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
Search
Yohei Munesada
April 28, 2017
Science
0
340
Data Science BOOTCAMP Practices
データサイエンス・機械学習の演習説明です。
http://www.sompo.io/bootcamp/
Yohei Munesada
April 28, 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
5.1k
How to create a service, How to google !
yoheimune
0
270
Machine Learning Basic and Python
yoheimune
1
460
Python Scraping and Web Apps for G's ACADEMY TOKYO
yoheimune
0
220
DevelopWorkflow and Solving Problems
yoheimune
0
420
Git and Github for Beginners
yoheimune
1
270
Data Science BOOTCAMP Practices - Recommendation
yoheimune
0
180
Machine Learning with Python
yoheimune
0
310
Python Basics for G's ACADEMY TOKYO
yoheimune
1
570
Other Decks in Science
See All in Science
Sarcoptic Mange
uni_of_nomi
1
110
Machine Learning for Materials (Lecture 9)
aronwalsh
0
210
The Incredible Machine: Developer Productivity and the Impact of AI
tomzimmermann
0
390
(2024) Livres, Femmes et Math
mansuy
0
110
Improving Search @scale with efficient query experimentation @BerlinBuzzwords 2024
searchhub
0
240
生成AI による論文執筆サポートの手引き(ワークショップ) / A guide to supporting dissertation writing with generative AI (workshop)
ks91
PRO
0
250
科学で迫る勝敗の法則(名城大学公開講座.2024年10月) / The principle of victory discovered by science (Open lecture in Meijo Univ. 2024)
konakalab
0
200
教師なしテンソル分解に基づく、有糸分裂後の転写再活性化におけるヒストン修飾ブックマークとしての転写因子候補の抽出法
tagtag
0
120
Machine Learning for Materials (Lecture 6)
aronwalsh
0
510
大規模画像テキストデータのフィルタリング手法の紹介
lyakaap
6
1.5k
Cross-Media Information Spaces and Architectures (CISA)
signer
PRO
3
29k
Boil Order
uni_of_nomi
0
120
Featured
See All Featured
Code Review Best Practice
trishagee
64
17k
Keith and Marios Guide to Fast Websites
keithpitt
409
22k
[RailsConf 2023] Rails as a piece of cake
palkan
52
4.9k
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
47
2.1k
Speed Design
sergeychernyshev
24
610
Imperfection Machines: The Place of Print at Facebook
scottboms
265
13k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
10
720
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
42
9.2k
Docker and Python
trallard
40
3.1k
Build your cross-platform service in a week with App Engine
jlugia
229
18k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
26
1.4k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
44
6.8k
Transcript
Data Science BOOTCAMP ΞϓϦέʔγϣϯ੍࡞ԋश Yohei Munesada
About Me 㾎फఆ༸ฏ ΉͶͩ͞Α͏͍ 㾎 ג αΠόʔΤʔδΣϯτ 㾎(`TΞΧσϛʔϝϯλʔ 㾎IUUQXXXZPIFJNOFU 㾎ͱσʔλαΠΤϯε
िؒɺΈͳ͞·͍͔͕Ͱͨ͠Ͱ͠ΐ͏͔ʁ
May think as … 㾎ֶతͳجૅΛड͚͖ͯͨɻ 㾎Ӭా͞ΜߨٛͰ৭ʑͱख๏ΛֶΜͰ͖ͨɻ 㾎ߨٛதͷԋशΛղ͍͚ͨͲɺͬͱ͍ͯ͠Δͱ͜Ζ͋Δɻ 㾎੍࡞ԋशΛ௨ͯ͠ɺʹ͚͍ͨͱ͜Ζʂ
May think as … ͦ͏ͩʂԿ͔࡞ͬͯΈΑ͏ʂ
Exercises .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε ҙͷެ։σʔλΛ༻͍ͨػցֶश ػցֶशܥΫϥυ"1*Λ༻͍ͨαʔϏε։ൃ ඞਢ՝
બ՝
Objective ՌΛग़͢͜ͱ ϑϩʔʹԊͬͨ࡞ۀεςοϓΛ౿Ή͜ͱ
ϑϩʔʹԊͬͨ࡞ۀ
How to ԋशʹऔΓΉͷݸਓͰ ൃදάϧʔϓͰ
Schedule .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷൃද 5VF ϫʔΫ࣭࣌ؒٙԠλΠϜ 8FE ҙ՝ͷൃද 'SJ
Exercises - MovieLens .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ඞਢ՝ .PWJF-FOTͱ͍͏ެ։σʔλʹɺөըͷใɺϢʔβʔͷөըʹର͢Δใ ͳͲؚ͕·Ε·͢ɻͦΕΒσʔλΛ༻͍ͯϨίϝϯυγεςϜΛߏங͍ͯͩ͘͠͞ɻ ٻΊΔΞτϓοτ ɹɾϢʔβʔʹରͯ͠өըΛਪન͢Δ
ϙΠϯτ ɹɾਪનʹ͍ͭͯͲͷΑ͏ʹػցֶशͱͯ͠ఆٛ͢Δ͔ʁ ɹɾͳͥͦͷϞσϧΛબ͢Δͷ͔ʁ ɹɾ༧ଌ݁ՌͷධՁ݁ՌʁͲͷΑ͏ʹධՁ͢Εྑ͍͔ʁ
Exercises - MovieLens ར༻Մೳͳσʔλ ɹIUUQTHSPVQMFOTPSHEBUBTFUTNPWJFMFOT .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ඞਢ՝
Exercises - MovieLens
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 Exercises - ࠃௐࠪ ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε બ՝
Exercises - ࠃௐࠪ
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 Exercises
- ҙͷσʔλͰʂ ɹ,BHHMF ɹɹIUUQTXXXLBHHMFDPNEBUBTFUT ɹ-JWFEPPSχϡʔε ɹɹIUUQOFXTMJWFEPPSDPN ɹ౦ژϝτϩΦʔϓϯσʔλ ɹɹIUUQTEFWFMPQFSUPLZPNFUSPBQQKQJOGP ɹ5XJUUFS"1*ɺͳͲ ҙͷެ։σʔλΛ༻͍ͨػցֶश બ՝
Exercises - ҙͷσʔλͰʂ
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
Exercises - ػցֶशAPIΛͬͯʂ
Exercises બ՝͕͔͔࣌ؒΓ·͢ͷͰɺ ͓ૣΊʹʂ .PWJF-FOTΛ༻͍ͨϨίϝϯσʔγϣϯͷߏங ࠃௐࠪσʔλΛ༻͍ͨσʔλαΠΤϯε ҙͷެ։σʔλΛ༻͍ͨػցֶश
ػցֶशܥΫϥυ"1*Λ༻͍ͨαʔϏε։ൃ ඞਢ՝ બ՝
Q and A ࣭ٙԠλΠϜ
Team Building άϧʔϓ͚Λ͠·͢ ʢʙਓఔʣ
Team Building ࣗݾհͱσΟεΧογϣϯ
Thank you ͦΕͰྑ͍σʔλαΠΤϯεΛʂ