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
編入試験への準備と編入後の生活 (Ver.2018)
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
S.Shota
March 17, 2018
Education
830
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
編入試験への準備と編入後の生活 (Ver.2018)
第6回関東合同編入説明会 (
https://www.zenpen-kosen.com/kantou_6/
) のフリートークで使用したスライドです
S.Shota
March 17, 2018
More Decks by S.Shota
See All by S.Shota
[ICLR/ICML2019読み会] Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
satuma777
2
3.3k
論文紹介:Neural Architecture Search with Bayesian Optimisation and Optimal Transport [Kandasamy et al., NIPS 2018]
satuma777
0
1.1k
Path-Level Network Transformation for Efficient Architecture Search (ICML2018読み会)
satuma777
5
1.3k
論文紹介:Efficient Architecture Search by Network Transformation [Cai et al., AAAI-2018]
satuma777
0
1.1k
新ラボ生向けチュートリアル:文献調査(サーベイ)の仕方
satuma777
0
1.3k
The beautiful world of evolutionary computation made by probability and statistics
satuma777
0
1.2k
論文紹介:Gradient Boosted Feature Selection
satuma777
0
1.1k
論文紹介:Neural Architecture Search with Reinforcement Learning
satuma777
0
1.7k
論文紹介:Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves
satuma777
0
1.2k
Other Decks in Education
See All in Education
Estimating Group × Time Interaction in Scale-Transformed CEFR-J Self-Assessment Scores: A Case in Study-Abroad Research
uranoken
0
130
Case Studies and Future Research - Lecture 12 - Next Generation User Interfaces (4018166FNR)
signer
PRO
0
200
NDIAS Automotive / IoT CTF 2026 Recap - Keyfob & OSINT
himitu23
0
200
Course Review - Lecture 13 - Information Visualisation (4019538FNR)
signer
PRO
1
2.7k
「機械学習と因果推論」入門 ③ 漸近効率な推定量と二重機械学習
masakat0
0
730
Data Physicalisation - Lecture 9 - Next Generation User Interfaces (4018166FNR)
signer
PRO
1
1.1k
0513
cbtlibrary
0
210
2026年度春学期 統計学 第3回 クロス集計と感度・特異度,データの可視化 (2026. 4. 23)
akiraasano
PRO
0
180
勝手にCULTIBASE で広げよう、探究の輪! - CULTIVAL 2026
hiroc_sk
1
230
Visionary Initiative: Materials-Positive Society — Evolving “Things,” empowering a positive society | Science Tokyo
sciencetokyo
PRO
0
120
AI時代に、 なぜ英語を勉強するのか
empelt
0
120
焦燥を平穏に変えるエンジニアのための哲学
ichimichi
7
5.4k
Featured
See All Featured
Redefining SEO in the New Era of Traffic Generation
szymonslowik
1
350
So, you think you're a good person
axbom
PRO
2
2.1k
Taking LLMs out of the black box: A practical guide to human-in-the-loop distillation
inesmontani
PRO
3
2.3k
Building Experiences: Design Systems, User Experience, and Full Site Editing
marktimemedia
0
550
Mobile First: as difficult as doing things right
swwweet
225
10k
Art, The Web, and Tiny UX
lynnandtonic
304
22k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
666
130k
Claude Code のすすめ
schroneko
67
230k
What's in a price? How to price your products and services
michaelherold
247
13k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
49
10k
The Pragmatic Product Professional
lauravandoore
37
7.4k
The Spectacular Lies of Maps
axbom
PRO
1
850
Transcript
ୈճؔ౦߹ಉฤೖઆ໌ձ!ίϩϓϥ ฤೖࢼݧͷ४උͱ ฤೖޙͷੜ׆ ੪౻ ᠳଡ ڥใֶ ใϝσΟΞڥֶઐ߈ ใϝσΟΞֶίʔε നݚڀࣨ म࢜
݄
"CPVU.F • ੪౻ ᠳଡ αΠτ γϣλ • BLBͭ͞·
TBUVNB • ɿ!EBZUC@UXZ • ɿTBUVNB • ϙʔτϑΥϦΦɾϒϩάɿ • IUUQTBUVNBQPSUGPMJPYZ[BCPVU • IUUQTBUVNBQPSUGPMJPIBUFCMPKQ
"CPVU.FɿֶྺαϚϦʔ • ɿἚߴઐ ిࢠใֶՊ ଔۀ • Ṳནঘݚڀࣨ ग़ɼଔݚςʔϚ"3 •
ɿԣࠃཱେֶ ཧֶ෦ ɾిࢠใܥֶՊ ใֶ&1ଔۀ • ݱࡏɿԣࠃཱେֶ ڥใֶ ใ ϝσΟΞڥֶઐ߈ ใϝσΟΞֶίʔε • നਅҰݚڀࣨ ॴଐɼݚڀςʔϚػցֶश
େֶબͼͷج४ • ୈҰʹ༏ઌ͖͢ʮݚڀࣨʯ • ͕ࣗΓ͍ͨݚڀԿʁ • ͦͷςʔϚʹ߹க͢Δݚڀࣨଘࡏ͢Δʁ • ࣌ؒతɾڑతɾۚમతʹ༨༟͕͋Ε
ݚڀࣨݟֶͷ͓ئ͍Λ͠Α͏ • େͷ1* 1SJODJQBM*OWFTUJHBUPSݚڀࣨओ࠻ շ͘ड͚ೖΕͯ͘ΕΔͣ • ΑΓςʔϚʹ߹கͨ͠ଞͷઌੜΛ հͯ͘͠ΕΔέʔε
ฤೖࢼݧͷରࡦ • ใֶ&1ͷ߹ɼࢼݧՊͭ • ֶʢඍੵɾઢܗʣɼཧʢྗֶʣɼ ઐՊʢཧճ࿏ $ݴޠ +BWBʣ •
ӳޠ50&*$ͷείΞΛఏग़ • աڈˠॻ੶ˠաڈͷॱʹऔΓΉ • աڈʮͷΈʯͨΓతͰඇৗʹةݥ • ͓͢͢Ίͷॻ੶ͪ͜Βʹ ·ͱΊ·ͨ͠ˠ https://www.slideshare.net/ShotaSatuma/ss-66615294
୯Ґৼସʹ͍ͭͯ • ిࢠใܥˠใܥͷΑ͏ʹҟͳΔઐ߈ʹ ҠΔͱৼସՄೳͳ୯Ґগͳ͘ͳΔ • ࢲͷ߹ɿߴઐࣗମʹऔಘͨ͠ిؾܥՊͷ ୯Ґ΄ͱΜͲৼସઌͳ͠ • Պམͱͨ͠Βཹͱ͍͏ͱ͜Ζ͔Β
ελʔτͱ͍͏έʔεʜ • ෦ੜΑΓ୯Ґ͕গͳΊͳͷͰɼ ߴઐ࣌ΑΓؤுΔඞཁ͋Γ
େֶͷߨٛ • ҰൠڭཆՊ͕໘ന͍ • ϕϯνϟʔ͔ΒֶͿϚωδϝϯτͳͲ • ઐجૅՊɿֶɾཧɾԽֶͷجૅ • ઢܗɼྗֶɼࡐྉ༗ػԽֶʜ
• ใֶ&1ͷઐՊ෯͍ • σʔλϕʔεɼใηΩϡϦςΟɼػցֶशɼ ܭࢉཧɼใࣾձྙཧɼཧݴޠֶʜ
ฤೖ͔Βͷڭһ໔ڐऔಘ • ใֶ&1ͰऔಘՄೳͳڭһ໔ڐɿ • தֶߍڭ་Ұछ໔ڐঢ়ʢֶɾཧՊʣ • ߴֶߍڭ་Ұछ໔ڐঢ়ʢֶɾཧՊɾใʣ • தֶߍ
ֶ ͱߴߍ ֶɾใ Λऔಘ • ͜ͷέʔεͰଔۀཁ݅୯Ґʴ୯Ґ • ҆қʹऔΖ͏ͱ͢Δͷ ͓͢͢Ί͠·ͤΜʜ
ฤೖ͔Βͷڭһ໔ڐऔಘ • ՃͰऔΔඞཁ͕͋ͬͨ୯Ґɿ • ڭ৬ؔ࿈ • ڭҭ৺ཧֶɼڭՊڭҭ๏ɼಓಙڭҭͳͲ • ڭՊؔ࿈
• ֶɼزԿֶɼใॲཧͳͲ • िؒͷհޢࢱࢪઃͰͷ࣮श • िؒͷڭҭ࣮श • ߴߍ໔ڐͳΒिؒɼதֶ໔ڐͳΒिؒ
εέδϡʔϧʢલظʣ ̍ݶ ̎ݶ ̏ݶ ̐ݶ ̑ݶ ̒ݶ ̓ݶ ʙ
ʙ ʙ ʙ ʙ ʙ ʙ ݄ ཧ ݴޠֶ" ใཧ ຊࠃ ݑ๏ ౷ܭֶ *$ Ր ιϑτΣΞ ֶ ϓϩδΣΫτ ϥʔχϯά ΞϧόΠτ ਫ ࡐྉ ༗ػԽֶ ཧɾԽֶ࣮ݧ ίϯύΠϥ ใ ηΩϡϦςΟ زԿֶ* ڭҭ૬ஊͷ جૅͱํ๏ ۚ ϚϧνϝσΟΞ ใॲཧ ใֶ ֓ தࠃޠ B ूதߨٛʢલظʣ ใՊڭҭ๏** தֶՊڭҭ๏** ڭ৬ ɿ௨ৗͷଔۀཁ݅ʹؔΘΔߨٛ ɿڭһ໔ڐऔಘʹؔΘΔߨٛ
εέδϡʔϧʢޙظʣ ̍ݶ ̎ݶ ̏ݶ ̐ݶ ̑ݶ ̒ݶ ̓ݶ ʙ
ʙ ʙ ʙ ʙ ʙ ʙ ݄ ڭҭͷ ৺ཧֶ σΟδλϧɾ ίϛϡχέʔγϣϯ ݱ࣏ ʢຊʣ ίϯϐϡʔλ γεςϜͱ ίϛϡχέʔ γϣϯ ౷ܭֶ **$ ΧϦΩϡϥϜ Ր ใࣾձ ྙཧ ը૾ɾԻ ใॲཧ தֶՊ ڭҭ๏* ϕϯνϟʔ͔Β ֶͿϚωδϝϯτ ڭҭ جૅ ਫ ࡐྉ ༗ػԽֶ ྗֶ ใֶ ಛผԋश ΞϧόΠτ ઢܗ ֶ** σʔλϕʔε ଟ༷ମ ಓಙڭҭͷ ཧͱํ๏ ۚ ࡐྉ ແػԽֶ ݱͷ ܦࡁ# ֬ Ϟσϧ தࠃޠ B ΞϧόΠτ ूதߨٛʢޙظʣ ڭҭํ๏ ɿ௨ৗͷଔۀཁ݅ʹؔΘΔߨٛ ɿڭһ໔ڐऔಘʹؔΘΔߨٛ
εέδϡʔϧʢલظʣ ̍ݶ ̎ݶ ̏ݶ ̐ݶ ̑ݶ ̒ݶ ̓ݶ ʙ
ʙ ʙ ʙ ʙ ʙ ʙ ݄ ڭҭܦӦ ΞϧόΠτ Ր ྠߨ ۀܦӦ ਫ ܭࢉཧ** ྠߨ తࡒ࢈ ΞϧόΠτ ۚ ྠߨ ֶ* ઌిࢠ ใֶ ࣭ཧ ूதߨٛʢલظʣ ڭҭࣾձֶ ੜెɾਐ࿏ࢦಋ ڭҭ࣮शࣄલࢦಋ ɿ௨ৗͷଔۀཁ݅ʹؔΘΔߨٛ ɿڭһ໔ڐऔಘʹؔΘΔߨٛ
εέδϡʔϧʢޙظʣ ̍ݶ ̎ݶ ̏ݶ ̐ݶ ̑ݶ ̒ݶ ̓ݶ ʙ
ʙ ʙ ʙ ʙ ʙ ʙ ݄ ྠߨ Ր ྠߨ ਫ ΞϧόΠτ ΞϧόΠτ ۚ ྠߨ ֬ɾ ౷ܭ ಛผ ׆ಈ ڭ৬࣮ઓ ԋश ूதߨٛʢޙظʣ ڭҭ࣮शࣄޙࢦಋ ڭҭ࣮श"ɾ# հޢࢱ࣮श ɿ௨ৗͷଔۀཁ݅ʹؔΘΔߨٛ ɿڭһ໔ڐऔಘʹؔΘΔߨٛ
ʑͷੜ׆ • ΞϧόΠτˍΠϯλʔϯγοϓ • क़ߨࢣͷΞϧόΠτʢिʙʣ • 8FCΤϯδχΞظΠϯλʔϯʢिʣ • ༡Ϳ༨༟࡞ΕΔ
• Πϕϯτ͍͍ͩͨͳͷͰʜ • ूதߨٛΛ͏·͘ආ͚ͯ ՆϑΣεߦͬͨΓʜ
େֶӃʹ͍ͭͯ • ฤೖੜͰਪનऔΕΔ • ڥใֶͷ߹ʮ্ҐʯPS ʮҎ্Ͱऔಘͨ͠୯Ґ͕Ҏ্ʯ • ߴઐ࣌ͷؔ͠ͳ͍ •
ߴઐ࣌ͷ୯Ґऔಘ࣌ͷʹؔͳ͘ ʮৼସ୯ҐʯͱΈͳ͞ΕΔͨΊ • Ӄਐ͔ब৬͔͙͢ʹબ͕ഭΒΕΔͷͰ େֶೖֶޙ͔Βߟ͑࢝ΊΔͷ͕͓͢͢Ί
࠷ޙʹɿฤೖͷ1304ʗ$0/4 • ฤೖͷ͍͍ͱ͜Ζʢ1304ʣ • ෯͍ڭཆɾઐՊΛਂΊΔ͜ͱ͕Մೳ • ىۀΛ͡Ίͱͯ͠νϟϯεࢸΔॴʹ • ฤೖͷѱ͍ͱ͜Ζʢ$0/4ʣ
• ߴઐ࣌ʹशͬͨ͜ͱΛ࠶श͏͜ͱʜ • ཹͷϦεΫߴΊ ʮେֶʯΛ͍ͤΔ͔ ࣗͷ৺͕͚࣍ୈ