{"action":"opened","issue":{"url":"https://api.github.com/repos/linkinpark213/linkinpark213.github.io/issues/5","repository_url":"https://api.github.com/repos/linkinpark213/linkinpark213.github.io","labels_url":"https://api.github.com/repos/linkinpark213/linkinpark213.github.io/issues/5/labels{/name}","comments_url":"https://api.github.com/repos/linkinpark213/linkinpark213.github.io/issues/5/comments","events_url":"https://api.github.com/repos/linkinpark213/linkinpark213.github.io/issues/5/events","html_url":"https://github.com/linkinpark213/linkinpark213.github.io/issues/5","id":318723308,"number":5,"title":"A Review of ResNet - Residual Networks | Linkin213's Park","user":{"login":"linkinpark213","id":12746381,"avatar_url":"https://avatars0.githubusercontent.com/u/12746381?v=4","gravatar_id":"","url":"https://api.github.com/users/linkinpark213","html_url":"https://github.com/linkinpark213","followers_url":"https://api.github.com/users/linkinpark213/followers","following_url":"https://api.github.com/users/linkinpark213/following{/other_user}","gists_url":"https://api.github.com/users/linkinpark213/gists{/gist_id}","starred_url":"https://api.github.com/users/linkinpark213/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/linkinpark213/subscriptions","organizations_url":"https://api.github.com/users/linkinpark213/orgs","repos_url":"https://api.github.com/users/linkinpark213/repos","events_url":"https://api.github.com/users/linkinpark213/events{/privacy}","received_events_url":"https://api.github.com/users/linkinpark213/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-04-29T14:54:23Z","updated_at":"2018-04-29T14:54:23Z","closed_at":null,"author_association":"OWNER","body":"https://linkinpark213.com/2018/04/22/resnet/ \n\n \n\n0 IntroductionDeep learning researchers have been constructing skyscrapers in recent years. Especially, VGG nets and GoogLeNet have pushed the depths of convolutional networks to the extreme. But questions remain: if time and money aren&#39;t probl"}}
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