Gratefulness and Joy

이번 강의는 Coursera 강의만으로는 이해가 가지 않아서 유튜브 영상을 참고하여 공부했다. Programming Assignment를 하고 나면 조금 더 이해가 잘 가겠지?! 퀴즈 틀린문제 정리 Question 7 Neural style transfer is trained as a supervised learning task in which the goal is to input two images (x), and train a network to output a new, synthesized image (y). True/False => 답: False Neural Style Transfer는 Parameter를 학습하는 게 아니라, Generated Image의 픽셀값을 업데이트 해서 J(Cost)를..

[강의에서 언급된 논문 링크] 1. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 2. MobileNetV2: Inverted Residuals and Linear Bottlenecks 3. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

[강의에서 언급된 논문 링크] 1. Deep Residual Learning for Image Recognition(ResNet) 2. Going Deeper with Convolutions (Inception Network)

[강의에서 언급된 논문 링크] 1. Gradient-Based Learning Applied to Document Recognition (LesNet-5) 2. ImageNet Classification with Deep Convolutional Neural Networks (AlexNet) 3. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION (AGG-16)