D1 sn을활용한싸이bgm추천

[주요이슈] - 이벤트 및 이슈 부재로 전체 신규가입 및 설치수는 소폭으로 감소하였으나 TOP 10 앱스 설치수의 경우 전주대비 8% 상승 - 전주대비 화이트 이벤트 효과 감소 및 종료로 TOP10앱스 트래픽 소폭 감소

포트폴리오 #4

창업가들이 알아야할 지식재산 전략을 사례와 이론을 중심으로 작성하였습니다. 특히, 성공하고 있는 BLT의 고객사례를 많이 다루었습니다. - 특허권 이론 및 전략 - 정부지원사업을 통한 초기 창업전략 - 디자인권과 저작권의 비교 - 상표이론 및 좋은 브랜드 네이밍 문의 : shawn@blte.kr (엄정한 변리사)

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.