which is now Skipping effectively compresses the network into fewer layers in the initial training stages, which speeds learning. I wrote The intuition on why this work is that the neural network collapses into fewer layers in the initial phase, which makes it easier to learn, and then gradually expands as it learns more of the feature space. Jeblad ( talk) 20:27, 6 March 2019 (UTC) Compressed layers? Staying close to the manifold is not something that only happen during final training. When skipping is progressively dropped, with further learning in progress, then the network will stay close to the manifold during this learning. Initial learning with skipped layers will bring the solution somewhat close to the manifold.

but now it is Towards the end of training, when all layers are expanded, it stays closer to the manifold and thus learns faster. I wrote During later learning it will stay closer to the manifold and thus learn faster. Preceding unsigned comment added by Petkond ( talk О” w в„“ в€’ 1, в„“ := в€’ О· в€‚ E в€‚ w в„“ в€’ 1, в„“ = в€’ О· a в„“ в€’ 1 в‹… Оґ в„“ ДЄs the learning rules are similar the weight matrices can be merged and learned in the same step. Things you can help WikiProject Computer science with:Дuring backpropagation learning for the normal path This article has been rated as Low-importance on the project's importance scale. Computer science Wikipedia:WikiProject Computer science Template:WikiProject Computer science Computer science articles If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks. This article is within the scope of WikiProject Computer science, a collaborative effort to improve the coverage of Computer science related articles on Wikipedia. Computing Wikipedia:WikiProject Computing Template:WikiProject Computing Computing articles This article is within the scope of WikiProject Computing, a collaborative effort to improve the coverage of computers, computing, and information technology on Wikipedia. This article has been rated as Mid-importance on the project's importance scale. This article has been rated as Start-Class on the project's quality scale. Robotics Wikipedia:WikiProject Robotics Template:WikiProject Robotics Robotics articles If you would like to participate, you can choose to edit this article, or visit the project page ( Talk), where you can join the project and see a list of open tasks. Residual neural network is within the scope of WikiProject Robotics, which aims to build a comprehensive and detailed guide to Robotics on Wikipedia. Cognitive science Wikipedia:WikiProject Cognitive science Template:WikiProject Cognitive science Cognitive science articles This article is within the scope of WikiProject Cognitive science, a project which is currently considered to be inactive. This article is of interest to the following WikiProjects: