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
450
Git and Github for Beginners
yoheimune
1
290
Data Science BOOTCAMP Practices
yoheimune
0
370
Machine Learning with Python
yoheimune
0
350
Python Basics for G's ACADEMY TOKYO
yoheimune
1
620
Other Decks in Technology
See All in Technology
サイバーエージェントグループのSRE10年の歩みとAI時代の生存戦略
shotatsuge
4
1.1k
LLM拡張解体新書/llm-extension-deep-dive
oracle4engineer
PRO
24
6.9k
Amplify Gen2から知るAWS CDK Toolkit Libraryの使い方/How to use the AWS CDK Toolkit Library as known from Amplify Gen2
fossamagna
1
370
"Découvrir le Liberland"
rlifchitz
0
110
Autify Company Deck
autifyhq
2
44k
AIでテストプロセス自動化に挑戦する
sakatakazunori
1
570
組織内、組織間の資産保護に必要なアイデンティティ基盤と関連技術の最新動向
fujie
0
370
Four Keysから始める信頼性の改善 - SRE NEXT 2025
ozakikota
0
420
SREのためのeBPF活用ステップアップガイド
egmc
2
1.3k
RapidPen: AIエージェントによる高度なペネトレーションテスト自動化の研究開発
laysakura
1
240
SRE with AI:実践から学ぶ、運用課題解決と未来への展望
yoshiiryo1
1
420
モニタリング統一への道のり - 分散モニタリングツール統合のためのオブザーバビリティプロジェクト
niftycorp
PRO
1
540
Featured
See All Featured
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
282
13k
How GitHub (no longer) Works
holman
314
140k
Put a Button on it: Removing Barriers to Going Fast.
kastner
60
3.9k
Testing 201, or: Great Expectations
jmmastey
43
7.6k
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
PRO
181
54k
Build The Right Thing And Hit Your Dates
maggiecrowley
37
2.8k
Principles of Awesome APIs and How to Build Them.
keavy
126
17k
Thoughts on Productivity
jonyablonski
69
4.7k
Visualization
eitanlees
146
16k
Mobile First: as difficult as doing things right
swwweet
223
9.7k
[RailsConf 2023] Rails as a piece of cake
palkan
55
5.7k
StorybookのUI Testing Handbookを読んだ
zakiyama
30
5.9k
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