第3回 機械学習の手法

B3勉強会 第3回目
発表日 2018年1月30日

Transcript

1. B3  130  
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2. word2vec u 84)60#= 84(*%  -,:;  u 84$+5 ( 
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3. word2vec !C)+ u ?5( )+$6("% u 4?5*<101. 10001.:&',8)+ u ?5*<D @3    
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4. word2vec %A+- 2"/B/#//3<// #69@;2"B3<0 7$';!D)"(!) 1 C4@;V=? ,1Vid 5:  #& /( ! : (" !% , " !' , ⋯ , " !) ) .8*  ! #,>+ !
5. word2vec  
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6. word2vec
    >>ibm query: ibm machines: 0.390068233013 digital: 0.382320284843 p&g: 0.342204213142 navigation:0.331580519676 mixte:0.323415249586 >> monday query: monday friday: 0.679558992386 late: 0.640058636665 plunge: 0.542724490166 thursday: 0.527576982975 yesterday: 0.52104818821
7. word2vec ]Ec u '/3!04* kb, )2HMFl
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8. word2vec - word2vec#$- u
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9. Recurrent Neural Network $
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10. Recurrent Neural Network   RNNLM u   !"(!) u s = %& %' ⋯ %) (N! #) " ! = " %& , %' , ⋯ , %- = " %& " %' %& " %. %& %' ⋯ " %) %& %' ⋯ %)/& = "(%& ) 0 "(%1 |%& %' ⋯ %1/& ) ) 13' "(%1 |%& %' ⋯ %1/& ) 
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11. Recurrent Neural Network * RNNLM *!  s=“+ "
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12. Recurrent Neural Network )- ! /* %1 
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13. Recurrent Neural Network #0  u 1epoch 
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14. Recurrent Neural Network
     u 1epoch 208.103092604 u 2epoch 174.181462185 u 3epoch 164.917536858 u 4epoch 160.432661616 u 5epoch 157.165286105
15. Recurrent Neural Network  u $%" Google%"RNNNeural Machine Translation 
     %"  u  u !#  u  !  Convolution Neural NetworkRNN 
    
16. V W u word2vec u Recurrent Neural Network op u v(2017) Chainer v2 m s n bf u 0 5 /6 gl de Nura c wiRk 9 566 2 6 6 3: 52 2 2 9: 61 62 : ) 5 6 1 6 1 71 51 6 9 u .-- ht _ .6 6 -6 2 -6 9 566 2 6 6 566 1 62 : ( 6 6 6 2 6 9