Deep Residual Learning for Image Recognition
Abstract - Summary: The paper introduces and explains training of a new class of large networks known as Residual Networks, Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. Paper - https://arxiv.org/abs/1512.03385 Code - https://github.com/robbertliu/deeplearning.ai-andrewNG/blob/master/COURSE%204%20Convolutional%20Neural%20Networks/Week%2002/ResNets/Residual%20Networks.ipynb Dataset - links available in repo