Examples of using Convolutional in English and their translations into Korean
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So, why convolutional filter is better?
All right, let's apply one last convolutional layer.
좋습니다, 마지막 컨볼루션 레이어 하나를 적용해 봅시다.What is Convolutional Neural Network?
그럼 Convolutional Neural Net에서 Convolution이 뭘까요?Striving for simplicity: The all convolutional net.”.
Striving for Simplicity: The All Convolutional Net'.Convolutional layers are often interweaved with pooling layers.
Convolutional layer는 종종 pooling layer와 엮이곤 한다.The cost of this second convolutional layer would be that well.
이 두 번째 컨볼루션 레이어의 비용은 잘 될 것입니다.Convolutional Neural Networks for Sentence Classification.
Convolutional neural networks for sentence classification 논문을 소개한 슬라이드 입니다.We developed a 1D convolutional neural network.
여기에는 1D CNN(Convolutional Neural Network)을 사용했다.Convolutional Neural Networks(CNNs) are particularly suited for finding spatial patterns.
Convolutional Neural Networks (CNN)는 공간 패턴을 찾는 데 특히 적합합니다.Node Classification by Graph Convolutional Network.”.
Discriminator는 graph convolutional network 구조로 이루어져있다.Online Hands-On Convolutional Neural Networks with TensorFlow.
브랜드명 상품명 Hands-On Convolutional Neural Networks with Tensor.(2012) ImageNet classification with deep convolutional neural networks.
년 제프리힌튼 팀의 이미지 분류 논문(ImageNet Classification with Deep Convolutional Neural Networks).Very deep convolutional networks for text classification.
Very Deep Convolutional Networks for Text Classification 아주 좋은 예시 ↩.Titled“ImageNet Classification with Deep Convolutional Neural Networks.
AlexNet의 original 논문명은 "ImageNet Classification with Deep Convolutional Neural Networks"이다.CS231n Convolutional Neural Networks for Visual Recognition.
스탠포드 강의: CS231n Convolutional Neural Networks for Visual Recognition.Last year Apple announced the Metal CNN and BNNS frameworks for creating basic convolutional networks.
작년 애플은 기본 컨볼루셔널 신경망(basic convolutional networks)을 위한 Metal CNN and BNNS frameworks를 발표했다.CS231n Convolutional Neural Networks for Visual Recognition.
번역CS231n - 시각 인식을 위한 CNN(Convolutional Neural Networks for Visual Recognition).It contains 9 layers,with one normalization layer, 5 convolutional layers and 3 fully-connected layer.
총 9개의 layer가 포함되어 있으며, 1 normalization layer,5 convolutional layers, 3 fully connected layers로 이루어져 있다.Supports both convolutional networks and recurrent networks, and combinations of both.
Convolutional 네트워크와 recurrent 네트워크를 지원하고, 두 개의 결합도 가능하다.AlexNet consists of 5 Convolutional Layers and 3 Fully Connected Layers.
AlexNet은 다섯 개의 convolutional layer, 세 개의 fully connected layer로 구성됨.Convolutional neural networks are a form of deep neural networks(DNNs) that engineers have recently begun using for various recognition tasks.
CNN(Convolutional Neural Networks)은 DNN(Deep Neural Networks)의 한 형태로 최근 엔지니어들이 다양한 인식 작업에 사용하기 시작했다.Lecture 1| Introduction to Convolutional Neural Networks for Visual Recognition.
스탠퍼드대학 cs231n 수업 Convolutional Neural Networks for Visual Recognition.Vision Model Convolutional Neural Network, Image Classification, Recognition, and Cognitive Algorithm.
비전(Vision) 모델 Convolutional Neural Network, 이미지 분류· 인식· 인지 알고리즘.Regularization, CS231n Convolutional Neural Networks for Visual Recognition.
스탠퍼드대학 cs231n 수업 Convolutional Neural Networks for Visual Recognition.Supports both convolutional networks and recurrent networks, as well as combinations of the two.
Convolutional 네트워크와 recurrent 네트워크를 지원하고, 두 개의 결합도 가능하다.Therefore, the output of the convolutional layer will actually be 3 dimensional(again, for a 2D image).
따라서 컨볼루션 레이어의 출력은 실제로 3차원이 됩니다(다시 2D 이미지의 경우).Taken from“Convolutional Neural Networks for Sentence Classification”, 2014.
이번 논문은 2014년 EMNLP에 발표된 “Convolutional Neural Networks for Sentence Classification”입니다.The author proposed a Dynamic Convolutional Neural Network(DCNN) architecture for sentence modeling tasks.
이들은 문장의 의미를 모델링하기 위한 dynamic convolutional neural network(DCNN)을 제안했다.DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.
더 많은 정보는 DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition을 통해 보면 된다.Voyager introduced Reed- Solomon coding concatenated with convolutional codes, a practice that has since become very widespread in deep space and satellite(e.g., direct digital broadcasting) communications.
Voyager는 컨볼루션 코드와 결합된 리드 솔로몬 코딩을 도입했으며, 이는 이후 깊은 공간과 위성(예: 직접 디지털 방송) 통신에서 매우 널리 보급되었습니다.
