6월, 2021의 게시물 표시

3차원 건물 모형 디지털 트윈으로 우리 국토를 보다 안전하고 편리하게 관리하겠습니다 https://t.co/fAClfOsnxP

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Favorite tweet: 3차원 건물 모형 디지털 트윈으로 우리 국토를 보다 안전하고 편리하게 관리하겠습니다 https://t.co/fAClfOsnxP — 국토교통부 (@Korea_land) Jun 28, 2021 3차원 건물 모형 디지털 트윈으로 우리 국토를 보다 안전하고 편리하게 관리하겠습니다 https://t.co/fAClfOsnxP http://twitter.com/Korea_land/status/1409643740348616706 https://t.co/fAClfOsnxP June 29, 2021 at 07:43AM

한국형 스마트시티가 세계 11개국으로 진출합니다 https://t.co/EkAlInFlG1

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Favorite tweet: 한국형 스마트시티가 세계 11개국으로 진출합니다 https://t.co/EkAlInFlG1 — 국토교통부 (@Korea_land) Jun 23, 2021 한국형 스마트시티가 세계 11개국으로 진출합니다 https://t.co/EkAlInFlG1 http://twitter.com/Korea_land/status/1407518943938445316 https://t.co/EkAlInFlG1 June 23, 2021 at 11:00AM

Maryland transportation officials will demonstrate a ramp metering system that will detect #realtimetraffic conditions and activate #trafficsignals to more efficiently control how traffic merges. https://t.co/37qyzCSuua

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Favorite tweet: Maryland transportation officials will demonstrate a ramp metering system that will detect #realtimetraffic conditions and activate #trafficsignals to more efficiently control how traffic merges. https://t.co/37qyzCSuua — INRIX ® (@INRIX) Jun 22, 2021 Maryland transportation officials will demonstrate a ramp metering system that will detect #realtimetraffic conditions and activate #trafficsignals to more efficiently control how traffic merges. https://t.co/37qyzCSuua http://twitter.com/INRIX/status/1407368407113101313 https://t.co/37qyzCSuua June 23, 2021 at 01:02AM

디지털 트윈 국토가 궁금한 사람~??🙋🏻‍♀️🙋🏻‍♂️ 디지털 트윈 국토는 우리 국토를 3D로 표현한 것인데요! 그럼, 디지털 트윈 국토를 하면 뭐가 좋을까요?🤔 #국토교통부 #국토부 #디지털트윈 #디지털트윈국토 #자율주행 #스마트시티 #도시환경분석 #재난재해 https://t.co/M1a4FZP9nu

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Favorite tweet: 디지털 트윈 국토가 궁금한 사람~??🙋🏻‍♀️🙋🏻‍♂️ 디지털 트윈 국토는 우리 국토를 3D로 표현한 것인데요! 그럼, 디지털 트윈 국토를 하면 뭐가 좋을까요?🤔 #국토교통부 #국토부 #디지털트윈 #디지털트윈국토 #자율주행 #스마트시티 #도시환경분석 #재난재해 https://t.co/M1a4FZP9nu — 국토교통부 (@Korea_land) Jun 21, 2021 디지털 트윈 국토가 궁금한 사람~??🙋🏻‍♀️🙋🏻‍♂️ 디지털 트윈 국토는 우리 국토를 3D로 표현한 것인데요! 그럼, 디지털 트윈 국토를 하면 뭐가 좋을까요?🤔 #국토교통부 #국토부 #디지털트윈 #디지털트윈국토 #자율주행 #스마트시티 #도시환경분석 #재난재해 https://t.co/M1a4FZP9nu http://twitter.com/Korea_land/status/1406809099858112512 https://t.co/M1a4FZP9nu June 21, 2021 at 12:00PM

The @SAP #SAPHANA Digital #SupplyChain and Intelligent Enterprise: https://t.co/JR5Qvk811J #SAPPHIRENOW #IntelligentEnterprise #Automation #CX #ERP #SCM #DigitalTransformation #BigData #Analytics #AI #ML #IoT #IIoT #IoTPL #IoTCommunity #Industry40 https://t.co/eTNqmD3ZwZ

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Favorite tweet: The @SAP #SAPHANA Digital #SupplyChain and Intelligent Enterprise: https://t.co/JR5Qvk811J #SAPPHIRENOW #IntelligentEnterprise #Automation #CX #ERP #SCM #DigitalTransformation #BigData #Analytics #AI #ML #IoT #IIoT #IoTPL #IoTCommunity #Industry40 https://t.co/eTNqmD3ZwZ — Kirk Borne (@KirkDBorne) Jun 10, 2021 The @SAP #SAPHANA Digital #SupplyChain and Intelligent Enterprise: https://t.co/JR5Qvk811J #SAPPHIRENOW #IntelligentEnterprise #Automation #CX #ERP #SCM #DigitalTransformation #BigData #Analytics #AI #ML #IoT #IIoT #IoTPL #IoTCommunity #Industry40 https://t.co/eTNqmD3ZwZ http://twitter.com/KirkDBorne/status/1403135317427863554 https://t.co/JR5Qvk811J June 11, 2021 at 08:41AM

Entropy levels in 100 world cities according to street network orientation. Ranked from ordered to disordered, Chicago is #1, Charlotte is #100. #Vancouver Canada is #4 (perhaps one reason its trolley bus network functions so well). https://t.co/a0SlFfL1VB https://t.co/t7Q6jYAmtL

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Favorite tweet: Entropy levels in 100 world cities according to street network orientation. Ranked from ordered to disordered, Chicago is #1, Charlotte is #100. #Vancouver Canada is #4 (perhaps one reason its trolley bus network functions so well). https://t.co/a0SlFfL1VB https://t.co/t7Q6jYAmtL — Taras Grescoe 🐌 (@grescoe) Jun 10, 2021 Entropy levels in 100 world cities according to street network orientation. Ranked from ordered to disordered, Chicago is #1, Charlotte is #100. #Vancouver Canada is #4 (perhaps one reason its trolley bus network functions so well). https://t.co/a0SlFfL1VB https://t.co/t7Q6jYAmtL http://twitter.com/grescoe/status/1403082969758187526 https://t.co/a0SlFfL1VB June 11, 2021 at 05:13AM

5 Minutes Cheat Sheet Explaining all Machine Learning Models by @AnveeNaik https://t.co/wfl4BbL49j

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Favorite tweet: 5 Minutes Cheat Sheet Explaining all Machine Learning Models by @AnveeNaik https://t.co/wfl4BbL49j — Towards Data Science (@TDataScience) Jun 9, 2021 5 Minutes Cheat Sheet Explaining all Machine Learning Models by @AnveeNaik https://t.co/wfl4BbL49j http://twitter.com/TDataScience/status/1402438301877952512 https://t.co/wfl4BbL49j June 09, 2021 at 10:32AM

Logistic Regression, Explained in One Picture: https://t.co/1JQHLRcoWO ———————— #BigData #DataScience #AI #MachineLearning #DeepLearning #NeuralNetworks #Algorithms #Statistics #StatisticalLiteracy #DataLiteracy #Mathematics #Infographics #abdsc https://t.co/8wBiKL0NtA

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Favorite tweet: Logistic Regression, Explained in One Picture: https://t.co/1JQHLRcoWO ———————— #BigData #DataScience #AI #MachineLearning #DeepLearning #NeuralNetworks #Algorithms #Statistics #StatisticalLiteracy #DataLiteracy #Mathematics #Infographics #abdsc https://t.co/8wBiKL0NtA — Kirk Borne (@KirkDBorne) Jun 8, 2021 Logistic Regression, Explained in One Picture: https://t.co/1JQHLRcoWO ———————— #BigData #DataScience #AI #MachineLearning #DeepLearning #NeuralNetworks #Algorithms #Statistics #StatisticalLiteracy #DataLiteracy #Mathematics #Infographics #abdsc https://t.co/8wBiKL0NtA http://twitter.com/KirkDBorne/status/1402411110792323074 https://t.co/1JQHLRcoWO June 09, 2021 at 08:43AM

a decision was made here https://t.co/oyjL9JJfn0

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Favorite tweet: a decision was made here https://t.co/oyjL9JJfn0 — Spencer (@SpencerNeumann) Jun 8, 2021 a decision was made here https://t.co/oyjL9JJfn0 http://twitter.com/SpencerNeumann/status/1402383941848420353 https://t.co/oyjL9JJfn0 June 09, 2021 at 06:56AM

Tiny #MachineLearning brings #AI to #IoT devices: https://t.co/MWtUBMO4GR ————— #IIoT #IoTPL #IoTCommunity #ML #DeepLearning #NeuralNetworks #BigData #DataScience #Industry40 #EdgeComputing #Edge #EdgeAI ——— +see #TinyML book: https://t.co/1mzV2wWAHW https://t.co/uCe4RhytfY

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Favorite tweet: Tiny #MachineLearning brings #AI to #IoT devices: https://t.co/MWtUBMO4GR ————— #IIoT #IoTPL #IoTCommunity #ML #DeepLearning #NeuralNetworks #BigData #DataScience #Industry40 #EdgeComputing #Edge #EdgeAI ——— +see #TinyML book: https://t.co/1mzV2wWAHW https://t.co/uCe4RhytfY — Kirk Borne (@KirkDBorne) Jun 8, 2021 Tiny #MachineLearning brings #AI to #IoT devices: https://t.co/MWtUBMO4GR ————— #IIoT #IoTPL #IoTCommunity #ML #DeepLearning #NeuralNetworks #BigData #DataScience #Industry40 #EdgeComputing #Edge #EdgeAI ——— +see #TinyML book: https://t.co/1mzV2wWAHW https://t.co/uCe4RhytfY http://twitter.com/KirkDBorne/status/1402305294533791745 https://t.co/MWtUBMO4GR June 09, 2021 at 01:43AM

🗞New Paper🗞 🤖🧪Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning 🧪🤖 Huge thanks to @neilbband* as well as @clarelyle, @AidanNGomez, @tom_rainforth, @yaringal, and @OATML_Oxford ! Introducing 🚀Non-Parametric Transformers🚀 1/ https://t.co/onIR9V89bx

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Favorite tweet: 🗞New Paper🗞 🤖🧪Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning 🧪🤖 Huge thanks to @neilbband* as well as @clarelyle, @AidanNGomez, @tom_rainforth, @yaringal, and @OATML_Oxford ! Introducing 🚀Non-Parametric Transformers🚀 1/ https://t.co/onIR9V89bx — Jannik Kossen (@janundnik) Jun 7, 2021 🗞New Paper🗞 🤖🧪Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning 🧪🤖 Huge thanks to @neilbband* as well as @clarelyle, @AidanNGomez, @tom_rainforth, @yaringal, and @OATML_Oxford ! Introducing 🚀Non-Parametric Transformers🚀 1/ https://t.co/onIR9V89bx http://twitter.com/janundnik/status/1401813691251761153 https://t.co/onIR9V89bx June 07, 2021 at 05:10PM

While most #ML robotics research occurs in a fixed lab environment, as the field advances into complex and challenging real-world scenarios, using A/B testing to compare results of different models and lab settings is increasingly important. Learn more → https://t.co/L4LFOz8G6P https://t.co/mUmDlDTykV

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Favorite tweet: While most #ML robotics research occurs in a fixed lab environment, as the field advances into complex and challenging real-world scenarios, using A/B testing to compare results of different models and lab settings is increasingly important. Learn more → https://t.co/L4LFOz8G6P https://t.co/mUmDlDTykV — Google AI (@GoogleAI) Jun 10, 2021 While most #ML robotics research occurs in a fixed lab environment, as the field advances into complex and challenging real-world scenarios, using A/B testing to compare results of different models and lab settings is increasingly important. Learn more → https://t.co/L4LFOz8G6P https://t.co/mUmDlDTykV http://twitter.com/GoogleAI/status/1403082499643875329 https://t.co/L4LFOz8G6P June 11, 2021 at 05:11AM

[FREE 185-page PDF] Comprehensive Guide to #MachineLearning for #DataScientists → https://t.co/rEvJ7hNalv —————— #BigData #DataScience #AI #AppliedMathematics #DataMining #Mathematics #Algorithms #NeuralNetworks #DeepLearning #LinearAlgebra #Statistics #Calculus #abdsc https://t.co/ghi0slKjbj

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Favorite tweet: [FREE 185-page PDF] Comprehensive Guide to #MachineLearning for #DataScientists → https://t.co/rEvJ7hNalv —————— #BigData #DataScience #AI #AppliedMathematics #DataMining #Mathematics #Algorithms #NeuralNetworks #DeepLearning #LinearAlgebra #Statistics #Calculus #abdsc https://t.co/ghi0slKjbj — Kirk Borne (@KirkDBorne) Jun 10, 2021 [FREE 185-page PDF] Comprehensive Guide to #MachineLearning for #DataScientists → https://t.co/rEvJ7hNalv —————— #BigData #DataScience #AI #AppliedMathematics #DataMining #Mathematics #Algorithms #NeuralNetworks #DeepLearning #LinearAlgebra #Statistics #Calculus #abdsc https://t.co/ghi0slKjbj http://twitter.com/KirkDBorne/status/1403019822057799684 https://t.co/rEvJ7hNalv June 11, 2021 at 01:02AM

Pleased to share that GPytorch now has native support for Bayesian Gaussian Process Latent Variable Models using SVI 🙌. Thanks to @gpleiss for following through and @lawrennd for guiding all the way through. Check out the tutorial here: https://t.co/BTq5mp41IE https://t.co/42LHZ69dlk

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Favorite tweet: Pleased to share that GPytorch now has native support for Bayesian Gaussian Process Latent Variable Models using SVI 🙌. Thanks to @gpleiss for following through and @lawrennd for guiding all the way through. Check out the tutorial here: https://t.co/BTq5mp41IE https://t.co/42LHZ69dlk — Vidhi Lalchand (@VRLalchand) Jun 10, 2021 Pleased to share that GPytorch now has native support for Bayesian Gaussian Process Latent Variable Models using SVI 🙌. Thanks to @gpleiss for following through and @lawrennd for guiding all the way through. Check out the tutorial here: https://t.co/BTq5mp41IE https://t.co/42LHZ69dlk http://twitter.com/VRLalchand/status/1403019581103484939 https://t.co/BTq5mp41IE June 11, 2021 at 01:01AM

How to Generate Automated PDF Documents with #Python - KDnuggets https://t.co/I8HHEygac0 https://t.co/9ITaqk5ESJ

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Favorite tweet: How to Generate Automated PDF Documents with #Python - KDnuggets https://t.co/I8HHEygac0 https://t.co/9ITaqk5ESJ — KDnuggets (@kdnuggets) Jun 10, 2021 How to Generate Automated PDF Documents with #Python - KDnuggets https://t.co/I8HHEygac0 https://t.co/9ITaqk5ESJ http://twitter.com/kdnuggets/status/1402992461329272849 https://t.co/I8HHEygac0 June 10, 2021 at 11:14PM

서울시 출퇴근길, 어디가 제일 막힐까? 자가용 출퇴근러라면 꼭 확인해야 하는 서울시내 막히는 길 TOP 3! 자세히 보기 ▶ https://t.co/vRhDuxKgCL https://t.co/CxJRYg3A57

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Favorite tweet: 서울시 출퇴근길, 어디가 제일 막힐까? 자가용 출퇴근러라면 꼭 확인해야 하는 서울시내 막히는 길 TOP 3! 자세히 보기 ▶ https://t.co/vRhDuxKgCL https://t.co/CxJRYg3A57 — 국토교통부 (@Korea_land) Jun 10, 2021 서울시 출퇴근길, 어디가 제일 막힐까? 자가용 출퇴근러라면 꼭 확인해야 하는 서울시내 막히는 길 TOP 3! 자세히 보기 ▶ https://t.co/vRhDuxKgCL https://t.co/CxJRYg3A57 http://twitter.com/Korea_land/status/1402822831675645955 https://t.co/vRhDuxKgCL June 10, 2021 at 12:00PM

FREE comprehensive 570-page eBook on Applications of Deep #NeuralNetworks: https://t.co/pH0PDyIvFz —————— #abdsc #BigData #DataScience #AI #MachineLearning #DeepLearning #Mathematics #Python #DataScientists #NLProc #NLU #NLG #ComputerVision #GenerativeAdversarialNetworks #GANs https://t.co/TgqUsFi5ki

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Favorite tweet: FREE comprehensive 570-page eBook on Applications of Deep #NeuralNetworks: https://t.co/pH0PDyIvFz —————— #abdsc #BigData #DataScience #AI #MachineLearning #DeepLearning #Mathematics #Python #DataScientists #NLProc #NLU #NLG #ComputerVision #GenerativeAdversarialNetworks #GANs https://t.co/TgqUsFi5ki — Kirk Borne (@KirkDBorne) Jun 8, 2021 FREE comprehensive 570-page eBook on Applications of Deep #NeuralNetworks: https://t.co/pH0PDyIvFz —————— #abdsc #BigData #DataScience #AI #MachineLearning #DeepLearning #Mathematics #Python #DataScientists #NLProc #NLU #NLG #ComputerVision #GenerativeAdversarialNetworks #GANs https://t.co/TgqUsFi5ki http://twitter.com/KirkDBorne/status/1402412568686247942 https://t.co/pH0PDyIvFz June 09, 2021 at 08:49AM

New @madewithml MLOps lesson on monitoring machine learning systems: - identifying drift (data, target, concept) - measuring drift on uni/multivariate data via - reducers (PCA, UAE) - detectors (chi^2, KS, MMD) - solutions (not always retraining) https://t.co/oircOJVxgo https://t.co/OaOmy8IyxQ

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Favorite tweet: New @madewithml MLOps lesson on monitoring machine learning systems: - identifying drift (data, target, concept) - measuring drift on uni/multivariate data via - reducers (PCA, UAE) - detectors (chi^2, KS, MMD) - solutions (not always retraining) https://t.co/oircOJVxgo https://t.co/OaOmy8IyxQ — Goku Mohandas (@GokuMohandas) Jun 7, 2021 New @madewithml MLOps lesson on monitoring machine learning systems: - identifying drift (data, target, concept) - measuring drift on uni/multivariate data via - reducers (PCA, UAE) - detectors (chi^2, KS, MMD) - solutions (not always retraining) https://t.co/oircOJVxgo https://t.co/OaOmy8IyxQ http://twitter.com/GokuMohandas/status/1401877477098541056 https://t.co/oircOJVxgo June 07, 2021 at 09:23PM

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Introduces a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. https://t.co/ARIsdy3nUg https://t.co/UfSeJRSDwX

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Favorite tweet: Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Introduces a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. https://t.co/ARIsdy3nUg https://t.co/UfSeJRSDwX — Aran Komatsuzaki (@arankomatsuzaki) Jun 7, 2021 Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Introduces a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. https://t.co/ARIsdy3nUg https://t.co/UfSeJRSDwX http://twitter.com/arankomatsuzaki/status/1401701981790474240 https://t.co/ARIsdy3nUg June 07, 2021 at 09:46AM

Graph Neural networks tutorial in Google Colab https://t.co/RwN1le0C7A #AI #DeepLearning #MachineLearning #DataScience https://t.co/MeTIFWQ656

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Favorite tweet: Graph Neural networks tutorial in Google Colab https://t.co/RwN1le0C7A #AI #DeepLearning #MachineLearning #DataScience https://t.co/MeTIFWQ656 — Mike Tamir, PhD (@MikeTamir) Jun 8, 2021 Graph Neural networks tutorial in Google Colab https://t.co/RwN1le0C7A #AI #DeepLearning #MachineLearning #DataScience https://t.co/MeTIFWQ656 http://twitter.com/MikeTamir/status/1402068118730645505 https://t.co/RwN1le0C7A June 08, 2021 at 10:01AM

Poorly timed #signals mean U.S. drivers experience a collective 17.25 million hours of delays per day at #intersections. Peachtree Corners, GA announced it will deploy the traffic management C-V2X technology which will improve travel times and efficiency. https://t.co/jQn25V0iaN

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Favorite tweet: Poorly timed #signals mean U.S. drivers experience a collective 17.25 million hours of delays per day at #intersections. Peachtree Corners, GA announced it will deploy the traffic management C-V2X technology which will improve travel times and efficiency. https://t.co/jQn25V0iaN — INRIX ® (@INRIX) Jun 7, 2021 Poorly timed #signals mean U.S. drivers experience a collective 17.25 million hours of delays per day at #intersections. Peachtree Corners, GA announced it will deploy the traffic management C-V2X technology which will improve travel times and efficiency. https://t.co/jQn25V0iaN http://twitter.com/INRIX/status/1401961647145508864 https://t.co/jQn25V0iaN June 08, 2021 at 02:57AM

Using @INRIX Probe Data, @VaDOT tracked location, time, frequency, percentages & communicated the information via heat maps, graphs, columns, rows & color comparisons. Up to 60% of drivers exceeded the posted speed limit by 10 mph or more between 5-10am. https://t.co/67tY0mSEes

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Favorite tweet: Using @INRIX Probe Data, @VaDOT tracked location, time, frequency, percentages & communicated the information via heat maps, graphs, columns, rows & color comparisons. Up to 60% of drivers exceeded the posted speed limit by 10 mph or more between 5-10am. https://t.co/67tY0mSEes — INRIX ® (@INRIX) Jun 4, 2021 Using @INRIX Probe Data, @VaDOT tracked location, time, frequency, percentages & communicated the information via heat maps, graphs, columns, rows & color comparisons. Up to 60% of drivers exceeded the posted speed limit by 10 mph or more between 5-10am. https://t.co/67tY0mSEes http://twitter.com/INRIX/status/1400869162956705793 https://t.co/67tY0mSEes June 05, 2021 at 02:36AM

클라우드 기반 공간정보로 완성되는 한국판 뉴딜 https://t.co/FBcPAyBhsf

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Favorite tweet: 클라우드 기반 공간정보로 완성되는 한국판 뉴딜 https://t.co/FBcPAyBhsf — 국토교통부 (@Korea_land) June 2, 2021 클라우드 기반 공간정보로 완성되는 한국판 뉴딜 https://t.co/FBcPAyBhsf http://twitter.com/Korea_land/status/1399982381604343809 https://t.co/FBcPAyBhsf June 02, 2021 at 03:53PM

자율협력주행 중소기업 육성에 힘 모은다 https://t.co/rozFa1tyUM

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Favorite tweet: 자율협력주행 중소기업 육성에 힘 모은다 https://t.co/rozFa1tyUM — 국토교통부 (@Korea_land) June 2, 2021 자율협력주행 중소기업 육성에 힘 모은다 https://t.co/rozFa1tyUM http://twitter.com/Korea_land/status/1399910465153630208 https://t.co/rozFa1tyUM June 02, 2021 at 11:07AM

위험운전 행동 분석을 통한 ‘맞춤형 안전대책’ 추진 https://t.co/6sgFWTbmtS

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Favorite tweet: 위험운전 행동 분석을 통한 ‘맞춤형 안전대책’ 추진 https://t.co/6sgFWTbmtS — 국토교통부 (@Korea_land) June 1, 2021 위험운전 행동 분석을 통한 ‘맞춤형 안전대책’ 추진 https://t.co/6sgFWTbmtS http://twitter.com/Korea_land/status/1399546485474164736 https://t.co/6sgFWTbmtS June 01, 2021 at 11:00AM

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future https://t.co/1ADuK3PP4s by David Ahmedt-Aristizabal et al. #MachineLearning #DeepLearning

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Favorite tweet: Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future https://t.co/1ADuK3PP4s by David Ahmedt-Aristizabal et al. #MachineLearning #DeepLearning — arXiv Daily (@arXiv_Daily) May 29, 2021 Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future https://t.co/1ADuK3PP4s by David Ahmedt-Aristizabal et al. #MachineLearning #DeepLearning http://twitter.com/arXiv_Daily/status/1398506402939916288 https://t.co/1ADuK3PP4s May 29, 2021 at 02:08PM