Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Amazon Machine Learning を使ってみた
Search
Kenta Murata
April 21, 2015
Technology
17
5.1k
Amazon Machine Learning を使ってみた
画面を指さしながら説明するために作った背景画像の上に、簡単な説明テキストを追加したやつです。
Kenta Murata
April 21, 2015
Tweet
Share
More Decks by Kenta Murata
See All by Kenta Murata
waitany と waitall を作った話
mrkn
0
210
HolidayJp.jl を作りました
mrkn
0
220
Calling Julia functions from Streamlit applications
mrkn
1
460
Red Data Tools で切り開く Ruby の未来
mrkn
3
1.2k
Method-based JIT compilation by transpiling to Julia
mrkn
0
7.3k
Apache Arrow C++ Datasets
mrkn
4
1.6k
Reducing ActiveRecord memory consumption using Apache Arrow
mrkn
0
1.7k
RubyData and Rails
mrkn
0
3.1k
Tensor and Arrow
mrkn
0
950
Other Decks in Technology
See All in Technology
Conquering PDFs: document understanding beyond plain text
inesmontani
PRO
2
460
更新系と状態
uhyo
8
2.2k
AI 코딩 에이전트 더 똑똑하게 쓰기
nacyot
0
490
今日からはじめるプラットフォームエンジニアリング
jacopen
8
1.9k
Gateway H2 モジュールで スマートホーム入門
minoruinachi
0
130
Twelve-Factor-Appから学ぶECS設計プラクティス/ECS practice for Twelve-Factor-App
ozawa
3
160
LINE 購物幕後推手
line_developers_tw
PRO
0
340
エンジニアリングで組織のアウトカムを最速で最大化する!
ham0215
1
280
グループ ポリシー再確認 (2)
murachiakira
0
210
AOAI で AI アプリを開発する時にまず考えたいこと
mappie_kochi
0
110
Azure Maps Visual in PowerBIで分析しよう
nakasho
0
190
Web Intelligence and Visual Media Analytics
weblyzard
PRO
1
6k
Featured
See All Featured
GraphQLの誤解/rethinking-graphql
sonatard
71
10k
Optimising Largest Contentful Paint
csswizardry
37
3.2k
BBQ
matthewcrist
88
9.6k
A designer walks into a library…
pauljervisheath
205
24k
Art, The Web, and Tiny UX
lynnandtonic
298
20k
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
179
53k
How to train your dragon (web standard)
notwaldorf
91
6k
The Web Performance Landscape in 2024 [PerfNow 2024]
tammyeverts
5
550
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
30
2.3k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
Typedesign – Prime Four
hannesfritz
41
2.6k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
280
13k
Transcript
Amazon ML Λ ͬͯΈͨ Kenta Murata 2015.04.21
ػցֶश
ػցֶशͰͰ͖Δ͜ͱ 1. ճؼ 2. ྨ 3. ΫϥελϦϯά
ػցֶशͰͰ͖Δ͜ͱ 1. ճؼ 2. ྨ 3. ΫϥελϦϯά → ࣮ͷ༧ଌ http://commons.wikimedia.org/wiki/File:Linear_regression.svg
http://commons.wikimedia.org/wiki/File:Polyreg_scheffe.svg
ػցֶशͰͰ͖Δ͜ͱ 1. ճؼ 2. ྨ 3. ΫϥελϦϯά → ࣮ͷ༧ଌ →
͔̋×͔Λ༧ଌ http://en.wikipedia.org/wiki/File:SVM_with_soft_margin.pdf
ػցֶशͰͰ͖Δ͜ͱ 1. ճؼ 2. ྨ 3. ΫϥελϦϯά → ࣮ͷ༧ଌ →
͔̋×͔Λ༧ଌ → ࣗಈάϧʔϓ͚ http://commons.wikimedia.org/wiki/File:KMeans-density-data.svg
Amazon Machine Learning
Amazon Machine Learning ͰͰ͖Δ͜ͱ 1. ճؼ 2. ೋྨ 3. ଟྨ
Amazon Machine Learning ͰͰ͖Δ͜ͱ 1. ճؼ 2. ೋྨ 3. ଟྨ
ͬͯΈͨ
Amazon Machine Learning Ͱ ଟྨثΛ࡞Δ
σʔλͷ४උ ↓ σʔλιʔε࡞ ↓ Ϟσϧ࡞ ↓ (σʔλιʔεͷࣗಈׂ) ↓ Ϟσϧͷֶश ↓
ϞσϧͷධՁ ଟྨثͷ࡞खॱ
σʔλͷ४උ
None
70,000ݸͷखॻ͖ࣈ http://myselph.de/neuralNet.html 28px 28px
60,000ݸ → ֶश༻ 10,000ݸ → ධՁ༻ ֶश༻ͱධՁ༻ʹ༧Ί͚ͯ͞Ε͍ͯΔ
όΠφϦσʔλͳͷͰ CSV ม͢Δ
28px 28px y, x1, x2,ɾɾɾ, x_k,ɾɾɾ, x784 8, 0, 0,ɾɾɾ,
221,ɾɾɾ, 0 256֊ௐάϨΠεέʔϧ ਖ਼ղϥϕϧ ϐΫηϧ
μϯϩʔυ͢Δ
https://rubygems.org/gems/mnist
$ gem install mnist $ mnist2csv train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz > mnist_train.csv
$ mnist2csv t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz > mnist_test.csv
CSV ϑΝΠϧΛ S3 ʹΞοϓϩʔυ͢Δ
σʔλιʔεΛ࡞Δ
None
Ξοϓϩʔυͨ͠ CSV ϑΝΠϧ
None
None
None
None
ྨରͷΧϥϜΛબͯ͠Ͷὑ
σʔλΛݟͯࣗಈఆ
༧ଌ݁Ռ͕σʔλιʔεͷͲͷߦʹରԠ͢Δ͔Λ ࣝผ͢ΔͨΊͷ ID ͕͋Εࢦఆ͢Δ ࠓճແ͍ͷͰࢦఆ͠ͳ͍
None
None
None
None
ϞσϧΛ࡞Δ
None
ೖྗσʔλΛબ
બͿ
None
None
σʔλΛ 7:3 ʹׂͯ͠ 7 ͷํΛ܇࿅ʹɺ3 ͷํ ΛϞσϧͷධՁʹ͏
͍Ζ͍ΖࣗͰࢦఆ͢Δ ࠓճͬͪ͜
None
σʔλͷલॲཧํ๏ͳͲ Λ JSON Ͱࢦఆ͢Δ ϑΟʔϧυɻ ࠓճ CSV ʹมͨ͠ ͚ͩͰલॲཧ͕ྃͯ͠ ΔͷͰσϑΥϧτͷ··
Ͱ͓̺
None
Regularization (ਖ਼ଇԽ) ɺϞσϧͷաֶश (܇࿅σʔ λʹద߹͗ͯ͢͠͠·͏ࣄ) Λ͙ͨΊʹߦ͏ɻ L1 (Lasso ճؼ) ɺෆཁͳύϥϝʔλΛͬͯϞσϧΛ
γϯϓϧʹ͍ͨ͠ͱ͖ʹ͏ɻ L2 (Ridge ճؼ) Β͔ͳϞσϧ͕ཉ͍͠ͱ͖ʹ͏ɻ (ײ: L1 ͱ L2 ΛࠞͥΒΕΕͬͱྑ͍ͷʹ)
None
Ϟσϧͷ࡞ޙʹࣗಈతʹධՁ࣮ࢪ͢Δ͔Ͳ͏͔ɻ ࠓճผʹධՁΛΔͷͰ No ΛબͿɻ
None
None
ϞσϧΛ࡞Δ
ֶशδϣϒࣗಈతʹ։࢝͢Δ
None
60,000 ڭࢣσʔλ → 20
ϞσϧΛධՁ͢Δ
None
None
None
None
None
None
None
10,000 ςετσʔλ → 1ʙ2
None
ҎԼͷࣜͰܭࢉ͞ΕΔϞσϧͷ༏ल͞ΛଌΔྔ 2 × ద߹ × ࠶ݱ ద߹ + ࠶ݱ
ਅͷྨ 1 ͦͷଞ ༧ ଌ ݁ Ռ 1 True Positive
False Positive ͦ ͷ ଞ False Negative True Negative ద߹ ʹ ࠶ݱ ʹ True Positive True Positive + False Positive True Positive True Positive + False Negative TP FP FN TN TP FP FN TN
None
1,000 ڭࢣσʔλͰ࡞ͬͨϞσϧͷ߹
None
ڭࢣσʔλ͕ଟ͍΄ͲϞσϧͷੑೳ͕ྑ͘ͳΔ
ϞσϧΛ͏
Ϟσϧͷ͍ํ 1. όον༧ଌ 2. ϦΞϧλΠϜ༧ଌ
Ϟσϧͷ͍ํ 1. όον༧ଌ 2. ϦΞϧλΠϜ༧ଌ → ·ͱ·ͬͨσʔλΛ·ͱΊͯ༧ଌ
Ϟσϧͷ͍ํ 1. όον༧ଌ 2. ϦΞϧλΠϜ༧ଌ → ·ͱ·ͬͨσʔλΛ·ͱΊͯ༧ଌ → API Λͬͯ1ͭͣͭ༧ଌ
Amazon Machine Learning ͷྉۚମܥ
Amazon Machine Learning ͷྉۚମܥ
1,000 σʔλͰϞσϧΛ࡞ͬͨͱ͖
70,000 σʔλͰϞσϧΛ࡞ͬͨͱ͖
S3 price
Amazon Machine Learning ΛͬͯΈͨײ 1. Α͘Ͱ͖ͯΔ 2. ͬ͘͞ͱϓϩτλΠϓ͍ͨ࣌͠ʹศརͦ͏ 3. ֶशࡁΈͷϞσϧΛΤΫεϙʔτͰ͖ͳ͍
Amazon Machine Learning ΛͬͯΈͨײ 1. Α͘Ͱ͖ͯΔ 2. ͬ͘͞ͱϓϩτλΠϓ͍ͨ࣌͠ʹศརͦ͏ → ΞϧΰϦζϜΛදʹग़ͣ͞ʹ্ख͘؆ུԽͯ͠Δ
3. ֶशࡁΈͷϞσϧΛΤΫεϙʔτͰ͖ͳ͍
Amazon Machine Learning ΛͬͯΈͨײ 1. Α͘Ͱ͖ͯΔ 2. ͬ͘͞ͱϓϩτλΠϓ͍ͨ࣌͠ʹศརͦ͏ → ΞϧΰϦζϜΛදʹग़ͣ͞ʹ্ख͘؆ུԽͯ͠Δ
→ ࣮ӡ༻લʹ༷ʑͳಛϕΫτϧΛ؆୯ʹࢼͤΔ 3. ֶशࡁΈͷϞσϧΛΤΫεϙʔτͰ͖ͳ͍
Amazon Machine Learning ΛͬͯΈͨײ 1. Α͘Ͱ͖ͯΔ 2. ͬ͘͞ͱϓϩτλΠϓ͍ͨ࣌͠ʹศརͦ͏ → ΞϧΰϦζϜΛදʹग़ͣ͞ʹ্ख͘؆ུԽͯ͠Δ
→ ࣮ӡ༻લʹ༷ʑͳಛϕΫτϧΛ؆୯ʹࢼͤΔ 3. ֶशࡁΈͷϞσϧΛΤΫεϙʔτͰ͖ͳ͍ → ࣮ӡ༻࣌ࣗͰ࣮ͨ͠ϞσϧΛ͏ ɹ ϓϩτλΠϓͰ্ख͘ߦ͖ͦ͏ͳ͜ͱ͕ ɹ ͔ͬͯΔͷͰ࣮ίετؾʹͳΒͳ͍!?