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
Sponsored
·
Ship Features Fearlessly
Turn features on and off without deploys. Used by thousands of Ruby developers.
→
Kenta Murata
April 21, 2015
Technology
5.3k
17
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
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
More Decks by Kenta Murata
See All by Kenta Murata
waitany と waitall を作った話
mrkn
0
330
HolidayJp.jl を作りました
mrkn
0
370
Calling Julia functions from Streamlit applications
mrkn
1
610
Red Data Tools で切り開く Ruby の未来
mrkn
3
1.3k
Method-based JIT compilation by transpiling to Julia
mrkn
0
9.2k
Apache Arrow C++ Datasets
mrkn
4
1.9k
Reducing ActiveRecord memory consumption using Apache Arrow
mrkn
0
1.9k
RubyData and Rails
mrkn
0
3.5k
Tensor and Arrow
mrkn
0
1.1k
Other Decks in Technology
See All in Technology
書籍セキュアAPIについて
riiimparm
0
250
AI工学特論: MLOps・継続的評価
asei
5
1.2k
「休む」重要さ
smt7174
6
1.6k
AIが当たり前の組織で エンジニアはどう育つか
nishihira
1
970
大量データに対しても、生成AIを用いてリーズナブルにデータ加工をしたい!Databricksのai_queryについて調べてみた
kamoshika
1
280
10年目を迎えた「ABEMA」がどのように AI 活用を推進して、AI 駆動開発にシフトしているのか / How ABEMA, entering its 10th year, is promoting the use of AI and shifting toward AI-driven development
miyukki
0
370
そのドキュメント、自動化しませんか?
yuksew
1
410
CDKで書くECSのベストプラクティス、 改めて考え直す2026 #cdkconf2026
makies
3
940
SoccerMaster: A Vision Foundation Model for Soccer Understanding
kzykmyzw
0
170
AI_Dev_Day_製造業領域でのAI活用から見た活用の罠と成功に導く実践知.pdf
kintotechdev
0
160
AIエージェントがあれば技術書なんてすぐ書けるでしょ→無理でした
watany
2
270
AI時代こそ、スケールしないことをしよう -「作る人」から「なぜ作るか」を考える人へ / Do Things That Don't Scale in the AI Era — From How to Why
kaminashi
1
100
Featured
See All Featured
Mobile First: as difficult as doing things right
swwweet
225
10k
Design in an AI World
tapps
1
270
How to make the Groovebox
asonas
2
2.3k
Have SEOs Ruined the Internet? - User Awareness of SEO in 2025
akashhashmi
0
400
Introduction to Domain-Driven Design and Collaborative software design
baasie
1
910
Agile Leadership in an Agile Organization
kimpetersen
PRO
0
190
Optimizing for Happiness
mojombo
378
71k
Effective software design: The role of men in debugging patriarchy in IT @ Voxxed Days AMS
baasie
0
450
It's Worth the Effort
3n
188
29k
Automating Front-end Workflow
addyosmani
1370
210k
New Earth Scene 8
popppiees
3
2.4k
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
28
3.6k
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!?