$30 off During Our Annual Pro Sale. View Details »
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
220
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
320
Machine Learning Basic and Python
yoheimune
1
530
Python Scraping and Web Apps for G's ACADEMY TOKYO
yoheimune
0
250
DevelopWorkflow and Solving Problems
yoheimune
0
460
Git and Github for Beginners
yoheimune
1
310
Data Science BOOTCAMP Practices
yoheimune
0
380
Machine Learning with Python
yoheimune
0
370
Python Basics for G's ACADEMY TOKYO
yoheimune
1
640
Other Decks in Technology
See All in Technology
AWS Bedrock AgentCoreで作る 1on1支援AIエージェント 〜Memory × Evaluationsによる実践開発〜
yusukeshimizu
6
370
プロダクトマネジメントの分業が生む「デリバリーの渋滞」を解消するTPMの越境
recruitengineers
PRO
3
720
Karate+Database RiderによるAPI自動テスト導入工数をCline+GitLab MCPを使って2割削減を目指す! / 20251206 Kazuki Takahashi
shift_evolve
PRO
1
500
Haskell を武器にして挑む競技プログラミング ─ 操作的思考から意味モデル思考へ
naoya
1
450
直接メモリアクセス
koba789
0
280
バグハンター視点によるサプライチェーンの脆弱性
scgajge12
3
1k
Reinforcement Fine-tuning 基礎〜実践まで
ch6noota
0
150
Microsoft Agent 365 を 30 分でなんとなく理解する
skmkzyk
1
1k
法人支出管理領域におけるソフトウェアアーキテクチャに基づいたテスト戦略の実践
ogugu9
1
210
ML PM Talk #1 - ML PMの分類に関する考察
lycorptech_jp
PRO
1
720
Playwrightのソースコードに見る、自動テストを自動で書く技術
yusukeiwaki
13
4.9k
re:Inventで気になったサービスを10分でいけるところまでお話しします
yama3133
1
120
Featured
See All Featured
Become a Pro
speakerdeck
PRO
31
5.7k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
37
2.6k
Statistics for Hackers
jakevdp
799
230k
Thoughts on Productivity
jonyablonski
73
5k
Context Engineering - Making Every Token Count
addyosmani
9
490
Writing Fast Ruby
sferik
630
62k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
10
720
It's Worth the Effort
3n
187
29k
Leading Effective Engineering Teams in the AI Era
addyosmani
8
1.3k
[SF Ruby Conf 2025] Rails X
palkan
0
490
Designing for Performance
lara
610
69k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
31
2.6k
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