D3 네이버소셜앱스 어떻게 만들어야 하나

안드로이드 6.0 마시멜로 대응을 위해 먼저 확인해야 할 항목들, App Linking, Fingerprint API등의 새로운 기능들과 Runtime Permission, Doze 모드등 주요 변경사항을 정리했��니다.

인공지능 기술의 발전으로 인한 사회의 변화 및 생활의 변화를 예측하고, 인공지능 시장을 대비하여 무엇을 준비해야하는지에 대해 고찰한다.

200819 NAVER TECH CONCERT - 옥수환 100만 달러짜리 빠른 앱을 만드는 비법 전수

1. 안드로이드 내부에서 일어나는 일들(feat. 렌더링) 2. View의 성능을 끌어올리는 팁 3. 앱 성능을 개선하기 위한 도구

Dreamweaver CS5.5 를 이용한 jQuery Mobile 개발

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Light Tutorial Django Studybee 3주차 - 가볍게 배우는 장고!! Django를 이용해 블로그를 만들기 전에 가볍게 Django에 대해 알아보고 익숙해져 봅시다. **http://www.studybee.kr 에서 운영하는 '초심자를 위한 웹개발' 클래스에서 만드는 교재이며, 장고를 이용해 간단하게 블로그를 만드는 것을 목표로 하고 있습니다.

[124] 하이브리드 앱 개발기 김한솔

< 2016 3rd UX Trend Report Part1> 라이트브레인 UX 트렌드 리포트 UX Discovery는 해외 다양한 매체들을 통해 하루 평균 50여건의 트렌드를 탐색, 수집, 검토하며 UX적 관점에서 분야별로 분석해서 정리됩니다. 2016 UX Discovery 3호에서는 본격적인 AI시대의 진입을 맞아 선보이는 다양한 AI제품들과 서비스 그리고 빅데이터를 활용한 지진감지 경고앱과 같은 최신앱에서 가상현실, 웨어러블 등 뉴 UX 트랜드들도 한번에 살펴 보실 수 있습니다. 이��� 1부에서는 새로운 앱(New App), 인공지능(Artificial Intelligence), 가상현실(Virtual Reality), 증강현실(Augmented Reality) 분야의 최신 트렌드를 담고 있으며 전체 리포트는 총 248페이지로, 나머지 내용 및 자세한 정보는 라이트브레인 웹사이트(www.rightbrain.co.kr)와 블로그에서 확인할 수 있습니다.

Progressive Web Apps - Get your site out of the tab! A talk slide shared to Naver in Jan 2016 and to KIG in April 2016.

플러터의 기초내용과 자주 사용하는 라이브러리 소개, 활용 사례들을 정리한 발표 세션입니다.

Open source APM Scouter로 모니터링 잘 하기 - Application performance monitoring https://github.com/scouter-project/scouter

[PAG 비즈니스 플랫폼데이] 네이버 앱스토어

A session presentation for WebDevMobile Open Seminar at Aug. 14, 2010.

The document discusses various machine learning clustering algorithms like K-means clustering, DBSCAN, and EM clustering. It also discusses neural network architectures like LSTM, bi-LSTM, and convolutional neural networks. Finally, it presents results from evaluating different chatbot models on various metrics like validation score.

[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler in IPVS, Linux Kernel

[215] Druid로 쉽고 빠르게 데이터 분석하기

[245]Papago Internals: 모델분석과 응용기술 개발

[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈

[235]Wikipedia-scale Q&A

The document discusses challenges with using reinforcement learning for robotics. While simulations allow fast training of agents, there is often a "reality gap" when transferring learning to real robots. Other approaches like imitation learning and self-supervised learning can be safer alternatives that don't require trial-and-error. To better apply reinforcement learning, robots may need model-based approaches that learn forward models of the world, as well as techniques like active localization that allow robots to gather targeted information through interactive perception. Closing the reality gap will require finding ways to better match simulations to reality or allow robots to learn from real-world experiences.

This document describes research on using deep learning to predict student performance in massive open online courses (MOOCs). It introduces GritNet, a model that takes raw student activity data as input and predicts outcomes like course graduation without feature engineering. GritNet outperforms baselines by more than 5% in predicting graduation. The document also describes how GritNet can be adapted in an unsupervised way to new courses using pseudo-labels, improving predictions in the first few weeks. Overall, GritNet is presented as the state-of-the-art for student prediction and can be transferred across courses without labels.

This document provides a summary of new datasets and papers related to computer vision tasks including object detection, image matting, person pose estimation, pedestrian detection, and person instance segmentation. A total of 8 papers and their associated datasets are listed with brief descriptions of the core contributions or techniques developed in each.

그림이 정상 출력되는 다음 링크의 자료를 확인해 주세요. https://www.slideshare.net/deview/233-network-load-balancing-maglev-hashing-scheduler-in-ipvs-linux-kernel

This document presents a formula for calculating the loss function J(θ) in machine learning models. The formula averages the negative log likelihood of the predicted probabilities being correct over all samples S, and includes a regularization term λ that penalizes predicted embeddings being dissimilar from actual embeddings. It also defines the cosine similarity term used in the regularization.

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기

[224]네이버 검색과 개인화

[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)

The document discusses running a TensorFlow Serving (TFS) container using Docker. It shows commands to: 1. Pull the TFS Docker image from a repository 2. Define a script to configure and run the TFS container, specifying the model path, name, and port mapping 3. Run the script to start the TFS container exposing port 13377

The document discusses linear algebra concepts including: - Representing a system of linear equations as a matrix equation Ax = b where A is a coefficient matrix, x is a vector of unknowns, and b is a vector of constants. - Solving for the vector x that satisfies the matrix equation using linear algebra techniques such as row reduction. - Examples of matrix equations and their component vectors are shown.

This document describes the steps to convert a TensorFlow model to a TensorRT engine for inference. It includes steps to parse the model, optimize it, generate a runtime engine, serialize and deserialize the engine, as well as perform inference using the engine. It also provides code snippets for a PReLU plugin implementation in C++.

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

The document discusses machine reading comprehension (MRC) techniques for question answering (QA) systems, comparing search-based and natural language processing (NLP)-based approaches. It covers key milestones in the development of extractive QA models using NLP, from early sentence-level models to current state-of-the-art techniques like cross-attention, self-attention, and transfer learning. It notes the speed and scalability benefits of combining search and reading methods for QA.