{"action":"opened","issue":{"url":"https://api.github.com/repos/tpai/tpai.github.io/issues/35","repository_url":"https://api.github.com/repos/tpai/tpai.github.io","labels_url":"https://api.github.com/repos/tpai/tpai.github.io/issues/35/labels{/name}","comments_url":"https://api.github.com/repos/tpai/tpai.github.io/issues/35/comments","events_url":"https://api.github.com/repos/tpai/tpai.github.io/issues/35/events","html_url":"https://github.com/tpai/tpai.github.io/issues/35","id":322765835,"number":35,"title":"Machine Learning","user":{"login":"tpai","id":579145,"avatar_url":"https://avatars3.githubusercontent.com/u/579145?v=4","gravatar_id":"","url":"https://api.github.com/users/tpai","html_url":"https://github.com/tpai","followers_url":"https://api.github.com/users/tpai/followers","following_url":"https://api.github.com/users/tpai/following{/other_user}","gists_url":"https://api.github.com/users/tpai/gists{/gist_id}","starred_url":"https://api.github.com/users/tpai/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tpai/subscriptions","organizations_url":"https://api.github.com/users/tpai/orgs","repos_url":"https://api.github.com/users/tpai/repos","events_url":"https://api.github.com/users/tpai/events{/privacy}","received_events_url":"https://api.github.com/users/tpai/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-05-14T10:52:35Z","updated_at":"2018-05-14T10:52:35Z","closed_at":null,"author_association":"OWNER","body":"Teaching machine how to do the performance action/decision based on the past experience.\r\n\r\n## RNN\r\n\r\n```\r\nINPUT  +----> RECURRENT +----> OUTPUT\r\n  ^           NEURAL              +\r\n  |           NETWORK             |\r\n  |                               |\r\n  +-------------------------------+\r\n```\r\n\r\nInput and output are all vectors, the purpose of model training is to gain the multi-dimension vector with lots of parameters, and do the calculation to predict the result then on and on to improve the model to raise the accurancy.\r\n\r\n## Decision Tree\r\n\r\nDecision Tree is built by data, and grow based on the scale of data, but it is not always correct.\r\n\r\nFor example, there's a recommendation system of app store, and data told us the age below 20 download PokemonGo, over 20 also female download WhatsApp, and male over 20 years old download SnapChat, so here we use age at first and then the gender to get the result.\r\n\r\n## Naive Byes Alogorithm\r\n\r\nNaive Byes Alogorithm is using particular features to classify the data and get the probability.\r\n\r\nFor example, how the email system decide to spam mail? Contain some keywords, wrong spelling or without subject title, and use these to get the spam probability of target mail.\r\n\r\n## Gradient Descent 梯度下降\r\n\r\nGradient Descent is finding possible ways to get close to the answer, the purpose is to minimize the error and whole trip is an optimization process.\r\n\r\n## Linear Regression 線性迴歸\r\n\r\nLinear Regression is to create a line which is the most close to data points on X-Y axis. Now we could predict specific value according to the line.\r\n\r\n## Logistic Regression 對數回歸\r\n\r\nLogistic Regression is much like but there's few difference between them, such as the output of linear regression could be negative(could not be used as probabilities), linear regression could have infinite outcome but logistic could not, the response varialbe of logistic regression is categorical(yes/no, true/false, 1st/2nd/3rd), but linear is not it should be continuous(weight, height, hours).\r\n\r\n> Ref: https://stackoverflow.com/a/39227073"}}
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