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
Data Science BOOTCAMP Practices - Recommendation
Search
Yohei Munesada
May 09, 2017
Technology
0
210
Data Science BOOTCAMP Practices - Recommendation
レコメンデーションの制作演習のスライドです。中に解答例のリンクも掲載しています。
G's Academy Data Science Bootcamp
Yohei Munesada
May 09, 2017
Tweet
Share
More Decks by Yohei Munesada
See All by Yohei Munesada
G'sデータベース設計の講義
yoheimune
4
5.3k
How to create a service, How to google !
yoheimune
0
300
Machine Learning Basic and Python
yoheimune
1
510
Python Scraping and Web Apps for G's ACADEMY TOKYO
yoheimune
0
240
DevelopWorkflow and Solving Problems
yoheimune
0
440
Git and Github for Beginners
yoheimune
1
290
Data Science BOOTCAMP Practices
yoheimune
0
360
Machine Learning with Python
yoheimune
0
350
Python Basics for G's ACADEMY TOKYO
yoheimune
1
610
Other Decks in Technology
See All in Technology
Azure AI Foundryでマルチエージェントワークフロー
seosoft
0
150
Snowflake Summit 2025 データエンジニアリング関連新機能紹介 / Snowflake Summit 2025 What's New about Data Engineering
tiltmax3
0
240
_第3回__AIxIoTビジネス共創ラボ紹介資料_20250617.pdf
iotcomjpadmin
0
140
Snowflake Summit 2025全体振り返り / Snowflake Summit 2025 Overall Review
mtpooh
2
210
(非公式) AWS Summit Japan と 海浜幕張 の歩き方 2025年版
coosuke
PRO
1
340
Agentic Workflowという選択肢を考える
tkikuchi1002
1
390
IAMのマニアックな話 2025を執筆して、 見えてきたAWSアカウント管理の現在
nrinetcom
PRO
4
660
AIにどこまで任せる?実務で使える(かもしれない)AIエージェント設計の考え方
har1101
3
1.3k
[TechNight #90-1] 本当に使える?ZDMの新機能を実践検証してみた
oracle4engineer
PRO
3
140
Prox Industries株式会社 会社紹介資料
proxindustries
0
210
より良いプロダクトの開発を目指して - 情報を中心としたプロダクト開発 #phpcon #phpcon2025
bengo4com
1
380
Amplifyとゼロからはじめた AIコーディング 成果と展望
mkdev10
1
360
Featured
See All Featured
Navigating Team Friction
lara
187
15k
The Straight Up "How To Draw Better" Workshop
denniskardys
233
140k
Raft: Consensus for Rubyists
vanstee
140
7k
The Language of Interfaces
destraynor
158
25k
Mobile First: as difficult as doing things right
swwweet
223
9.7k
Code Reviewing Like a Champion
maltzj
524
40k
Music & Morning Musume
bryan
46
6.6k
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
29
1.8k
Keith and Marios Guide to Fast Websites
keithpitt
411
22k
The Art of Programming - Codeland 2020
erikaheidi
54
13k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
48
5.4k
Transcript
Data Science BOOTCAMP Ϩίϝϯσʔγϣϯ࡞ Yohei Munesada
About Me 㾎फఆ༸ฏ ΉͶͩ͞Α͏͍ 㾎 ג αΠόʔΤʔδΣϯτ 㾎(`TΞΧσϛʔϝϯλʔ 㾎IUUQXXXZPIFJNOFU 㾎ͱσʔλαΠΤϯε
Time tables 19:30ʙ19:40ɹΦʔϓχϯάͱࠓͷׂ࣌ؒ 19:40ʙ19:50ɹάϧʔϓϫʔΫઆ໌ 19:50ʙ20:30ɹάϧʔϓϫʔΫʢൃද४උʣ 20:30ʙ20:40ɹٳܜ 20:40ʙ21:30ɹάϧʔϓผൃදʢ5 x 7νʔϜ +
αʣ 21:30ʙ21:40ɹ࣍ͷ՝ͷઆ໌ʢ͞Βͬͱʣ 21:40ʙ21:50ɹάϧʔϓϫʔΫʢऔΓΈ༰ͷڞ༗ͱϒϥογϡΞοϓʣ 21:50ʙ22:00ɹऔΓΈ༰ͷൃදʢ30ඵ x 7νʔϜ + αʣ
Exercises - MovieLens .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ඞਢ՝ .PWJF-FOTͱ͍͏ެ։σʔλʹɺөըͷใɺϢʔβʔͷөըʹର͢Δใ ͳͲؚ͕·Ε·͢ɻͦΕΒσʔλΛ༻͍ͯϨίϝϯυγεςϜΛߏங͍ͯͩ͘͠͞ɻ ٻΊΔΞτϓοτ ɹɾϢʔβʔʹରͯ͠өըΛਪન͢Δ
ϙΠϯτ ɹɾਪનʹ͍ͭͯͲͷΑ͏ʹػցֶशͱͯ͠ఆٛ͢Δ͔ʁ ɹɾͳͥͦͷϞσϧΛબ͢Δͷ͔ʁ ɹɾ༧ଌ݁ՌͷධՁ݁ՌʁͲͷΑ͏ʹධՁ͢Εྑ͍͔ʁ
Exercises - MovieLens
Presentation contents ʢՄೳͰͨ͠ΒʣσϞ ͲͷΑ͏ͳػցֶशͱͯ͠ఆ͔ٛͨ͠ʁ ͲͷΑ͏ͳ࣮Λ͔ͨ͠ʁ ͲͷΑ͏ʹϞσϧΛධՁ͔ͨ͠ʁ
ͨ͠ͱ͜Ζɺۤ࿑ͨ͠ͱ͜Ζ ͦͷଞओு͍ͨ͜͠ͱΛͲ͏ͧʂ
Group work ݸਓͰͷՌΛνʔϜͰൃද͢Δ νʔϜͱͯ͠ͷൃද༰Λ࡞͢ΔʢϓϨθϯܗࣜࣗ༝ʣ άϧʔϓϫʔΫΛߦ͍·͢ ʢʙʣ ʢՄೳͰͨ͠ΒʣσϞ
ͲͷΑ͏ͳػցֶशͱͯ͠ఆ͔ٛͨ͠ʁ ͲͷΑ͏ͳ࣮Λ͔ͨ͠ʁ ͲͷΑ͏ʹϞσϧΛධՁ͔ͨ͠ʁ ͨ͠ͱ͜Ζɺۤ࿑ͨ͠ͱ͜Ζ ͦͷଞओு͍ͨ͜͠ͱΛͲ͏ͧʂ ϓϨθϯ༰
Take a break ͓ർΕ༷Ͱͨ͠ɺٳܜͰ͢ ʢʙʣ
How is your recommend system ? ൃදͷ͓࣌ؒͰ͢ʂ
How is your recommend system ? ղྫ https://goo.gl/4jGdHI
Next exercises .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε ҙͷެ։σʔλΛ༻͍ͨػցֶश ػցֶशܥΫϥυ"1*Λ༻͍ͨαʔϏε։ൃ
ඞਢ՝ બ՝
Next exercises - ࠃௐࠪ ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε બ՝ ࠃௐࠪσʔλ͔ΒਓޱɺՈߏɺ৬ۀͳͲ༷ʑͳใΛಘΔ͜ͱ͕Ͱ͖·͢ɻ ԿΒ͔ͷϏδωε՝Λఆٛͨ͠ͷͪʹɺࠃௐࠪσʔλΛ༻͍ͯϏδωεͷ ҙࢥܾఆΛॿ͚ΔใΛఏ͍ࣔͯͩ͘͠͞ɻ
ٻΊΔΞτϓοτ ɹɾఆٛͨ͠Ϗδωε՝Կ͔ʁ ɹɾͦΕʹରͯ͠ࠃௐࠪσʔλΛͲͷΑ͏ʹ׆༻͔ͨ͠ʁ Ϗδωε՝ྫ ɹɾ*5ڭҭϏδωεΛల։͍ͨ͠ɻͲͷࢢொଜΛλʔήοτʹ͢Δ͖͔ʁ ɹɾϑΟϦϐϯਓʹ͚ͨΧϑΣϏδωεΛߦ͍͍ͨɻͲ͜ͰΔ͔ʁ ɹɾͳͲ
ར༻Մೳͳσʔλ ɹIUUQXXXTUBUHPKQEBUBLPLVTFJJOEFYIUN Next exercises - ࠃௐࠪ ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε બ՝
Next exercises - ࠃௐࠪ
Next exercises - ҙͷσʔλͰʂ ҙͷެ։σʔλΛ༻͍ͨػցֶश બ՝ ੈͷதʹ༷ʑͳσʔλ͕ެ։͞Ε͓ͯΓɺػցֶशʹར༻Ͱ͖Δσʔλ ଟʑଘࡏ͠·͢ɻڵຯͷ͋Δσʔλʹ͍ͭͯԾઆΛఆٛͯ͠ػցֶशΛߦ͍ɺ ԿΒ͔ͷՌΛग़͢औΓΈΛ͍ͯͩ͘͠͞ɻ
ٻΊΔΞτϓοτ ɹɾͲͷΑ͏ͳσʔλΛ͏͔ʁ ɹɾͲΜͳԾઆΛઃఆ͔ͨ͠ʁ ɹɾͲͷΑ͏ͳՌΛಋ͍ͨͷ͔ʁ·ͨͦΕΛͲͷΑ͏ʹಋ͍ͨͷ͔ʁ
ར༻Մೳͳσʔλྫ ɹ6$*.BDIJOF-FBSOJOH ɹɹIUUQBSDIJWFJDTVDJFEVNM ɹࠃཱใֶݚڀॴ ɹɹIUUQXXXOJJBDKQETDJESEBUBMJTUIUNM ɹ%"5"(0+1 ɹɹIUUQXXXEBUBHPKQ ɹ*NBHF/FU ɹɹIUUQXXXJNBHFOFUPSH Next
exercises - ҙͷσʔλͰʂ ɹ,BHHMF ɹɹIUUQTXXXLBHHMFDPNEBUBTFUT ɹ-JWFEPPSχϡʔε ɹɹIUUQOFXTMJWFEPPSDPN ɹ౦ژϝτϩΦʔϓϯσʔλ ɹɹIUUQTEFWFMPQFSUPLZPNFUSPBQQKQJOGP ɹ5XJUUFS"1*ɺͳͲ ҙͷެ։σʔλΛ༻͍ͨػցֶश બ՝
Next exercises - ҙͷσʔλͰʂ
Next exercises - ػցֶशAPIΛͬͯʂ ػցֶशܥΫϥυ"1*Λ༻͍ͨαʔϏε։ൃ બ՝ (PPHMF"84"[VSF#JOH*#.ͷ֤αʔϏεͰػցֶशܥͷ"1*͕ ఏڙ͞Ε͍ͯΔʢྫɿإೝࣝɺԻೝࣝɺςΩετUPεϐʔνɺFUDʣɻ ͜ΕΒͷ"1*Λ͍ɺԿΒཱ͔ͪͦ͏ͳΞϓϦαʔϏεΛ੍࡞͍ͯͩ͘͠͞ɻ
ٻΊΔΞτϓοτ ɹɾͲͷ"1*Λར༻͢Δͷ͔ʁ ɹɾԿʹཱͯΔͷ͔ʁͲͷΑ͏ͳαʔϏε͔ʁ ग़ҙਤ ɹɾֶशࡁΈͷϞσϧΛͲͷΑ͏ʹ࣮ੈքͰ׆͔͢ͷ͔ɺͦΕΛߟ͑ߦಈ͢Δɻ
Next exercises - ػցֶशAPIΛͬͯʂ
Next exercises .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε ҙͷެ։σʔλΛ༻͍ͨػցֶश ػցֶशܥΫϥυ"1*Λ༻͍ͨαʔϏε։ൃ
ඞਢ՝ બ՝
Group work ݸਓͦΕͧΕͰऔΓΜͰ͍Δ༰ʢऔΓΉ༰ʣΛڞ༗ ൃද༰·ͱΊʢϓϨθϯܗࣜޱ಄Ͱʣ άϧʔϓϫʔΫΛߦ͍·͢ ʢʙʣ
Group work ൃදʢͲͷΑ͏ͳ༰Λѻ͏͔ʣ άϧʔϓϫʔΫΛߦ͍·͢ ʢʙʣ
Thank you ͦΕͰྑ͍σʔλαΠΤϯεΛʂ