What's that dog thinking? Does that cat look like a psycho or what? And why is a gorilla getting in a car?  Whether you want to convey your current mood or you just need a few laughs, animals have you covered.

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Animals, Streets, and 25 Years: The 20th will mark 25 years since Tyke was brutally murdered on the streets of Miami. RIP Tyke and all other animals that have died at the hands, indirectly or otherwise, of mankind.
Animals, Streets, and 25 Years: The 20th will mark 25 years since Tyke was brutally murdered on the streets of Miami. RIP Tyke and all other animals that have died at the hands, indirectly or otherwise, of mankind.

The 20th will mark 25 years since Tyke was brutally murdered on the streets of Miami. RIP Tyke and all other animals that have died at the h...

Animals, Animal, and Animal Farm: Ive read Animal Farm to know that sooner or later, Felix's animals will rebel aganist him....
Animals, Animal, and Animal Farm: Ive read Animal Farm to know that sooner or later, Felix's animals will rebel aganist him....

Ive read Animal Farm to know that sooner or later, Felix's animals will rebel aganist him....

Animals, Best, and Soviet: As Soviet troops approached Berlin in 1945, citizens did their best to take care of Berlin Zoo's animals
Animals, Best, and Soviet: As Soviet troops approached Berlin in 1945, citizens did their best to take care of Berlin Zoo's animals

As Soviet troops approached Berlin in 1945, citizens did their best to take care of Berlin Zoo's animals

Animals, Computers, and Google: Exploiting Similarities among Languages for Machine Translation lya Sutskever Google Inc. Mountain View ilyasu@google.com Quoc V. Le Google Inc. Mountain View qv18google.com Tomas Mikolov Google Inc. Mountain View tmikolov@google.com Our study found that it is possible to infer missing dictionary entries using distributed representations of words and phrases. We achieve this by learning a lincar projection between vector spaces that rep- resent each language. The method consists of two- simple steps. First, we build monolingual models of languages using large amounts of text. Next, we use a small bilingual dictionary to learn a linear projec- tion between the languages. At the test time, we can translate any word that has been seen in the mono- lingual corpora by projecting its vector representa- tion from the source language space to the target language space. Once we obtain the vector in the target language space, we output the most similar word vector as the translation The representations of languages are learned using the distributed Skip-gram or Continuous Bag-of-Words (CBOW) models recently proposed by (Mikolov et al., 2013a). These models learn word representations using a simple neural network archi- tecture that aims to predict the neighbors of a word Because of its simplicity, the Skip-gram and CBOW models can be trained on a large amount of text data: our parallelized implementation can learn a model from billions of words in hours. Figure I gives simple visualization to illustrate why and how our method works. In Figure 1, we Abstract Dictionaries and phrase tables are the basis of modern statistical machine translation sys- tems. This paper develops a method that can automate the process of generating and ex- ending dictionaries and phrase tables. Our method can tramslate missing word and phrase entries by learning language structures hased on large monolingual data and mapping be tween languages from small bilingual data. It uses distributed representation of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for ransla tion of words berween English and Spanish. This method makes little assumption abous the languages, so it can be used to exiend and re- fine dictionaries and translation tables for any language pairs water sheep 1 Introduction Statistical machine translation systems have been developed for years and became very successful in systems rely on dictionaries and for mumhers and animals in En- Why Computers Suck At Translation 1,186,709 views 29K 276 Share Download Save Tom Scott 1,795,487 subscribers SUBSCRIBED Published on May 21, 2015 Tom Scott predicted this Minecraft series.
Animals, Computers, and Google: Exploiting Similarities among Languages for Machine Translation
 lya Sutskever
 Google Inc.
 Mountain View
 ilyasu@google.com
 Quoc V. Le
 Google Inc.
 Mountain View
 qv18google.com
 Tomas Mikolov
 Google Inc.
 Mountain View
 tmikolov@google.com
 Our study found that it is possible to infer missing
 dictionary entries using distributed representations
 of words and phrases. We achieve this by learning
 a lincar projection between vector spaces that rep-
 resent each language. The method consists of two-
 simple steps. First, we build monolingual models of
 languages using large amounts of text. Next, we use
 a small bilingual dictionary to learn a linear projec-
 tion between the languages. At the test time, we can
 translate any word that has been seen in the mono-
 lingual corpora by projecting its vector representa-
 tion from the source language space to the target
 language space. Once we obtain the vector in the
 target language space, we output the most similar
 word vector as the translation
 The representations of languages are learned
 using the distributed Skip-gram or Continuous
 Bag-of-Words (CBOW) models recently proposed
 by (Mikolov et al., 2013a). These models learn word
 representations using a simple neural network archi-
 tecture that aims to predict the neighbors of a word
 Because of its simplicity, the Skip-gram and CBOW
 models can be trained on a large amount of text data:
 our parallelized implementation can learn a model
 from billions of words in hours.
 Figure I gives simple visualization to illustrate
 why and how our method works. In Figure 1, we
 Abstract
 Dictionaries and phrase tables are the basis
 of modern statistical machine translation sys-
 tems. This paper develops a method that can
 automate the process of generating and ex-
 ending dictionaries and phrase tables. Our
 method can tramslate missing word and phrase
 entries by learning language structures hased
 on large monolingual data and mapping be
 tween languages from small bilingual data.
 It uses distributed representation of words
 and learns a linear mapping between vector
 spaces of languages. Despite its simplicity,
 our method is surprisingly effective: we can
 achieve almost 90% precision@5 for ransla
 tion of words berween English and Spanish.
 This method makes little assumption abous the
 languages, so it can be used to exiend and re-
 fine dictionaries and translation tables for any
 language pairs
 water
 sheep
 1 Introduction
 Statistical machine translation systems have been
 developed for years and became very successful in
 systems rely on dictionaries and
 for mumhers and animals in En-
 Why Computers Suck At Translation
 1,186,709 views
 29K
 276
 Share
 Download
 Save
 Tom Scott
 1,795,487 subscribers
 SUBSCRIBED
 Published on May 21, 2015
Tom Scott predicted this Minecraft series.

Tom Scott predicted this Minecraft series.

Animals, Instagram, and Help: My friend drew this picture of pewds and his animals, I couldnโ€™t help but share. (Her instagram - @_lynpanzee__ )
Animals, Instagram, and Help: My friend drew this picture of pewds and his animals, I couldnโ€™t help but share. (Her instagram - @_lynpanzee__ )

My friend drew this picture of pewds and his animals, I couldnโ€™t help but share. (Her instagram - @_lynpanzee__ )

Animals, Computers, and Google: Exploiting Similarities among Languages for Machine Translation lya Sutskever Google Inc. Mountain View Quoc V. Le Google Inc. Mountain View qv1@google.com Tomas Mikolov Google Inc. Mountain View tmikolov@google.com ilyasu@google . com Our study found that it is possible to infer missing dictionary entries using distributed representations of words and phrases. We achieve this by learning a lincar projection between vector spaces that rep- resent each language. The method consists of two simple steps. First, we build monolingual models of languages using large amounts of text. Next, we use a small bilingual dictionary to learn a linear projec- tion between the languages. At the test time, we can translate any word that has been seen in the mono- lingual corpora by projecting its vector representa tion from the source language space to the target language space. Once we obtain the vector in the target language space, we output the most similar word vector as the translation. The representations of languages are learned using the distributed Skip-gram or Continuous Bag-of-Words (CBOW) models recently proposed by (Mikolov et al., 2013a). These models learn word representations using a simple neural network archi- tecture that aims to predict the neighbors of a word. Because of its simplicity, the Skip-gram and CBOW Abstract Dictionaries and phrase tables are the basis of modern statistical machine translation sys tems. This paper develops a method that can automate the process of generating and ex tending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures based on large monolingual data and mpping be tween languages from small bilingual data It uses distributed representation of words and leams a linear mapping between vector spaces of languages. Despite its simplicity our method is surprisingly effective: we can achieve almost 90% precision@5 for transla tion of words between English and Spanish This method makes little assumption about the languages, so it can be used to extend and re- fine dictionaries and translation tables for any language pairs water sheep atroduction ST:18'5O translation systems heen models can be trained on a large amount of texi data: and became very successful in ied implementation can learn a model developed ceThese systems rely on dictionaries and from billions of words n ius Figure 1 gives simple visualization to illustrate why and how our method works. In Figure 1, we ors for numbers and animals in En that these ch efforts to generate Why Computers Suck At Translation CC HD 1,186,223 views 29K 276 Tom Scott SHARE E SAVE FOUND IN A VIDEO FROM 2015....I could not believe my eyes
Animals, Computers, and Google: Exploiting Similarities among Languages for Machine Translation
 lya Sutskever
 Google Inc.
 Mountain View
 Quoc V. Le
 Google Inc.
 Mountain View
 qv1@google.com
 Tomas Mikolov
 Google Inc.
 Mountain View
 tmikolov@google.com
 ilyasu@google . com
 Our study found that it is possible to infer missing
 dictionary entries using distributed representations
 of words and phrases. We achieve this by learning
 a lincar projection between vector spaces that rep-
 resent each language. The method consists of two
 simple steps. First, we build monolingual models of
 languages using large amounts of text. Next, we use
 a small bilingual dictionary to learn a linear projec-
 tion between the languages. At the test time, we can
 translate any word that has been seen in the mono-
 lingual corpora by projecting its vector representa
 tion from the source language space to the target
 language space. Once we obtain the vector in the
 target language space, we output the most similar
 word vector as the translation.
 The representations of languages are learned
 using the distributed Skip-gram or Continuous
 Bag-of-Words (CBOW) models recently proposed
 by (Mikolov et al., 2013a). These models learn word
 representations using a simple neural network archi-
 tecture that aims to predict the neighbors of a word.
 Because of its simplicity, the Skip-gram and CBOW
 Abstract
 Dictionaries and phrase tables are the basis
 of modern statistical machine translation sys
 tems. This paper develops a method that can
 automate the process of generating and ex
 tending dictionaries and phrase tables. Our
 method can translate missing word and phrase
 entries by learning language structures based
 on large monolingual data and mpping be
 tween languages from small bilingual data
 It uses distributed representation of words
 and leams a linear mapping between vector
 spaces of languages. Despite its simplicity
 our method is surprisingly effective: we can
 achieve almost 90% precision@5 for transla
 tion of words between English and Spanish
 This method makes little assumption about the
 languages, so it can be used to extend and re-
 fine dictionaries and translation tables for any
 language pairs
 water
 sheep
 atroduction
 ST:18'5O translation systems heen models can be trained on a large amount of texi data:
 and became very successful in ied implementation can learn a model
 developed
 ceThese systems rely on dictionaries and
 from billions of words n ius
 Figure 1 gives simple visualization to illustrate
 why and how our method works. In Figure 1, we
 ors for numbers and animals in En
 that these
 ch efforts to generate
 Why Computers Suck At Translation
 CC
 HD
 1,186,223 views
 29K
 276
 Tom Scott
 SHARE
 E SAVE
FOUND IN A VIDEO FROM 2015....I could not believe my eyes

FOUND IN A VIDEO FROM 2015....I could not believe my eyes