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
CCCの最終発表
Search
キンジョウショウタロウ
March 04, 2022
0
27
CCCの最終発表
2021に参加さしてもらったときの最終発表スライド(TrendOwl)
キンジョウショウタロウ
March 04, 2022
Tweet
Share
More Decks by キンジョウショウタロウ
See All by キンジョウショウタロウ
逆求人スライド
kinjyo
0
19
夏のゼミ課題
kinjyo
0
18
春学期最終プレゼンスライド
kinjyo
0
29
monthly-LT-LINE.pdf
kinjyo
0
30
NSEEMのLTの資料です
kinjyo
0
82
LINE_DC
kinjyo
0
23
easy_easy_re.pdf
kinjyo
0
260
easy_easy3.pdf
kinjyo
0
25
学校登壇.pdf
kinjyo
0
18
Featured
See All Featured
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
8
880
The Language of Interfaces
destraynor
159
25k
The MySQL Ecosystem @ GitHub 2015
samlambert
251
13k
Testing 201, or: Great Expectations
jmmastey
45
7.6k
Writing Fast Ruby
sferik
628
62k
Why You Should Never Use an ORM
jnunemaker
PRO
58
9.5k
Why Our Code Smells
bkeepers
PRO
338
57k
Unsuck your backbone
ammeep
671
58k
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
34
6k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
667
120k
Agile that works and the tools we love
rasmusluckow
329
21k
Fashionably flexible responsive web design (full day workshop)
malarkey
407
66k
Transcript
None
.FNCFST Α͏͔Μ ͿΜͿΜ ͍ʔͪΌΜ 5ZMFS Ωϯδϣ ͱΈͬ͘͢ ࣎լ ژ ౦ژ
ౡ aaࢲ͕ͳ͍ͯ͠·͢
None
IUUQTRJJUBDPNBCDTIPUBSPJUFNTGDCBDF
ເΛඳ͖͍ͨ ʮϑΝΫτνΣοΫͷຽओԽʯ Λࢦ͍ͯ͠·͢ʜ
੍࡞ 🤔ϑΣΠΫχϡʔεͱ
໋࣏ʹؔΘΔ͜ͱ ۽ຊ ถબڍ
ϫΫνϯࣗͷҙࢥͰଧͯΔ ͷʹɺଧͭͳͱڧ੍͞Εͯ͠ ·͍ͬͯΔʜ ࣮ࡍͷମݧஊ ͕ใݯΛͭʹཔΓ͗ͯ͢ɺ ภͬͨใͰ͢Α͏ʹͳΓɺ ձ͕Γཱͨͳ͍ ϝϯόʔͷ5͘Μ ༑ୡͷ/͞Μ
૯লͷൃදͰϲ݄ʹҎ্ͷਓϑΣΠΫχϡʔεʹ৮Ε͍ͯΔ Ҿ༻ɿIUUQTXXXTPVNVHPKQNBJO@TPTJLJKPIP@UTVTJOE@TZPIJJIPZVHBJ@IUNM ʢຊݚڀձʢୈճʣࢿྉ̍ʮʰϑΣΠΫχϡʔεʱʹؔ͢ΔΞϯέʔτௐࠪ݁Ռʯʢ/3*ʣʣ
ίϩφʹؔ͢ΔใͷਅِΛஅͰ͖ͳ͔ͬͨਓ͕ଟ͍ ɾίϩφՒͷຊͰɺ ʮ͓౬Ͱ৽ܕίϩφΠϧε͕ࢮ໓͢Δʯ ʮ৽ܕίϩφΠϧεͷӨڹͰ τΠϨοτϖʔύʔ͕ෆ͢Δʯ ͳͲͷِใ͕֦ࢄͨ͠ɻ
ɾ૯লͷௐࠪͰɺ͜ΕΒͷِใͷ͏ͪ ҰͭͰ৴ͨ͡ͱ͑ͨਓ ྸ͕͘ͳΔ΄Ͳଟ͘ɺ ̍̑ʙ̍̕ࡀͰ̏̒ʹٴΜͰ͍Δɻ Ҿ༻ɿIUUQTXXXTPVNVHPKQNBJO@DPOUFOUQEG ʢ৽ܕίϩφΠϧεײછʹؔ͢Δใྲྀ௨ௐࠪʣ
Ҿ༻ɿIUUQTXXXOBUVSFDPNBSUJDMFTT ʢ5IFPOMJOFDPNQFUJUJPOCFUXFFOQSPBOEBOUJWBDDJOBUJPOWJFXTʣ ϫΫνϯର͕͜ͷ··૿͑ଓ͚Δͱ͍ۙকདྷऔΓࠐ·Εͯ͠·͏ةݥੑ͕͋Δͱ͍͏ݚڀ݁Ռ😱
͍ۙকདྷͷةػײ ใੜଶܥͰੜ͞ΕΔใ͕૿େ͠ɺ ͔ͭෆ͔֬ͳใͷׂ߹͕ߴ͍ʢͭ·ΓϊΠζ͕ ଟ͍ʣใաଟͷঢ়ଶ͕ੜ͡Δ͜ͱͰɺِใΛ ࣄ࣮ͱޡೝͨ͠Γɺ͋Δ͍ࣄ࣮Λࣄ࣮ͩͱ৴͡ ΒΕͳ͍Α͏ͳ͜ͱ͕සൟʹى͜ΔΑ͏ʹͳΕ ɺৗੜ׆ܦࡁ׆ಈɺ͞Βʹຽओओٛʹ ෛͷӨڹ͕ٴͿՄೳੑ͕͋Δɻ
ʁ Έͳ͞ΜɺϑΣΠΫχϡʔε ରࡦͬͯ·͔͢ʁ ʁ
͍͍͑ ͍ ΠϯελάϥϜʹͯνʔϜϝϯόʔௐ ʮϑΣΠΫχϡʔεʹԿ͔͠ΒରࡦΛ͍ͯ͠Δ͔ʯ ߴߍੜɾେֶੜਓ
͔͠͠ϑΣΠΫχϡʔε ʹର͢ΔҙࣝΛ͍࣋ͬͯ Δਓ·ͩ·ͩগͳ͍ ௐࠪͷ݁Ռ ϑΣΠΫχϡʔεʹ৮ΕΔසʹʹ૿͍͑ͯΔ
ײओ؍ʹࠨӈ͞Εͣ ٬؍తͳࠜڌ Λ࣋ͬͯΒ͍͍ͨ Ծઆɾඪ ؆୯ʹϑΣΠΫχϡʔεఆʹ৮ΕΒΕΔ ڥ͕ඞཁͰ͋ΔͷͰͳ͍͔ɻ
੍࡞ ✌੍࡞
IUUQTUXJUUFSDPNUSFOE@PXM
σβΠϯͷϙΠϯτ ը૾ʹΛॻ͔ͳ͍ ҙࣄ߲ΛಡΜͰΒ͏ͨΊ ʹɺ͋͑ͯςΩετʹ͔݁͠ ՌΛॻ͖·ͤΜͰͨ͠ πΠʔτΛݟΔͱ͖ʹ ਓؒը૾͔ΒݟΔ 👎
੍࡞ 👩🎓ੳख๏
Ϟσϧºػցֶश Ҿ༻ɿIUUQTXXXSJLFMBCKQTUVEZ ਖ਼͍͠ใ '",&
IUUQTXXXBBBJPSHPDTJOEFYQIQ"""*"""*QBQFSWJFX'JMF அख๏ ɾΞΧϯτ͕ొ͞Ε͔ͯΒͲΕ͙Β͍͔ ɾݩπΠʔτ͔πΠʔτ͞Ε͔ͯΒͲΕ͙Β͍ܦա͍ͯ͠Δ͔ ɾೝূࡁΈΞΧϯτ͔Ͳ͏͔ ɾϓϩϑΟʔϧը૾Λࢦఆ͍ͯ͠Δ͔Ͳ͏͔ ɾ͍͍Ͷ͍ͯ͠Δ
ɾϑΥϩϫʔͷ ɾϑΥϩʔ͍ͯ͠Δ ɾ໊લͷจࣈ ɾࣗݾհͷจࣈ πΠʔτจষʹҰͱΒΘΕͣੳΛߦ͏ 📌 ࢀߟจ
੍࡞ ✍Ϟσϧͷਫ਼ɾಁ໌ੑ
ֶशϞσϧਫ਼͑ Ϟσϧͷ༧ଌ ࣮ ࡍ
ಁ໌ੑϨϙʔτΛ࡞ެ։ tensorboardXΛ༻͍ͨϞσϧͷՄࢹԽ
ੳ 🧠 πΠʔτऩू IUUQTUXJUUFSDPNUSFOE@PXM ϑΣΠΫੳ 🤖 ใࠂ
IUUQTUSFOEPXMTVHPLVOBSJUBJEFW ϙʔλϧαΠτ աڈͷੳ͕ݟΕΔ ੳϦΫΤετ͕ૹΕΔ
"NB[PO$MPVE8BUDI&WFOU πΠʔτ "NB[PO4 "NB[PO-BNCEB "NB[PO%ZOBNP%# ఆظ࣮ߦ ੳ ཧը໘ ϩά ը૾Ξοϓϩʔυ
ٕज़ߏ (PPHMF"QQ4DSJQU ϑϩϯτΤϯυ
੍࡞ 😁5SFOE0XMͷظ
ϑΣΠΫ χ ϡ ʔε ૣ ͘ ͕ Δ
ιʔείʔυϞσϧɺݟͷެ։ ڵຯΛ࣋ͬͯͩ͘͞ΔํΛ૿͠ ք۾શମͷٕज़ྗΛ্͛Δ ࠓޙͷల ҙݟΛΒ͏ɾΊΔ ݚڀػؔاۀͱͷڞಉݚڀ ༷ʑੈʹ֦ࢄ
ެ։ɾڞ༗͢Δ ਫ਼ΛߴΊΔ ࣗવݴޠॲཧͱϞσϧͷྑΛ ༻͍ͨϞσϧͷ։ൃ ΞϓϩʔνΛ૿͢ 5XJUUFS8FCҎ֎ͰͷϦϦʔε ༷ʑͳΞόλʔઃఆͰݕূΛߦ͏
None
༧උࢿྉ
ϑϩϯτΤϯυ Ϧʔμʔ ػցֶशɾαʔόʔαΠυ Α͏͔Μ ͿΜͿΜ ͍ʔͪΌΜ 5ZMFS Ωϯδϣ ͱΈͬ͘͢ αʔϕΠɾσβΠϯ
αʔϕΠɾϢʔβΠϯλϏϡʔ αʔόʔαΠυ
4DSBQCPY νʔϜ࿈ܞ /PUJPO 4MBDL
ͳͥɺʮϑΝΫτνΣοΫʯͱ͍͏ ΞϓϩʔνΛऔ͍ͬͯΔͷ͔ʁ
ใੜଶܥਐԽ͠ଓ͚͍ͯΔ Ҿ༻ɿIUUQTXXXEFMPJUUFDPNKQKBQBHFTTUSBUFHZBSUJDMFTDCTJOGPSNBUJPOFQJEFNJDIUNM ʢੈلͰສഒʹ૿େͨ͠ใୡྗʙใͷٸͳછʮΠϯϑΥσϛοΫʯͱʣ
None
օ͞ΜͷྗΛି͍ͯͩ͘͠͞ 5XJ UU FS ͷதͷਓͱ͓Γ߹͍ͷํ ޙ΄Ͳ͓ܨ͍͖͍͗ͨͩͨͰ͢ʂ
ࢲୡ͕͍ͬͯΔ͜ͱԿ͕ຊൃɺੈքॳͷϓϩμΫτͳͷ͔ ैདྷڝ߹ͱԿ͕ҧ͏ͷ طଘͷΈͱͷҧ͍ /-1 ࣗવݴޠॲཧ ͰͰ͖Δ͜ͱ طଘͷࣙॻɾίʔύεΛͱʹ จ຺Λղੳ͢Δ ٯʹݴ͑ ৽͍͠ࣄɾະͷʹ
ରॲͰ͖ͳ͍͔͠Εͳ͍
ࢲୡ͕͍ͬͯΔ͜ͱԿ͕ຊൃɺੈքॳͷϓϩμΫτͳͷ͔ ैདྷڝ߹ͱԿ͕ҧ͏ͷ طଘͷΈͱͷҧ͍ Ϟσϧղܾ͠·͢ɻ ະͷࣄ࣮ʹରͯ͠ ٬؍తͳੳ͕Մೳ
ࢲୡ͕͍ͬͯΔ͜ͱԿ͕ຊൃɺੈքॳͷϓϩμΫτͳͷ͔ ैདྷڝ߹ͱԿ͕ҧ͏ͷ ػցֶशϞσϧͷΈ 3// -45. ࠶ؼܕχϡʔϥϧ ωοτϫʔΫ ϩʔΧϧͳੳ $//
ΈࠐΈχϡʔϥϧ ωοτϫʔΫ άϩʔόϧͳੳ /-1Ͱকདྷతʹਫ਼্Λʂ [ࢀߟจݙ] Yang Liu, Yi-Fang Brook Wu (2018) Early Detection of Fake News on Social Media Through Propagation Path Classi fi cation with Recurrent and Convolutional Networks