grouping.

save | comments | report | share on


An Intuitive Explanation of the Bayesian Information Criterion

  • Going back to our example, you could imagine a model that has as many clusters as there are data points.
  • We have to balance the maximum likelihood of our model, L, against the number of model parameters, k.
  • We seek the model with the fewest number of parameters that still does a good job explaining the data.
  • The BIC balances the number of model parameters k and number of data points n against the maximum likelihood function, L.
  • We seek to find the number of model parameters k that minimizes the BIC.
  • Computing the maximum likelihood function is the hard part, but there exist analytical functions for most common models.
  • It also tells us that a larger number of clusters would also fit the data fairly well, but at the cost of having to introduce more parameters.

save | comments | report | share on


Making Sense of Text Clustering

  • It may be overwhelming for people who haven’t known about text data processing but stay with me, I won’t go into many complex details and just cover the important points for easy understanding.
  • We’ll attempt text clustering using this labeled dataset.
  • There are one-hot encoding methods like CountVectorizer and TF-IDF, but we’ll specifically use word embedding on this experiment.
  • To apply word embedding to our dataset, we’ll use the fastText library.
  • They provide the pre-trained model for Indonesian language, but instead, we’ll try to train our own word embedding model using the available 150,000+ tweets as our corpus.
  • Now that we have created the word vectors, how can we cluster similar tweets together?
  • But, how can we compare which word embedding model can cluster similar tweets the better?
  • With more dimensions, the word embedding model can capture more information and generates better cluster grouping.

save | comments | report | share on