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Deep Learning for Computer Vision : Neural Networks 3

https://cs231n.github.io/neural-networks-3/ CS231n Deep Learning for Computer VisionTable of Contents: Learning In the previous sections we’ve discussed the static parts of a Neural Networks: how we can set up the network connectivity, the data, and the loss function. This section is devoted to the dynamics, or in other words, the procecs231n.github.io이번에는 위 내용에 대해서 한글로 정리해보겠습니다. 학습지난 섹션에서는 인공신경..

Deep Learning for Computer Vision : Neural Networks 2

https://cs231n.github.io/neural-networks-2/ CS231n Deep Learning for Computer VisionTable of Contents: Setting up the data and the model In the previous section we introduced a model of a Neuron, which computes a dot product following a non-linearity, and Neural Networks that arrange neurons into layers. Together, these choices define thecs231n.github.io이번에는 위 내용을 한글로 정리해보겠습니다. 데이터와 모델 설정하기이전 섹..

Deep Learning for Computer Vision : Neural Networks 1

https://cs231n.github.io/neural-networks-1/ CS231n Deep Learning for Computer VisionTable of Contents: Quick intro It is possible to introduce neural networks without appealing to brain analogies. In the section on linear classification we computed scores for different visual categories given the image using the formula \( s = W x \), whecs231n.github.io이번에는 위 내용을 한글로 정리해보겠습니다. 빠른 소개 (Quick intr..

Deep Learning for Computer Vision : Backpropagation, Intuitions

https://cs231n.github.io/optimization-2/CS231n Deep Learning for Computer VisionTable of Contents: Introduction Motivation. In this section we will develop expertise with an intuitive understanding of backpropagation, which is a way of computing gradients of expressions through recursive application of chain rule. Understanding of thics231n.github.io이번에는 위 내용을 한글로 정리해보겠습니다.개요이 섹션에서 해결하려는 주요 문제는 ..

Deep Learning for Computer Vision : Optimization

https://cs231n.github.io/optimization-1/ CS231n Deep Learning for Computer VisionTable of Contents: Introduction In the previous section we introduced two key components in context of the image classification task: A (parameterized) score function mapping the raw image pixels to class scores (e.g. a linear function) A loss function thacs231n.github.io이번에는 위 내용에 대해서 한글로 정리해보겠습니다. 최적화(Optimization..

Deep Learning for Computer Vision : Linear Classification

https://cs231n.github.io/linear-classify/ CS231n Deep Learning for Computer VisionTable of Contents: Linear Classification In the last section we introduced the problem of Image Classification, which is the task of assigning a single label to an image from a fixed set of categories. Moreover, we described the k-Nearest Neighbor (kNN) clcs231n.github.io이번에는 위 내용에 대해서 한글로 정리해보겠습니다.선형 분류 개요이미지 분류를 ..

https://cs231n.github.io/classification/ CS231n Deep Learning for Computer VisionThis is an introductory lecture designed to introduce people from outside of Computer Vision to the Image Classification problem, and the data-driven approach. The Table of Contents: Image Classification Motivation. In this section we will introduce the Imcs231n.github.io 이번에는 위 내용에 대해서 한글로 정리하도록 하겠습니다. 이미지 분류고정된 카테..