PowerPoint
Deep Learning Deep Learning #general / YouTube
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Deep Learning Deep LearningDeep Learning
Deep LearningDeep Learning
Deep LearningDeep Learning3
n-gramWord EmbeddingGoogle
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1. n-gram
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n-gram
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n-gram leave chaos as is but cause it to evolve chaos leave but
is as to cause evolve it
bigram = leave chaos chaos as as is to evolvewikipedia
bigram 2-gram 7
n-gram leave chaos as is but cause it to evolve chaos leave but
is as to cause evolve it
trigram = leave chaos as chaos as is as is but it to
evolvetrigram 3-gram
4-gram, 5-gram n-gram 8
n-gram n-1
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2. Word Embedding
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float dog 11
ID12
IDID13
One-Hot EncodingID = n [0, 0,0, 1, 0,...0, 0] n1014
[1, 0, 0, 0, 0][0, 0, 0, 0, 1][0, 1, 0, 0, 0][1, 0, 0, 0, 0][0,
0, 1, 0, 0][0, 0, 0, 0, 1][0, 0, 0, 1, 0]
One-Hot Encoding
Word Embedding15
Word Embedding16
Word Embedding17
n-gram18
n-1 Embedding-Embedding Embedding
Word EmbeddingEmbeddingn-gram
: American Japanese the American society has the Japanese
society has ...American government says ...Japanese government says
...
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Skip-gram20
Embedding
skip-gram
Embedding
n-gram
Word Embedding
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3. (Google)22
Recurrent Neural
NetworkGooglehttps://www.udacity.com/course/deep-learning--ud730
https://goo.gl/xtzFmu
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Recurrent Neural
NetworkGoogle(1)https://youtu.be/-jpIbc20V-k
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Recurrent Neural
NetworkGoogle(2)https://youtu.be/n4tGj6TLJK8
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Recurrent Neural
NetworkGoogle(3)https://youtu.be/c2mdI_rDLDk
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Recurrent Neural
NetworkGoogle(4)https://youtu.be/186HUTBQnpY
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Recurrent Neural
NetworkGoogle(5)https://youtu.be/xMwx2A_o5r4
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Recurrent Neural
NetworkGoogle(6)https://youtu.be/p3wFE85dAyY
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Recurrent Neural
NetworkGoogle(7)https://youtu.be/p3wFE85dAyY
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Recurrent Neural
NetworkGoogle(8)https://youtu.be/BTfuN1cAsqw
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Recurrent Neural
NetworkGoogle(9)https://youtu.be/IbI2RJLxGZg
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Recurrent Neural
NetworkGoogle(10)https://youtu.be/LTbLxm6YIjE
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Recurrent Neural
NetworkGoogle(11)https://youtu.be/H3ciJF2eCJI
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Recurrent Neural Network
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Recurrent Neural
NetworkGoogle(12)https://youtu.be/Nj2ab1PzoEI
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Recurrent Neural Network
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Recurrent Neural
NetworkGoogle(13)https://youtu.be/VuamhbEWEWA
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Recurrent Neural
NetworkGoogle(14)https://youtu.be/V3D5ovKE9Og
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Recurrent Neural
NetworkGoogle(15)https://youtu.be/hO_EQZmqcro
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Recurrent Neural
NetworkGoogle(16)https://youtu.be/XHEsLfquXtg
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Recurrent Neural
NetworkGoogle(17)https://youtu.be/eNFgagZLHUQ
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Recurrent Neural
NetworkGoogle(18)https://youtu.be/qxoQTTTkicY
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Recurrent Neural
NetworkGoogle(19)https://youtu.be/UXW6Cs82UKo
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Recurrent Neural
NetworkGoogle(20)https://youtu.be/0SfdBWTdEmw
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Recurrent Neural
NetworkGoogle(21)https://youtu.be/QzhN985hzXQ
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4.
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4-1.
1. Sutskever, et.al. "Sequence to Sequence Learning with Neural
Networks" ,2014LSTM state-of-the-art49
1. Sutskever, et.al. "Sequence to Sequence Learning with Neural
Networks" ,2014 WMT14 1200 : 3.04 : 3.48
BLEUn-gram
: 16 : 8 UNK() UNK50
1. Sutskever, et.al. "Sequence to Sequence Learning with Neural
Networks" ,201451 one-hot (16)embedding (1000)LSTM (1000)LSTM
(1000)LSTM (1000)LSTM (1000) one-hot (8)
1. Sutskever, et.al. "Sequence to Sequence Learning with Neural
Networks" ,2014
State-of-the-Art(BLEU=34.81)(BLEU=36.5)
1GPUDNN 1700/8GPU 6300/8GPU11052
1. Sutskever, et.al. "Sequence to Sequence Learning with Neural
Networks" ,201453
LSTMPCA
4-2.
2. Hermann, et.al. Teaching Machines to Read and Comprehend,
2015CNNDailyMailCNN: 938DailyMail: 208855
2. Hermann, et.al. Teaching Machines to Read and Comprehend,
2015anonymise cause breast X X=cancer56
2. Hermann, et.al. Teaching Machines to Read and Comprehend,
20151: Deep LSTM - - LSTMX, LSTM256 x 257
2. Hermann, et.al. Teaching Machines to Read and Comprehend,
20152: Attentive Reader Bidirectional LSTM LSTM r LSTM uLSTMu58
2. Hermann, et.al. Teaching Machines to Read and Comprehend,
2015LSTMAttentive Reader59
2. Hermann, et.al. Teaching Machines to Read and Comprehend,
2015Attentive Reader60
4-3.
3. Vinyals, et.al. A Neural Conversational Model, 2015LSTM62
3. Vinyals, et.al. A Neural Conversational Model, 2015 IT
Helpdesk Troubleshooting Dataset1400300078URL ( , )10241LSTM2
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3. Vinyals, et.al. A Neural Conversational Model, 2015
OpenSubtitles Dataset88001340962LSTM10
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4-4.
4. Mao, et.al. Deep Captioning with Multimodal Recurrent Neural
Networks (m-RNN), 2015RNNCNNCNNVGGnetMS COCO12state-of-the-art
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RNN
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Deep LearningDeep Learning
Deep LearningDeep Learning
Deep LearningDeep Learning76
Hello World CreativeAI.net
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Deep Learning
Deep Learning etc..
https://goo.gl/4urjCn
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