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
成長を止めない機械学習のやり方 / Don't stop 'til you get enoug...
Search
Yuichiro Someya
March 23, 2018
Programming
15
5.2k
成長を止めない機械学習のやり方 / Don't stop 'til you get enough (data).
https://manabiya.tech
Yuichiro Someya
March 23, 2018
Tweet
Share
More Decks by Yuichiro Someya
See All by Yuichiro Someya
にんげんがさき 基盤はあと / Developers over ML platform
ayemos
0
14k
機械学習をスモールスタートさせる方法 / small machine learning
ayemos
3
2k
アットホームな分析基盤の作り方 / Homemade Machine Learning Toolkits
ayemos
1
970
サービス開発、機械学習、クラウド / the trinity of machine learning
ayemos
0
3.4k
AWS で加速する機械学習 / Accelerate Machine Learning with AWS
ayemos
1
320
クックパッドの機械学習基盤 2018 / Machine Learning Platform at Cookpad ~ 2018 ~
ayemos
15
20k
PyTorchとCaffe2とONNXと深層学習モデルのデプロイについて
ayemos
1
3k
クックパッドにおけるAWS GPUインスタンスの利用事例 / Powering by AWS GPU Instances in Cookpad Inc
ayemos
0
420
How we use GPUs in Cookpad
ayemos
0
140
Other Decks in Programming
See All in Programming
The Missing Link in Angular’s Signal Story: Resource API and httpResource
manfredsteyer
PRO
0
140
API for docs
soutaro
3
1.6k
Orleans + Sekiban + SignalR でリアルタイムWeb作ってみた
tomohisa
0
230
UMAPをざっくりと理解 / Overview of UMAP
kaityo256
PRO
3
1.4k
note の Elasticsearch 更新系を支える技術
tchov
9
3.4k
Bedrock × Confluenceで簡単(?)社内RAG
iharuoru
1
110
The Implementations of Advanced LR Parser Algorithm
junk0612
1
1.3k
Dissecting and Reconstructing Ruby Syntactic Structures
ydah
3
2k
Make Parsers Compatible Using Automata Learning
makenowjust
2
6.9k
iOSアプリで測る!名古屋駅までの 方向と距離
ryunakayama
0
150
AIコーディングの理想と現実
tomohisa
35
37k
Flutterでllama.cppをつかってローカルLLMを試してみた
sakuraidayo
0
120
Featured
See All Featured
Music & Morning Musume
bryan
47
6.5k
Automating Front-end Workflow
addyosmani
1370
200k
RailsConf 2023
tenderlove
30
1.1k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
60k
Faster Mobile Websites
deanohume
306
31k
Learning to Love Humans: Emotional Interface Design
aarron
273
40k
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
29
2.6k
Visualization
eitanlees
146
16k
Docker and Python
trallard
44
3.4k
Code Review Best Practice
trishagee
67
18k
Build The Right Thing And Hit Your Dates
maggiecrowley
35
2.7k
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
5
590
Transcript
ΛࢭΊͳ͍ ػցֶशͷΓํ ΫοΫύουגࣜձࣾɹછ୩༔Ұ ."/"#*:"
ࣗݾհ છ୩༔Ұ<:VJDIJSP4PNFZB> ౦ژۀେֶେֶӃܭࢉֶम࢜ ΫοΫύουגࣜձࣾݚڀ։ൃ෦ ϦαʔνΤϯδχΞ݄d ػցֶशج൫
Ϩγϐσʔλͷੳ UXJUUFSDPN!BZFNPT@Z HJUIVCDPNBZFNPT IUUQTXXXBZFNPTNF
None
ϨγϐɿສҎ্ ࠃͷ݄ؒར༻ऀɿສਓ
ରԠݴޠɿݴޠϲࠃ ւ֎ͷ݄ؒར༻ऀɿສਓҎ্
ΫοΫύουݚڀ։ൃ෦ ݄ʹൃ ࢲ͕ଐ͞Εͨͷಉ࣌ظ ໊࣌ͷϝϯόʔ ݄ݱࡏࠃʹ໊ ւ֎ʹ໊
ྉཧ͖Ζ͘ ΈࠐΈχϡʔϥϧωοτϫʔΫ ʹΑΔྉཧը૾ͷࣗಈೝࣝ εϚʔτϑΥϯͷࣸਅͷɺ ྉཧࣸਅΛࣗಈతʹऩूه
ྉཧ͖Ζ͘ ສਓҎ্ͷϢʔβʔ͕ར༻ ສຕҎ্ͷྉཧࣸਅΛه ݄ݱࡏ
ͦͷଞػցֶशϓϩδΣΫτ ը૾ੳʹΑΔϨγϐͷྨ ࡐྉ໊ͷਖ਼نԽ <͓͠ΐ͏Ώ ে༉ γϣʔϢ><͠ΐ͏Ώ>
ϑΟʔυόοΫͷࣗಈλά͚ FUD
લஔ͖
l લུ ʮ࣭ʯ·Ͱ͍͔ͳ͍͕ؾʹͳ͍ͬͯΔ͜ͱɺ ʮճʯΛΓ͍͕ͨͳ͔ͳ͔Δػձ͕ͳ͔ͬͨ͜ͱɻ ͦ͏͍ͬͨٙΛຊத͔ΒूΊɺ ΧϯϑΝϨϯεΛ௨ͯ͡ղܾࡦΛݟग़͠ɺܙΛੜΈग़͢ɻ ͜Ε͕ɺ."/"#*:"Λ։࠵͢Δ͍Ͱ͢ɻz IUUQTNBOBCJZBUFDI
l લུ ʮ࣭ʯ·Ͱ͍͔ͳ͍͕ؾʹͳ͍ͬͯΔ͜ͱɺ ʮճʯΛΓ͍͕ͨͳ͔ͳ͔Δػձ͕ͳ͔ͬͨ͜ͱɻ ͦ͏͍ͬͨٙΛຊத͔ΒूΊɺ ΧϯϑΝϨϯεΛ௨ͯ͡ղܾࡦΛݟग़͠ɺܙΛੜΈग़͢ɻ ͜Ε͕ɺ."/"#*:"Λ։࠵͢Δ͍Ͱ͢ɻz IUUQTNBOBCJZBUFDI ՝ͷڞ༗
ղܾࡦͷྻڍͱੳ
lຊηογϣϯͰ·ͣػցֶशΛऔΓೖΕͨ৫ʹ͓͚Δ ՁΛ્͋Δ͍ಷԽͤ͞Δز͔ͭͷཁҼΛࢦఠ͠ɺ ͦΕΒͷཁҼʹ͍ͭͯ։ൃج൫ΤϯδχΞϦϯάɺ νʔϜߏ༷ʑͳ͔֯Βߟ͠·͢ɻ·ͨ͞Βʹɺ ͦͷΑ͏ͳ՝ʹର͢ΔΫοΫύουͷऔΓΈΛհ͠·͢ɻz
lຊηογϣϯͰ·ͣػցֶशΛऔΓೖΕͨ৫ʹ͓͚Δ ՁΛ્͋Δ͍ಷԽͤ͞Δز͔ͭͷཁҼΛࢦఠ͠ɺ ͦΕΒͷཁҼʹ͍ͭͯ։ൃج൫ΤϯδχΞϦϯάɺ νʔϜߏ༷ʑͳ͔֯Βߟ͠·͢ɻ·ͨ͞Βʹɺ ͦͷΑ͏ͳ՝ʹର͢ΔΫοΫύουͷऔΓΈΛհ͠·͢ɻz ՝ͷڞ༗ ղܾࡦͷྻڍͱੳ
ʰΛࢭΊͳ͍ػցֶशͷΓํʱ ػցֶशʹऔΓΉݱʹ͓͚Δ՝Λࢦఠ͠ɺੳ͠·͢ɻ
ΞδΣϯμ ͡ΊʹਓೳϒʔϜͱݬ໓ظ ΛࢭΊͳ͍ػցֶशͷΓํʙ৫ɾνʔϜฤʙ αʔϏε։ൃͱػցֶश ػցֶशϓϩδΣΫτ
ΛࢭΊͳ͍ػցֶशͷΓํʙݸਓฤʙ ૯߹֨ಆٕͱͯ͠ͷػցֶश
ਓೳϒʔϜͱݬ໓ظ
ਓೳϒʔϜ ʰୈ࣍ਓೳϒʔϜʱ ը૾ྨʹ͓͚ΔਂֶशͷՌΛ͖͔͚ͬͱ͢Δ ਓೳ࣮ݱͷखஈͷ̍ͭͱͯ͠ػցֶशͷࢿʗظߴ·Δ ϋʔυΣΞʗιϑτΣΞʹ͔͔ΘΒͣ͋ΒΏΔྖҬʹԠ༻͞ΕΑ ͏ͱ͍ͯ͠Δ
ΫοΫύουͷ༷ͳαʔϏε։ൃͷݱྫ֎Ͱͳ͍
IUUQXXXHBSUOFSDPKQQSFTTIUNMQSIUNM
ϒʔϜ͔Βݬ໓ظ "*ʹظ͕աʹൃͨ͠ͷͪʰౙʱ͕๚ΕΔͱ͍͏ྺ࢙͕͋Δ ݬ໓ظΛΓӽ͍ͯͨ͘Ίʹݱͷզʑ͕͖͢͜ͱ ʰΛࢭΊͳ͍ػցֶशͷΓํʱ ࠓճɺαʔϏε։ൃݱͱͯ͠ͷΫοΫύουͱ͍͏ڥͰಘΒΕ ͨݟΛத৺ʹɺ͜ͷ՝ʹ͍ͭͯߟ͍͖͑ͯ·͢
ΛࢭΊͳ͍ػցֶशͷΓํ ৫ɾνʔϜฤ
ΞδΣϯμ ͡ΊʹਓೳϒʔϜͱݬ໓ظ ΛࢭΊͳ͍ػցֶशͷΓํʙ৫ɾνʔϜฤʙ αʔϏε։ൃͱػցֶश ػցֶशϓϩδΣΫτ
ΛࢭΊͳ͍ػցֶशͷΓํʙݸਓฤʙ ૯߹֨ಆٕͱͯ͠ͷػցֶश ͡ΊʹਓೳϒʔϜͱݬ໓ظ ΛࢭΊͳ͍ػցֶशͷΓํʙ৫ɾνʔϜฤʙ αʔϏε։ൃͱػցֶश ػցֶशϓϩδΣΫτ ΛࢭΊͳ͍ػցֶशͷΓํʙݸਓฤʙ ૯߹֨ಆٕͱͯ͠ͷػցֶश
αʔϏε։ൃͱػցֶश αʔϏε։ൃͰػցֶशΛ͍͍ͨͱ͍͏χʔζ͕૿͍͑ͯΔ ਓೳϒʔϜʹґΔͱ͜Ζ͕େ͖͍ ଟ ʰػցֶशΛ͏ʱͱʁ
՝ൃݟ ΰʔϧઃఆ σʔλऩू σʔλՃ ੳ &%" ֶश ධՁݕূ ຊ൪ ධՁݕূ
͏ ࡞Δ ՝ൃݟ ΰʔϧઃఆ σʔλऩू σʔλՃ ੳ &%" ֶश ධՁݕূ
ຊ൪ ධՁݕূ σ ϓ ϩ Π
ྫϞσϧͷ"1*Խʹඞཁͳͷ ֶशࡁΈϞσϧ ͷEVNQ NPEFM\QC I ^ "1* BQQQZ
)551 T &OEQPJOU QSFEJDU JOGPͱ͔ ϞσϧͷಡΈࠐΈͱ*0ϋϯυϥ ࡞ۀ طଘγεςϜʗΠϯϑϥͷΠϯςάϨʔγϣϯ -#ɺϞχλϦϯάɺ%/4ʑ ࣮ߦڥ %PDLFSpMF
ྫϞσϧͷ"1*Խʹඞཁͳͷ ֶशࡁΈϞσϧ ͷEVNQ NPEFM\QC I ^ "1* BQQQZ
)551 T &OEQPJOU QSFEJDU JOGPͱ͔ ϞσϧͷಡΈࠐΈͱ*0ϋϯυϥ ࡞ۀ طଘγεςϜʗΠϯϑϥͷΠϯςάϨʔγϣϯ -#ɺϞχλϦϯάɺ%/4ʑ ࣮ߦڥ %PDLFSpMF ػցֶशϞσϧͷσϓϩΠʹ खؒͱ͕͔͔࣌ؒΔ
def is_food_image(self, image_io): image = Image.open(image_io) result = self.classifier.infer(image) return
result == Category.food ར༻ྫྉཧ͖Ζ͘ͷ߹ ύοέʔδԽ͞ΕͨػցֶशϞσϧͷݺͼग़͠ ͋Δ͍҉ͳ"1*ίʔϧ
def is_food_image(self, image_io): image = Image.open(image_io) result = self.classifier.infer(image) return
result == Category.food ྫྉཧ͖Ζ͘ͷ߹ ύοέʔδԽ͞ΕͨػցֶशϞσϧͷݺͼग़͠ ͋Δ͍҉ͳ"1*ίʔϧ έʔεʹΑΔ͕ αʔϏε։ൃͱ͍͏จ຺ʹ͓͍ͯ ػցֶशϞσϧίʔυͰ࣮͞ΕͨϩδοΫ ͱಉʹѻΘΕ͏Δ
͏ ࡞Δ ϨγϐྨثͷΧςΰϦʹ ʰຑം౾ʱΛͯ͠Έ͍ͨ िؒ͘Β͍͔͔Δͳ Ϛδ͔ʂ if category == 'ΧϨʔ':
foo() + elif category == 'ຑം౾': + bar() ՝ൃݟ ΰʔϧઃఆ σʔλऩू σʔλՃ ੳ &%" ֶश ධՁݕূ ຊ൪ ධՁݕূ
αʔϏε։ൃͱϞσϧͷ։ൃʗσϓϩΠ ͷεϐʔυײʹΪϟοϓ͕͋Δ
Ͳ͏͢Εʁ ਓࡐஔͷ࠷దԽ
͏ ࡞Δ ϨγϐྨثͷΧςΰϦʹ ʰຑം౾ʱΛͯ͠Έ͍ͨ िؒ͘Β͍͔͔Δͳ Ϛδ͔ʂ if category == 'ΧϨʔ':
foo() + elif category == 'ຑം౾': + bar() ՝ൃݟ ΰʔϧઃఆ σʔλऩू σʔλՃ ੳ &%" ֶश ධՁݕূ ຊ൪ ධՁݕূ
͏ ࡞Δ ϨγϐྨثͷΧςΰϦʹ ʰຑം౾ʱΛͯ͠Έ͍ͨ िؒ͘Β͍͔͔Δͳ Ϛδ͔ʂ if category == 'ΧϨʔ':
foo() + elif category == 'ຑം౾': + bar() ՝ൃݟ ΰʔϧઃఆ σʔλऩू σʔλՃ ੳ &%" ֶश ධՁݕূ ຊ൪ ධՁݕূ
ਓࡐஔͷ࠷దԽ αʔϏε։ൃʹػցֶशΛԠ༻͢Δ͜ͱʹෆ׳Εͳݱͷ߹ αʔϏε։ൃʹٻΊΒΕΔ։ൃεϐʔυͱ ػցֶशϞσϧͷ։ൃͱσϓϩΠʹ͔͔Δ࣌ؒ ྆ऀʹΪϟοϓ͕͋Δɺͱ͍͏Λڞ༗ͯ͠าΈدΔ
ྫ͑ਓతϦιʔεͷஔΛݟͯ͠ΈΔ ྫɿαʔϏε։ൃଆͷΤϯδχΞ͕ϞσϧͷσϓϩΠΛख͏
Ͳ͏͢Εʁ ਓࡐஔͷ࠷దԽ औΓΉͷݟ͠ Ϟσϧ։ൃʗσϓϩΠͷޮԽ
Ͳ͏͢Εʁ ਓࡐஔͷ࠷దԽ औΓΉͷݟ͠ Ϟσϧ։ൃʗσϓϩΠͷޮԽ ։ൃεϐʔυͷҧ͍ػցֶशಛ༗ͷίετΛՃຯ্ͨ͠Ͱɺ ػցֶशΛར༻ͯ͠औΓΉ͖ͳͷ͔ߟ͑ͳ͓͢ IUUQTSFTFBSDIHPPHMFDPNQVCTQVCIUNM
ʰػցֶशٕज़తෛ࠴ͷߴརି͠ ҙ༁ ʱ
Ͳ͏͢Εʁ ਓࡐஔͷ࠷దԽ औΓΉͷݟ͠ Ϟσϧ։ൃʗσϓϩΠͷޮԽ
Ϟσϧ։ൃʗσϓϩΠͷޮԽ ։ൃσʔλͷੳɺ࣮ݧɺϞσϧͷֶश σϓϩΠύοέʔδԽɺ"1*Խ
Ϟσϧ։ൃʗσϓϩΠͷޮԽ ։ൃσʔλͷੳɺ࣮ݧɺϞσϧͷֶश σϓϩΠύοέʔδԽɺ"1*Խ
ػցֶशج൫ ػցֶशʹؔΘΔ࣮ݧ։ൃͷεϐʔυΛ্͛Δҝͷج൫ ΫοΫύουݚڀ։ൃ෦෦ࣗͰੵۃతʹվળ͍ͯ͠Δ IUUQTTQFBLFSEFDLDPNBZFNPTNBDIJOF MFBSOJOHQMBUGPSNBUDPPLQBE IUUQTTQFBLFSEFDLDPNBZFNPTBDDFMFSBUF NBDIJOFMFBSOJOHXJUIBXT
ΦϯσϚϯυ(16࣮ݧڥ
ΦϯσϚϯυ(16࣮ݧڥ ւ֎ؚΊ໊ͷϝϯόʔ͕͍Δ ෳͷϓϩδΣΫτʹෳͷਓ͕औΓΉ ࣮ݧ༻ͷܭࢉػڥΛͲͷΑ͏ʹ༻ҙ͢Δ͔ $IBUCPUܦ༝ͰىಈఀࢭՄೳͳΦϯσϚϯυ(16࣮ݧڥ
ʰ࣮ݧ༻Πϯελϯε࡞ͬͯʱ ઃఆࡁΈΠϯελϯεͷ εφοϓγϣοτ ".* ATTIBZFNPTXPSLCFODIEOTDPNQBOZA $SFBUF
ʰ࣮ݧ༻Πϯελϯε࡞ͬͯʱ ઃఆࡁΈΠϯελϯεͷ εφοϓγϣοτ ".* ATTIBZFNPTXPSLCFODIEOTDPNQBOZA $SFBUF IUUQTTQFBLFSEFDLDPNBZFNPTNBDIJOF
MFBSOJOHQMBUGPSNBUDPPLQBE ৄ͘͠
ϓϩδΣΫτςϯϓϨʔτ ʹΑΔ࣮ݧ࠶ݱੑͷ্
ྫͷϓϩδΣΫτͷ ࣮ݧ͕͍ͨ͠ OPUFCPPLͱεΫϦϓτϨϙδτϦʹɺ σʔλࠓखݩʹ͔͠ͳ͍ʜ ͍
࣮ݧ࠶ݱੑͷԼ ͍͍ͭͭ࠶ݱੑͷͳ͍ঢ়ଶΛ࡞ͬͯ͠·͏ ϫʔΫϑϩʔཧͱ͔େֻ͔Γͳͷͪΐͬͱʜ
$PPLJFDVUUFSʹΑΔςϯϓϨʔτԽ ϓϩδΣΫτͷςϯϓϨʔτԽ SVCZʹCVOEMFS͕͋Δ͚Ͳʜ IUUQTHJUIVCDPNBVESFZSDPPLJFDVUUFS 1ZUIPO +JOKB
ͷ൚༻ͳϓϩδΣΫτςϯϓϨʔτੜπʔϧ ಠࣗͷςϯϓϨʔτΛΉ͜ͱ͕ग़དྷɺσʔλαΠΤϯε༻ͷ͋Δ IUUQTHJUIVCDPNESJWFOEBUBDPPLJFDVUUFSEBUBTDJFODF
ESJWFOEBUBDPPLJFDVUUFSEBUBTDJFODF A.BLFpMFA֤छศརUBSHFUͱ͔ઃఆͱ͔ ASFRVJSFNFOUTUYUAґଘϥΠϒϥϦͷϦετ ֤छطσΟϨΫτϦ ATSDEBUBA ATSDNPEFMAͦΕͧΕσʔλͷੜʗલॲཧ༻ɺ
Ϟσϧͷֶश༻εΫϦϓτΛஔ͘ AEBUBAHJUJHOPSF͞ΕΔɺ.BLFpMFͷUBSHFUͰ4ͱTZOD
ྫͷϓϩδΣΫτͷ ࣮ݧ͕͍ͨ͠ HJUDPQBOZDPNSFTFBSDISFDJQFBOBMZTJT ͜͜ʙ HJUDMPOF NBLFTZOD@EBUB@GSPN@T
EPDLFSTDJFODFDPPLJFDVUUFSEPDLFSTDJFODF IUUQTHJUIVCDPNEPDLFSTDJFODFDPPLJFDVUUFSEPDLFS TDJFODF EPDLFSΛར༻͠ɺϓϩδΣΫτͷ࣮ݱੑΛ͞ΒʹߴΊΔ OPUFCPPLͷ্ཱͪ͛ 1PSUGPSXBSEߦ͏UBSHFU
Ϟσϧ։ൃʗσϓϩΠͷޮԽ ։ൃσʔλͷੳɺ࣮ݧɺϞσϧͷֶश ΫϥυڥΛ׆͔ͨ͠ΦϯσϚϯυ(16࣮ݧڥͷ࣮ݱ ϓϩδΣΫτͷςϯϓϨʔτԽʹΑΔ࣮ݧ࠶ݱੑͷ্ σϓϩΠύοέʔδԽɺ"1*Խ
Ϟσϧ։ൃʗσϓϩΠͷޮԽ ։ൃσʔλͷੳɺ࣮ݧɺϞσϧͷֶश ΫϥυڥΛ׆͔ͨ͠ΦϯσϚϯυ(16࣮ݧڥͷ࣮ݱ ϓϩδΣΫτͷςϯϓϨʔτԽʹΑΔ࣮ݧ࠶ݱੑͷ্ σϓϩΠύοέʔδԽɺ"1*Խ
ػցֶशϞσϧͷσϓϩΠ ͱ αʔόʔͷຒΊࠐΈʗύοέʔδʗ"1*
ػցֶशϞσϧͷσϓϩΠ ͱ αʔόʔͷຒΊࠐΈʗύοέʔδʗ"1* ϞσϧΛಡΈࠐΜͰɺ֎෦ϦΫΤετʹԠͯ͡ϞσϧΛ ݺͼग़͢ബ͘খ͞ͳ"1*Λ࣮͢Δ
"1* .JDSPTFSWJDF ͕͍͍ཧ༝ ͦͷଞͷख๏ ྫ͑ΞϓϦαʔόʔͷΈࠐΈ ͱൺͯ ػցֶशϞσϧͷσϓϩΠख๏ͱͯ͠ద͍ͯ͠Δཧ༝
"1* .JDSPTFSWJDF ͕͍͍ཧ༝ ػցֶशͱΞϓϦͰҟͳΔ࣮ߦڥ͕ར༻Ͱ͖Δ ҟͳΔݴޠɺ$16(16 "1*ͷεέʔϧʹΑͬͯߴεϧʔϓοτͷ࣮ݱՄೳ
ػցֶशϞσϧʹΑΔਪॏ͍ ྨثɺͷϓϦϛςΟϒͳػೳͷ࠶ར༻ ։ൃʹ͓͚Δ୲ൣғͷڥքઢͱͯ͠࠷ద ͩͱࢥ͏
࠶ܝϞσϧͷ"1*Խʹඞཁͳͷ ֶशࡁΈϞσϧ ͷEVNQ NPEFM\QC I ^ "1* BQQQZ
)551 T &OEQPJOU QSFEJDU JOGPͱ͔ ϞσϧͷಡΈࠐΈͱ*0ϋϯυϥ ࡞ۀ طଘγεςϜʗΠϯϑϥͷΠϯςάϨʔγϣϯ -#ɺϞχλϦϯάɺ%/4ʑ ࣮ߦڥ %PDLFSpMF ػցֶशͱͷυϝΠϯڞ༗ େ খ
࠶ܝϞσϧͷ"1*Խʹඞཁͳͷ ֶशࡁΈϞσϧ ͷEVNQ NPEFM\QC I ^ "1* BQQQZ
)551 T &OEQPJOU QSFEJDU JOGPͱ͔ ϞσϧͷಡΈࠐΈͱ*0ϋϯυϥ ࡞ۀ طଘγεςϜʗΠϯϑϥͷΠϯςάϨʔγϣϯ -#ɺϞχλϦϯάɺ%/4ʑ ࣮ߦڥ %PDLFSpMF ػցֶशͱͷυϝΠϯڞ༗ େ খ ͜ͷล͕ಛʹਏ͍
ϚωʔδυͳσϓϩΠج൫ ͋Δ͍ࣗ࡞ͷͦΕ Ϟσϧͷ"1*Խɺ࣮ߦڥͷඋͳͲڞ௨Խ͢Δ༨͕͋Δ ڞ௨Խ͞Εͨج൫Λఏڙ͢ΔࣄͰɺσϓϩΠखॱΛॖग़དྷΔ ৄ͘͠ IUUQTTQFBLFSEFDLDPNBZFNPTNBDIJOF MFBSOJOHQMBUGPSNBUDPPLQBE
ΛࢭΊͳ͍ػցֶशͷΓํ ݸਓฤ
૯߹֨ಆٕͱͯ͠ͷػցֶश ʰػցֶश͕ग़དྷΔΑ͏ʹͳΔʱͱ
None
1ZUIPO %PDLFS TDJLJUMFBSO 5FOTPS'MPX .F$BC 42- ,FSBT ϕΠζ౷ܭ χϡʔϥϧωοτ "84
($1 3 47. LBHHMF
૯߹֨ಆٕͱͯ͠ͷػցֶश ʰػցֶश͕ग़དྷΔΑ͏ʹͳΔʱͱ ϒʔϜख͍ɺใ͕൙ཞ͍ͯ͠Δ Α͏ʹݟ͑Δ ʰ͋Ε͜Εֶͼͨա͗Δʱ
ݸਓʹͱͬͯɺඪʗతͷ۩ମԽ͕ॏཁʹͳ͖͍ͬͯͯΔ ྫ͑ ʰαʔϏε։ൃʹػցֶशΛར༻͢Δதن dਓ ͷݱͰɺཱࣗͯ͠ՁΛੜΈग़ͤΔΤϯδχΞʹͳΔʱͱ͔
૯߹֨ಆٕͱͯ͠ͷػցֶश Ұํ͔֬ʹݱͰΔ͖͜ͱଟ͍ ͔͠ݱʹΑͬͯҟͳΔͷͰɺ ݄ฒΈ͕ͩ ຊͰֶͳ͍͜ͱ͕ଟ͍ Γ࣮Ͱػցֶशʹ৮ΕΔͷ͕ۙಓ
σʔλੳίϯϖྑ͍ ʰݶΒΕͨظؒͷதͰσʔλΛੳ͠ɺείΞΛܧଓతʹվળ͢Δʱ ·͋·࣮͋ͬΆ͍
·ͱΊ ʰΛࢭΊͳ͍ػցֶशͷΓํʱ αʔϏεͷ࣮ݱखஈͱͯ͠ͷػցֶशʹର͢Δظӈݞ্͕Γ ݱͱͯ͠ظʹԠ͑ଓ͚͍͖͍ͯͨͰ͢Ͷɻ
8FSFIJSJOH IUUQTDPPLQBEKPCT