[db analytics showcase sapporo 2017] microsoftのaiテクノロジーを活用しよう...
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Is_dog
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Step1: 予測モデルの構築コンピューターを用いてデータを統計学的な分析手法に則って解析。データに含まれる潜在的なルール,相関関係,特徴を見出す(=モデル化)
東京大学 特任准教授松尾 豊 氏 による AI 発展の系譜
総務省「AIネットワーク社会推進会議」資料
・・・・・
・・・・・
・・・・・
・・・・・
・・・・・
学習済みモデル利用
学習モデル作成
→ MLの予測モデルを自作せずに結果だけ利用
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Search
Computer Vision
Emotion
Face
Video
Vision
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Translator
Speech
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
LanguageAcademic
KnowledgeEntity LinkingKnowledge Exploration
Recommendations
Knowledge
microsoft.com/cognitive
Labs
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Translator
Speech
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
LanguageAcademic
KnowledgeEntity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
microsoft.com/cognitive
Content Moderator
Project Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongong
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Translator
Speech
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
LanguageAcademic
KnowledgeEntity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
Content Moderator
LabsProject Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongongmicrosoft.com/cognitive
Labs
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Translator
Speech
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
LanguageAcademic
KnowledgeEntity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
microsoft.com/cognitive
Content Moderator
Project Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongong
GANew
https://westus.api.cognitive.microsoft.com/emotion/v1.0/recognize
using Microsoft.ProjectOxford.Emotion;
using Microsoft.ProjectOxford.Emotion.Contract;
string subkey = "YOUR_SUBSCRIPTION_KEY";
var eClient = new EmotionServiceClient(subkey);
Emotion[] eResult = await eClient.RecognizeAsync(url);
float score = eResult[0].Scores.Happiness;
Labs
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Translator
Speech
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
LanguageAcademic
KnowledgeEntity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
Project Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongong
視覚
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Content Moderator
microsoft.com/cognitive
SearchVision Speech Language Knowledge
Categories [ { "Name": "people_swimming", "Score": 0.98046875 } ]
Faces
[ { "Age": 36, "Gender": "Male", "FaceRectangle": { "Top": 133, "Left": 298, "Width": 121, "Height": 121 } } ]
Dominant color background
■"White"
Dominant color foreground
■"Grey"
Accent Color ■#19A4B2
Where there is love there is life.Mahatma Gandhi2 October 1869 — 30 January 1948
GA
[Document] https://docs.microsoft.com/ja-jp/azure/cognitive-services/custom-vision-service/home[Check&Try] https://www.customvision.ai/
SearchVision Speech Language Knowledge
New
DemoCustom Vision API
[Document] https://docs.microsoft.com/ja-jp/azure/cognitive-services/video-indexer/video-indexer-overview[Check&Try] https://www.videoindexer.ai/
SearchVision Speech Language Knowledge
New
DemoVideo Indexer API
Labs
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Translator
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
LanguageAcademic
KnowledgeEntity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
Content Moderator
Project Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongong
音声
microsoft.com/cognitive
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Speech
*ja-jp含む
*ja-jp含む
Speech Translation=Speech Recognition+Translation+Text-to-Speech
Labs
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
SpeechAcademic
KnowledgeEntity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
Content Moderator
Project Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongong
言語
microsoft.com/cognitive
Translator
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
Language
SearchVision Speech Language Knowledge
[Document] https://docs.microsoft.com/en-us/azure/cognitive-services/luis/home[Check&Try] https://www.luis.ai/
*ja-jp 含む*ja-jp 含む
https://text-analytics-demo.azurewebsites.net/
SearchVision Speech Language Knowledge
Labs
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Translator
Speech
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
Language
Content Moderator
Project Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongong
知識
microsoft.com/cognitive
AcademicKnowledge
Entity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
SearchVision Speech Language Knowledge
SearchVision Speech Language Knowledge
SearchVision Speech Language Knowledge
[Document] https://docs.microsoft.com/ja-jp/azure/cognitive-services/custom-decision-service/custom-decision-service-overview[Check&Try] http://ds.microsoft.com/
New
Labs
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Translator
Speech
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
LanguageAcademic
KnowledgeEntity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
Content Moderator
Project Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongong
検索
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
microsoft.com/cognitive
SearchVision Speech Language Knowledge
[Document] https://docs.microsoft.com/ja-jp/azure/cognitive-services/bing-custom-search/overview[Check&Try] https://customsearch.ai/
New
Computer Vision
Emotion
Face
Video
Video Indexer
Custom Vision Service
Vision
Bing SpeechCustom Speech
ServiceSpeaker
Recognition
Translator
Speech
Bing Spell Check
Linguistic Analysis
Language Understanding
Text Analytics
Web Language Model
LanguageAcademic
KnowledgeEntity LinkingKnowledge Exploration
QnA Maker
Recommendations
Custom Decision Service
Knowledge
Content Moderator
Bing Web Search
Bing Autosuggest
Bing Image Search
Bing Video Search
Bing News Search
Bing Custom Search
Search
LabsProject Prague Nanjing Project Project JohannesburgProject Cuzco Project Abu Dhabi Project Wollongongmicrosoft.com/cognitive
https://aka.ms/gestures
[Check&Try] https://labs.cognitive.microsoft.com/
https://labs.cognitive.microsoft.com/en-us/project-johannesburg
学習済みモデル利用
学習モデル作成
サービス検証プロトタイプ開発
投資 業界 業務シナリオ販促 生産性
流通&小売 広報&マーケ品質 新製品
投資 業界 業務シナリオ販促 生産性
運輸 社内IT品質 新製品
Webチャットエンドユーザー
チャット制御質問特定(LUIS)
AQ
質問検索
F F
会話履歴 QA
AQ
F 特定?
Q
Y
N
A
追加学習
(Azure Search)
FAQ自動応答システム
複数チャットを特定応答
1 23
4
56
7 8
9
Q:質問A:回答F:フィードバック
1Intent-1Question
サービス実現性の検証を実施予定(2017年7月~)
投資 業界 業務シナリオ販促 生産性
流通&小売 営業&販売品質 新製品
サービス検証プロトタイプ開発
de:code 2017code your future
投資 業界 業務シナリオ販促 生産性
流通&小売 営業&販売品質 新製品
投資 業界 業務シナリオ販促 生産性
流通&小売 営業&販売品質 新製品
投資 業界 業務シナリオ販促 生産性
流通&小売 営業&販売品質 新製品
• Python, C++, BrainScript• 学習済みモデルは C#、Javaで動かすことも可能• MIT や Stanford 等の様々な研究者と共同作業で開発中• 2017年6月、バージョン2.0がGA (正式リリース)に
Caffe CNTK MxNet TensorFlowFCN5 (1024) 55.3ms 51.0ms 60.4ms 62.0msAlexNet (256) 36.8ms 27.2ms 29.0ms 104.0msResNet (32) 144.0ms 81.5ms 84.5ms 181.4msLSTM (256)(v7 benchmark)
- 43.6ms(44.9ms)
288.1ms(284.9ms)
-(223.5ms)
http://dlbench.comp.hkbu.edu.hk/ Benchmarking by HKBU, Version 8
2017年4月時点
エラー率
CNTK のスケール性が世界記録達成に貢献
歩道画像を解析撮影地点ごとに
ごみの種類や数を判別
地図データ(Google,ZENRIN)
清掃員の最適配置や新たな街づくりの提案へ
参照 http://www.image-net.org/http://image-net.org/challenges/LSVRC/2012/ilsvrc2012.pdf
ILSVRC大量のタグ付き画像データの認識精度を競うコンテスト
IMAGE NET 2012年
http://image-net.org
AlexNet, 8 layers(ImageNet 2012)
Very
VGG, 19 layers(ImageNet 2014)
Ultra
ResNet,152 layers
By Microsoft
重みづけ更新
重みづけ更新
重みづけ更新
皆さんも簡単にお試しいただけます
https://aka.ms/cntk_image
モデル
学習データを使ってモデルを教育すること
モデルの出来を精度から測ってあげること評価
The CIFAR-10 dataset
故障診断 医療診断物品の自動仕分け
データ準備
学習
モデル 分割
評価
データ準備
¥image¥train¥00000.png 6¥image¥train¥00001.png 9 ¥image¥train¥00002.png 9
データ準備
学習
モデル 分割
評価
データ準備
学習
モデル 分割
評価
モデル
http://intellabs.github.io/RiverTrail/tutorial/
Convolution, Pooling Layerを含むNeural Network
Convolution()MaxPooling() Dense()
AlexNet, 8 layers(ImageNet 2012)
データ準備
学習
モデル 分割
評価
学習
https://aka.ms/cntk-setup
CPU GPU
簡単!検証用には OK !
パフォーマンスはこちら!面倒くさい環境構築…
https://aka.ms/deepvm
Azure N シリーズ GPU インスタンス
2 種類の NVIDIA GPUを搭載サイズ コア
数 メモリ Disk RDMA GPU
NV6 6 56 GB 380 GB - M60 ×1
NV12 12 112 GB 680 GB - M60 ×2
NV24 24 224 GB 1.5 TB - M60 ×4
NC6 6 56 GB 340 GB - K80 ×1
NC12 12 112 GB 680 GB - K80 ×2
NC24 24 224 GB 1.5 TB - K80 ×4
NC24r 24 224 GB 1.5 TB InfiniBand K80 ×4
Visualization のNV 系
Compute のNC 系
Deep Learning はこちら
Kepler Maxwell Pascal Volta
GeForceゲーミング
Quadroプロフェッショナルグラフィックス
TeslaHPC & Cloud
M60
M6
M4 M40
P6000
GTX 1080
M6000M5000K6000K5000
GTX 980GTX 780
HPC 用
GRID 用
K80K40K20 P100
K2
K520
P40P4DL 用
M10
P5000
K1
https://aka.ms/cntk-tutorial
https://github.com/msmamita/cntk_handson
開発者チームも頻繁に見ています
https://stackoverflow.com/questions/tagged/cntk
microsoft.com/cognitive
https://docs.microsoft.com/ja-jp/azure/cognitive-services/
https://github.com/Microsoft?q=cognitive
https://westus.dev.cognitive.microsoft.com/docs/services/https://dev.cognitive.microsoft.com/docs/services/
https://docs.com/cogbot/9675/cognitive-services
[次回勉強会]2017/06/16(金) @日本マイクロソフト セミナールーム(品川)
Cognitive Toolkitドキュメント、チュートリアル、モデル ギャラリー、ブログなど…
http://cntk.ai/https://github.com/Microsoft/CNTKAzure Notebookshttps://notebooks.azure.com/cntk/libraries/tutorialsMicrosoft Cognitive Toolkit (CNTK) Japan Communityhttp://aka.ms/cntkjapan
© 2017 Microsoft Corporation. All rights reserved.
本情報の内容(添付文書、リンク先などを含む)は、作成日時点でのものであり、予告なく変更される場合があります。
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