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
240
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
5.4k
How to create a service, How to google !
yoheimune
0
320
Machine Learning Basic and Python
yoheimune
1
540
Python Scraping and Web Apps for G's ACADEMY TOKYO
yoheimune
0
250
DevelopWorkflow and Solving Problems
yoheimune
0
470
Git and Github for Beginners
yoheimune
1
320
Data Science BOOTCAMP Practices
yoheimune
0
390
Machine Learning with Python
yoheimune
0
380
Python Basics for G's ACADEMY TOKYO
yoheimune
1
650
Other Decks in Technology
See All in Technology
Go標準パッケージのI/O処理をながめる
matumoto
0
240
生成AIで速度と品質を両立する、QAエンジニア・開発者連携のAI協調型テストプロセス
shota_kusaba
0
240
Laravelで学ぶOAuthとOpenID Connectの基礎と実装
kyoshidaxx
1
660
詳解 強化学習 / In-depth Guide to Reinforcement Learning
prinlab
0
320
AI時代のSaaSとETL
shoe116
1
200
AI時代の「本当の」ハイブリッドクラウド — エージェントが実現した、あの頃の夢
ebibibi
0
150
生成AI活用でQAエンジニアにどのような仕事が生まれるか/Support Required of QA Engineers for Generative AI
goyoki
1
310
The_Evolution_of_Bits_AI_SRE.pdf
nulabinc
PRO
0
250
僕、S3 シンプルって名前だけど全然シンプルじゃありません よろしくお願いします
yama3133
1
230
【社内勉強会】新年度からコーディングエージェントを使いこなす - 構造と制約で引き出すClaude Codeの実践知
nwiizo
1
350
20260311 技術SWG活動報告(デジタルアイデンティティ人材育成推進WG Ph2 活動報告会)
oidfj
0
370
VPCエンドポイント意外とお金かかるなぁ。せや、共有したろ!
tommy0124
1
710
Featured
See All Featured
WCS-LA-2024
lcolladotor
0
480
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
35
3.4k
Efficient Content Optimization with Google Search Console & Apps Script
katarinadahlin
PRO
1
420
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
287
14k
Redefining SEO in the New Era of Traffic Generation
szymonslowik
1
250
Game over? The fight for quality and originality in the time of robots
wayneb77
1
140
Test your architecture with Archunit
thirion
1
2.2k
Sam Torres - BigQuery for SEOs
techseoconnect
PRO
0
220
Fantastic passwords and where to find them - at NoRuKo
philnash
52
3.6k
Navigating the moral maze — ethical principles for Al-driven product design
skipperchong
2
290
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
254
22k
Practical Orchestrator
shlominoach
191
11k
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