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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..