[SuperMap 기술세미나] 조직, IT환경을 고려한 공간정보 통합 재해 시ᄉ...KwangJin So
공간정보 통합 재해 시스템 구축에 어려움을 겪고있던 개발팀 PM이 찾은 답은 무엇일까요?
지난 2014.11.19(수) 개최된 슈퍼맵 기술세미나의 자료를 공유합니다. 현장에서 많이 부딪히는 공간정보 구축 프로젝트의 이슈사항과 기술적인 고민을 들어보고 그 해결방안에 대해 SuperMap R&D팀이 겪은 경험과 지식을 나누었습니다.
참석한분들께는 모두 보여드렸지만 public하게 공유하기에는 민감한 부분의 발표 2꼭지는 제외하였습니다. 만약 제외된 내용이 궁금하시면 아래 이메일로 연락주십시요. 방문세미나를 진행할 수 있도록 추가연락 드리겠습니다. 연락이메일 : kjso@sphinfo.co.kr 소광진 대표
The document outlines the steps for conducting a deep learning experiment in Korean. It introduces the speaker and their background in artificial intelligence and natural language processing. It then lists the steps, which include understanding neural networks, deep neural networks with techniques like pretraining, rectified linear units and dropout, using the Theano library, writing deep learning code with Theano, and applying deep learning to natural language processing with libraries like Gensim. It also discusses recent interest in deep learning and example applications.
[SuperMap 기술세미나] 조직, IT환경을 고려한 공간정보 통합 재해 시ᄉ...KwangJin So
공간정보 통합 재해 시스템 구축에 어려움을 겪고있던 개발팀 PM이 찾은 답은 무엇일까요?
지난 2014.11.19(수) 개최된 슈퍼맵 기술세미나의 자료를 공유합니다. 현장에서 많이 부딪히는 공간정보 구축 프로젝트의 이슈사항과 기술적인 고민을 들어보고 그 해결방안에 대해 SuperMap R&D팀이 겪은 경험과 지식을 나누었습니다.
참석한분들께는 모두 보여드렸지만 public하게 공유하기에는 민감한 부분의 발표 2꼭지는 제외하였습니다. 만약 제외된 내용이 궁금하시면 아래 이메일로 연락주십시요. 방문세미나를 진행할 수 있도록 추가연락 드리겠습니다. 연락이메일 : kjso@sphinfo.co.kr 소광진 대표
The document outlines the steps for conducting a deep learning experiment in Korean. It introduces the speaker and their background in artificial intelligence and natural language processing. It then lists the steps, which include understanding neural networks, deep neural networks with techniques like pretraining, rectified linear units and dropout, using the Theano library, writing deep learning code with Theano, and applying deep learning to natural language processing with libraries like Gensim. It also discusses recent interest in deep learning and example applications.
GStreamer-VAAPI: Hardware-accelerated encoding and decoding on Intel hardware...Igalia
By Víctor M. Jáquez.
Slides at https://github.com/01org/gstreamer-vaapi/tree/master/docs/slides/gstconf2015
GStreamer-VAAPI is a set of GStreamer elements (vaapidecode, vaapipostroc, vaapisink, and several encoders) and libgstvapi, a library that wraps libva under a GObject/GStreamer semantics.
This talk will be about VAAPI and its integration with GStreamer. We will show a general overview of VAAPI architecture, the role of libgstvaapi, and finally, the design of GStreamer elements. Afterwards we will show what is ahead in the development of GStreamer-VAAPI, and the current problems and challenges.
The document provides an overview of the global mobile market in 2016, including key insights about app revenues, active devices, and consumers. Some of the main points covered are:
- Global direct consumer spending on apps will reach $44.8 billion in 2016 and be led by games, though non-game revenues are growing faster.
- China has overtaken the US as the largest app market, expected to generate $11.9 billion in revenues compared to $9.4 billion for the US.
- Apple has the largest share of active mobile devices worldwide at 34.8%, followed by Samsung, Huawei, Xiaomi, and Lenovo.
- There will be 2.3 billion active
2016 아이펀팩토리 Dev Day 발표 자료
강연 제목 : Docker 로 Linux 없이 Linux 환경에서 개발하기
발표자 : 김진욱 CTO
<2016>
- 일시 : 2016년 9월 28 수요일 12:00~14:20
- 장소 : 넥슨 판교 사옥 지하 1층 교육실
This document discusses optimizing object-oriented code for performance. It begins with an overview of object-oriented programming and how CPU and memory performance have changed significantly since C++ was first created. It then analyzes a common scene tree example and finds it is slow due to excessive cache misses from scattered data. The solution is to restructure the code to have homogeneous, sequential data by allocating nodes and matrices contiguously in memory. Processing data in order and removing virtual function calls further improves performance. Prefetching is also able to reduce cache misses, resulting in a 6x speedup over the original implementation. The key lessons are to optimize for data locality and consider data-oriented design principles when performance is important.
The document discusses cloud networking, software-defined networking (SDN), and OpenStack. It covers characteristics of cloud networking like multi-tenancy and east-west traffic. SDN is described as enabling API-driven networking. Issues with OpenStack networking are mentioned. The document raises questions about SDN standards, controllers, and viable SDN solutions.
This document discusses software defined storage based on OpenStack. It provides background on the author's experience including medical image processing, Linux kernel development, and OpenStack components like Heat, SDN and OPNFV. It then discusses several OpenStack storage components - Cinder for block storage, Swift for object storage, and Manila for shared file systems. It explains how these components work, their APIs and plugins to interface with different backend storage systems. Finally, it compares Cinder, Swift and other technologies like Ceph.
1. Virtual networks and cloud platforms need to collaborate as companies extend their networks across public clouds.
2. NSX supports major public clouds like AWS and Azure, allowing customers to consistently manage networks and security across private and public clouds.
3. NSX aims to connect and secure applications across private and public multiple clouds by creating private networks within or across clouds and defining logical networking and security policies.
2017년 5월 25일 (목), IBM과 함께 하는 오픈스택 정기 세미나에서 IBM 김민석 과장님께서 발표해 주신 자료를 공유합니다.
- IBM 클라우드에 대해 궁금하신 사항 있으시면, IBM 담당자께 contact 바랍니다.
(한국IBM 클라우드 마케팅 담당 임지현, jihlim@kr.ibm.com)
This document discusses Kubernetes and its integration with OpenStack. It begins with an introduction to Kubernetes and how it manages containerized applications across multiple hosts. It then compares virtualization and containers, describing the architecture and components of Kubernetes including pods, services, and rolling upgrades. The document outlines how Kubernetes is implemented in OpenStack using Nova Docker, Murano, and Magnum. It concludes with a Q&A section.
GStreamer-VAAPI: Hardware-accelerated encoding and decoding on Intel hardware...Igalia
By Víctor M. Jáquez.
Slides at https://github.com/01org/gstreamer-vaapi/tree/master/docs/slides/gstconf2015
GStreamer-VAAPI is a set of GStreamer elements (vaapidecode, vaapipostroc, vaapisink, and several encoders) and libgstvapi, a library that wraps libva under a GObject/GStreamer semantics.
This talk will be about VAAPI and its integration with GStreamer. We will show a general overview of VAAPI architecture, the role of libgstvaapi, and finally, the design of GStreamer elements. Afterwards we will show what is ahead in the development of GStreamer-VAAPI, and the current problems and challenges.
The document provides an overview of the global mobile market in 2016, including key insights about app revenues, active devices, and consumers. Some of the main points covered are:
- Global direct consumer spending on apps will reach $44.8 billion in 2016 and be led by games, though non-game revenues are growing faster.
- China has overtaken the US as the largest app market, expected to generate $11.9 billion in revenues compared to $9.4 billion for the US.
- Apple has the largest share of active mobile devices worldwide at 34.8%, followed by Samsung, Huawei, Xiaomi, and Lenovo.
- There will be 2.3 billion active
2016 아이펀팩토리 Dev Day 발표 자료
강연 제목 : Docker 로 Linux 없이 Linux 환경에서 개발하기
발표자 : 김진욱 CTO
<2016>
- 일시 : 2016년 9월 28 수요일 12:00~14:20
- 장소 : 넥슨 판교 사옥 지하 1층 교육실
This document discusses optimizing object-oriented code for performance. It begins with an overview of object-oriented programming and how CPU and memory performance have changed significantly since C++ was first created. It then analyzes a common scene tree example and finds it is slow due to excessive cache misses from scattered data. The solution is to restructure the code to have homogeneous, sequential data by allocating nodes and matrices contiguously in memory. Processing data in order and removing virtual function calls further improves performance. Prefetching is also able to reduce cache misses, resulting in a 6x speedup over the original implementation. The key lessons are to optimize for data locality and consider data-oriented design principles when performance is important.
The document discusses cloud networking, software-defined networking (SDN), and OpenStack. It covers characteristics of cloud networking like multi-tenancy and east-west traffic. SDN is described as enabling API-driven networking. Issues with OpenStack networking are mentioned. The document raises questions about SDN standards, controllers, and viable SDN solutions.
This document discusses software defined storage based on OpenStack. It provides background on the author's experience including medical image processing, Linux kernel development, and OpenStack components like Heat, SDN and OPNFV. It then discusses several OpenStack storage components - Cinder for block storage, Swift for object storage, and Manila for shared file systems. It explains how these components work, their APIs and plugins to interface with different backend storage systems. Finally, it compares Cinder, Swift and other technologies like Ceph.
1. Virtual networks and cloud platforms need to collaborate as companies extend their networks across public clouds.
2. NSX supports major public clouds like AWS and Azure, allowing customers to consistently manage networks and security across private and public clouds.
3. NSX aims to connect and secure applications across private and public multiple clouds by creating private networks within or across clouds and defining logical networking and security policies.
2017년 5월 25일 (목), IBM과 함께 하는 오픈스택 정기 세미나에서 IBM 김민석 과장님께서 발표해 주신 자료를 공유합니다.
- IBM 클라우드에 대해 궁금하신 사항 있으시면, IBM 담당자께 contact 바랍니다.
(한국IBM 클라우드 마케팅 담당 임지현, jihlim@kr.ibm.com)
This document discusses Kubernetes and its integration with OpenStack. It begins with an introduction to Kubernetes and how it manages containerized applications across multiple hosts. It then compares virtualization and containers, describing the architecture and components of Kubernetes including pods, services, and rolling upgrades. The document outlines how Kubernetes is implemented in OpenStack using Nova Docker, Murano, and Magnum. It concludes with a Q&A section.
100% Serverless big data scale production Deep Learning Systemhoondong kim
- BigData Sale Deep Learning Training System (with GPU Docker PaaS on Azure Batch AI)
- Deep Learning Serving Layer (with Auto Scale Out Mode on Web App for Linux Docker)
- BigDL, Keras, Tensorlfow, Horovod, TensorflowOnAzure
The bleeding edge of machine learning stream in 2017 - APAC ML/DS Community ...Jeongkyu Shin
Video (Korean): https://www.youtube.com/watch?v=r64_PeoZvao
기계학습은 최근의 연구 성과 및 기술의 발전에 힘입어 다양한 분야에 본격적으로 적용되기 시작했습니다. 2017년은 응용분야의 확장에 힘입어 기계학습 응용이 대중화되는 한 해가 될 것입니다. 이 발표에서는 기계학습이 해결한 기술적인 문제와, 현재 해결하려고 하는 난제들을 다룹니다. 또한 2017년 현재 기계학습이 응용되고 있는 분야들과 응용 방법 및, 이후 기계학습 적용을 통해 발전할 수 있는 분야들과 적용 아이디어를 이야기합니다.
Machine learning has been applied to various areas in earnest owing to recent research results and technological advancements. In 2017, machine learning application will be popular with the expansion of the application area. This talk covers technical issues solved by machine learning, and difficult problems that should be solved now. It also covers the areas that apply machine learning in 2017, application methods, area that can develop by application machine learning, and application ideas.
- E-commerce BigData Scale AI Journey
- BigData Scale Deep Learning Production System Use Case
- Deep Learning, Cloud PaaS, Microservices, DevOps, etc.
- E-Commerce AI Production System Strategy
AI_introduction and requirements(2024.05.12).pdfLee Chanwoo
AI_introduction and requirements, Considerations for introducing artificial intelligence, understanding machine learning, artificial intelligence security, considerations for introducing ChatGPT, future of generative AI
[ http://infiniflux.com/download ]
The world's fastest time series DBMS.
What is InfiniFlux?
1) InfiniFlux is a time-series database which performs real-time data processing, i.e., data are inserted at high speed, retrieved and analyzed without elapsed time.
2) InfiniFlux also compresses and stores data in real-time. Its query language and syntax complies with the SQL standard. The extended SQL syntax provides additional features such as the text search tool.
(오리지널 구글 프리젠테이션은 http://goo.gl/uiX2UH 에)
- 권재명 (Jaimyoung Kwon)
1. 실리콘 벨리 데이터 기업들
2. 온라인 광고 사업
3. 데이터 사이언티스트, 데이터 엔지니어, 머신러닝 사이언티스트
4. 실리콘 벨리 데이터 사이언티스트의 하루
5. 데이터 사이언스 툴채인
6. 데이터 사이언스 베스트 프랙티스
7. 데이터 사이언스 필수 통계 개념
8. 사내 데이터 사이언스 도입
대표적인 인터넷 서비스인 온라인게임에 존재하는 대표적인 fraud 인 게임봇/작업장에 대해 소개하고 이를 탐지하기 위한 알고리즘을 사례와 함께 설명한다.
더불어 간편결제 서비스에 지속적인 공격이 발생하고 있는데, 이를 조기 탐지하기 위한 방법에는 어떠한 것이 있을지 소개하도록 한다.
71. 코딩도? /*
* Increment the size file of the new incorrect UI_FILTER group information
* of the size generatively.
*/
static int indicate_policy(void)
{
int error;
if (fd == MARN_EPT) {
/*
* The kernel blank will coeld it to userspace.
*/
if (ss->segment < mem_total)
unblock_graph_and_set_blocked();
else
ret = 1;
goto bail;
}
segaddr = in_SB(in.addr);
selector = seg / 16;
setup_works = true;
for (i = 0; i < blocks; i++) {
seq = buf[i++];
bpf = bd->bd.next + i * search;
if (fd) {
current = blocked;
}
}
rw->name = "Getjbbregs";
bprm_self_clearl(&iv->version);
regs->new = blocks[(BPF_STATS << info->historidac)] | PFMR_CLOBATHINC_SECONDS << 12;
return segtable;
}
87. Big Data
• 보통은 이게 없어서 진입장벽
• 하지만 제일 중요한게 함정
• 공개된 Dataset으로 공부/개발은 가능(MNIST, CIFAR-10 etc.)
• 포탈이 매우 유리
• 고객 동의 없이 학습에 사용하는 것은 불법임에 주의해야함.
88. 우리 나라는…
• 네이버
• 이미지, 음성처리 등에 다양하게 적용중
• http://deview.kr/2014/session?seq=26
• 엔씨소프트
• AI랩에서 게임에 적용중이라던데…
• 클디
• 카이스트 출신 스타트업. ILSVRC 세계 7위.
• http://scope.cldi.io/
• 카이스트 김대식 교수
• 서울대 장벽탄 교수
• https://www.youtube.com/watch?v=v3veAMEwoyU
• 엑소브레인
• 2013~2013년 1070억원
• ETRI, KAIST, Postech 등 26개 연구기관 366명 참여 프로젝트