{"componentChunkName":"component---src-components-posts-post-template-index-js","path":"/AI/Andrew_Ng/Machine-Learning/Week1/","result":{"data":{"mdx":{"body":"function _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n  \"title\": \"Machine Learning 수강 정리 - Week 1\",\n  \"date\": \"2020-12-06T00:00:00.000Z\",\n  \"draft\": true,\n  \"tags\": [\"ML\", \"AI\", \"Coursera\", \"Andrew Ng\", \"Stanford\", \"Cost Function\", \"Gradient Descent\", \"BGD\"],\n  \"excerpt\": \"Machine Learning Class by Andrew Ng, Stanford --Coursera Summary(20.12.07-21.03.01)\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n  return function MDXDefaultShortcode(props) {\n    console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n    return mdx(\"div\", props);\n  };\n};\n\nvar Warning = makeShortcode(\"Warning\");\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n      props = _objectWithoutProperties(_ref, [\"components\"]);\n\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"h2\", {\n    \"id\": \"machine-learning\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h2\"\n  }, {\n    \"href\": \"#machine-learning\",\n    \"aria-label\": \"machine learning permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"Machine Learning\"), mdx(\"p\", null, \"Aruthr Samuel(1959) - \\uCEF4\\uD4E8\\uD130\\uAC00 \\uCCB4\\uC2A4\\uB97C \\uC2A4\\uC2A4\\uB85C \\uD559\\uC2B5\\uD558\\uB3C4\\uB85D \\uD568\", mdx(\"br\", {\n    parentName: \"p\"\n  }), \"\\n\", \"Tom Michell(1998) - \\uD2B9\\uC815 \\uC791\\uC5C5(T)\\uC5D0 \\uB300\\uD55C \\uACBD\\uD5D8(E)\\uACFC \\uC131\\uB2A5\\uCE21\\uC815(P)\\uC73C\\uB85C\\uBD80\\uD130 \\uCEF4\\uD4E8\\uD130 \\uD504\\uB85C\\uADF8\\uB7A8\\uC774 \\uD559\\uC2B5\\uD558\\uB294\\uAC83\"), mdx(\"blockquote\", null, mdx(\"p\", {\n    parentName: \"blockquote\"\n  }, \"\\\"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.\\\"\")), mdx(\"h2\", {\n    \"id\": \"supervised--unsupervised\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h2\"\n  }, {\n    \"href\": \"#supervised--unsupervised\",\n    \"aria-label\": \"supervised  unsupervised permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"Supervised / Unsupervised\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"Supervised Learning\"), \" : \\uC54C\\uACE0\\uB9AC\\uC998\\uC5D0\\uAC8C \\uC815\\uB2F5\\uC774 \\uD3EC\\uD568\\uB41C data set(Training Set)\\uC744 \\uC900 \\uB4A4, input\\uACFC output\\uC758 \\uAD00\\uACC4\\uB97C \\uCC3E\\uC544 \\uB354 \\uB9CE\\uC740 \\uC815\\uB2F5\\uC744 \\uC608\\uCE21\\uD574\\uB0B4\\uB294 \\uD559\\uC2B5\\uBC29\\uC2DD\", mdx(\"ul\", {\n    parentName: \"li\"\n  }, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"Regression problem : output\\uC758 \\uD6C4\\uBCF4\\uAD70\\uC774 \\uC5F0\\uC18D\\uC801\\uC77C \\uB54C\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"Given a picture of a person, we have to predict their age on the basis of the given picture\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"Classification problem : input\\uC758 \\uC0AC\\uB840\\uB4E4\\uC774 \\uBD88\\uC5F0\\uC18D\\uC801(\\uC774\\uC0B0\\uC801)\\uC77C \\uB54C\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.\"))), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"Unsupervised Learning\"), \" : \\uC815\\uB2F5\\uC774 \\uC5C6\\uB294 \\uB370\\uC774\\uD130\\uB4E4\\uC744 clustering\\uD558\\uC5EC structure\\uB97C \\uADDC\\uBA85\\uD558\\uB294\\uAC83. \\uC774\\uB54C \\uC608\\uCE21\\uC758 \\uACB0\\uACFC\\uC5D0 \\uAE30\\uBC18\\uD55C \\uD53C\\uB4DC\\uBC31\\uC774 \\uC874\\uC7AC\\uD558\\uC9C0 \\uC54A\\uB294\\uB2E4.\", mdx(\"ul\", {\n    parentName: \"li\"\n  }, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"Non-clustering: The \\\"Cocktail Party Algorithm\\\", allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).\")))), mdx(\"hr\", null), mdx(\"h3\", {\n    \"id\": \"표기법\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h3\"\n  }, {\n    \"href\": \"#%ED%91%9C%EA%B8%B0%EB%B2%95\",\n    \"aria-label\": \"표기법 permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"\\uD45C\\uAE30\\uBC95\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"x\"), \" : input \\uB610\\uB294 feature(\\uD2B9\\uC131)\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"y\"), \" : output \\uB610\\uB294 \\\"target\\\" variable(\\uC608\\uCE21\\uAC12)\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"(x,y)\"), \" : \\uD558\\uB098\\uC758 Training example\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"m\"), \" : Training example\\uC758 \\uAC1C\\uC218(\\uC989, Training Set\\uC758 \\uD06C\\uAE30)\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"h\"), \" : hypothesis, x\\uC5D0\\uC11C y\\uAE4C\\uC9C0 \\uB3C4\\uB2EC\\uD558\\uB294 \\uACFC\\uC815(=\\uC9C0\\uB3C4)\")), mdx(\"hr\", null), mdx(\"h2\", {\n    \"id\": \"cost-function\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h2\"\n  }, {\n    \"href\": \"#cost-function\",\n    \"aria-label\": \"cost function permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"Cost Function\"), mdx(\"p\", null, \"\\uAC00\\uC124\\uD568\\uC218\\uC758 \\uC815\\uD655\\uB3C4\\uB97C \\uD310\\uB2E8\\uD560 \\uC218 \\uC788\\uB294 \\uC218\\uB2E8\\uC774 \\uB418\\uB294 \\uD568\\uC218\\uC774\\uB2E4.\"), mdx(\"p\", null, mdx(\"span\", _extends({\n    parentName: \"p\"\n  }, {\n    \"className\": \"gatsby-resp-image-wrapper\",\n    \"style\": {\n      \"position\": \"relative\",\n      \"display\": \"block\",\n      \"marginLeft\": \"auto\",\n      \"marginRight\": \"auto\",\n      \"maxWidth\": \"1024px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"14.84375%\",\n      \"position\": \"relative\",\n      \"bottom\": \"0\",\n      \"left\": \"0\",\n      \"backgroundImage\": \"url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAADCAYAAACTWi8uAAAACXBIWXMAABYlAAAWJQFJUiTwAAAAhklEQVQI101O2w6FIAzz/z9SYxBEIi8q4SLQc7qExCYN2+jWTu/7opSCWitaa8LneXAch8x67zLLOUudUoJzTjTsxx5f3pn2fce6rnLgvm+c5yn9sizQWiPGCGqu6xIqpTDPM4wx2LYN1loxG9oJf9CJJEIIsui9F1eCCQgmoCn/mHSk/2p+zY7nlJFGr0sAAAAASUVORK5CYII=')\",\n      \"backgroundSize\": \"cover\",\n      \"display\": \"block\"\n    }\n  })), \"\\n  \", mdx(\"img\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"alt\": \"Cost_Function\",\n    \"title\": \"Cost_Function\",\n    \"src\": \"/static/bff5ff42e67a83c4a0fea11ee168e1bd/2bef9/W1_Cost_Function.png\",\n    \"srcSet\": [\"/static/bff5ff42e67a83c4a0fea11ee168e1bd/6f3f2/W1_Cost_Function.png 256w\", \"/static/bff5ff42e67a83c4a0fea11ee168e1bd/01e7c/W1_Cost_Function.png 512w\", \"/static/bff5ff42e67a83c4a0fea11ee168e1bd/2bef9/W1_Cost_Function.png 1024w\", \"/static/bff5ff42e67a83c4a0fea11ee168e1bd/5148a/W1_Cost_Function.png 1228w\"],\n    \"sizes\": \"(max-width: 1024px) 100vw, 1024px\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    },\n    \"loading\": \"lazy\"\n  })), \"\\n    \")), mdx(\"p\", null, \"\\uC704\\uC758 \\uC2DD\\uC744 \\uBCF4\\uBA74, \\uC2E4\\uC81C input\\uC778 x\\uB97C \\uAC00\\uC124\\uD568\\uC218\\uC5D0 \\uC801\\uC6A9\\uC2DC\\uCF30\\uC744\\uB54C \\uB098\\uC624\\uB294 \\uAC12\\uACFC, \\uC2E4\\uC81C\\uC758 output\\uC774\\uC5C8\\uB358 y\\uB97C \\uBE44\\uAD50\\uD55C\\uB4A4 fancy\\uD558\\uAC8C \\uD3C9\\uADE0\\uB0B4\\uB294 \\uAC83\\uC744 \\uBCFC \\uC218 \\uC788\\uB2E4.\", mdx(\"br\", {\n    parentName: \"p\"\n  }), \"\\n\", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"\\uC774 \\uCC28\\uC774\\uB97C \\uCD5C\\uC18C\\uD654\\uD558\\uB294 h\\uB97C \\uCC3E\\uB294\\uAC83\"), \"\\uC774 \\uC6B0\\uB9AC\\uC758 \\uBAA9\\uC801\\uC774\\uBA70, Cost Function\\uC740 h\\uC758 \\uC815\\uD655\\uB3C4\\uB97C \\uD3C9\\uAC00\\uD558\\uAE30 \\uC704\\uD55C \\uC218\\uB2E8\\uC774\\uB2E4.\"), mdx(\"p\", null, \"\\uC704\\uC5D0\\uC11C \\uC18C\\uAC1C\\uD55C Cost Function J\\uB9D0\\uACE0\\uB3C4 \\uB2E4\\uB978 \\uB300\\uC548 \\uBE44\\uC6A9\\uD568\\uC218\\uB4E4\\uC774 \\uB9CE\\uB2E4. \\uC704\\uC5D0\\uC11C \\uC18C\\uAC1C\\uD55C \\uAC83\\uC740 \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Squared error function\"), \" \\uB610\\uB294 \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Mean square function\"), \" \\uC774\\uB77C\\uACE0 \\uD55C\\uB2E4.\\n\\uC774\\uAC83\\uC740 Gradient Descent \\uACC4\\uC0B0\\uC744 \\uC704\\uD574 \\uD3B8\\uC758\\uC801\\uC73C\\uB85C 1/2(mean)\\uCC98\\uB9AC\\uD558\\uC600\\uB2E4\\uB294 \\uAC83\\uC744 \\uC758\\uBBF8\\uD55C\\uB2E4.\"), mdx(\"hr\", null), mdx(\"h2\", {\n    \"id\": \"gradient-descent\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h2\"\n  }, {\n    \"href\": \"#gradient-descent\",\n    \"aria-label\": \"gradient descent permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"Gradient Descent\"), mdx(\"h3\", {\n    \"id\": \"outline\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h3\"\n  }, {\n    \"href\": \"#outline\",\n    \"aria-label\": \"outline permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"outline\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"\\uC784\\uC758\\uC758 \\uD30C\\uB77C\\uBBF8\\uD130 \\u03B8(\\uB4E4)\\uB85C\\uBD80\\uD130 \\uC2DC\\uC791(\\uC77C\\uBC18\\uC801\\uC73C\\uB85C default\\uB294 \\uBAA8\\uB4E0 \\uD30C\\uB77C\\uBBF8\\uD130\\uB4E4\\uC744 0\\uC73C\\uB85C \\uB450\\uB294\\uAC83)\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"\\uAE30\\uC874\\uC758 \\uD30C\\uB77C\\uBBF8\\uD130\\uB4E4\\uC744 Cost Function J\\uC758 \\uCD5C\\uC18C\\uAC12\\uC5D0 \\uB3C4\\uB2EC\\uD560 \\uB54C\\uAE4C\\uC9C0 \\uAC31\\uC2E0(\\uD558\\uAC15)\\uD55C\\uB2E4.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"\\uCD5C\\uC18C\\uAC12\\uC5D0 \\uB3C4\\uB2EC\\uD588\\uB2E4\\uB294 \\uAC83\\uC740, \\uD574\\uB2F9 \\uD30C\\uB77C\\uBBF8\\uD130\\uAC00 \\uC8FC\\uC5B4\\uC9C4 dataset\\uC744 \\uAC00\\uC7A5 \\uC798 \\uD45C\\uD604(\\uBE44\\uC2B7\\uD558\\uAC8C \\uBB18\\uC0AC)\\uD558\\uB294 \\uD568\\uC218\\uC758 \\uD30C\\uB77C\\uBBF8\\uD130\\uB77C\\uB294 \\uAC83\\uC744 \\uC758\\uBBF8\\uD55C\\uB2E4.\")), mdx(\"h3\", {\n    \"id\": \"description\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h3\"\n  }, {\n    \"href\": \"#description\",\n    \"aria-label\": \"description permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"Description\"), mdx(\"p\", null, mdx(\"span\", _extends({\n    parentName: \"p\"\n  }, {\n    \"className\": \"gatsby-resp-image-wrapper\",\n    \"style\": {\n      \"position\": \"relative\",\n      \"display\": \"block\",\n      \"marginLeft\": \"auto\",\n      \"marginRight\": \"auto\",\n      \"maxWidth\": \"1024px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"50.390625%\",\n      \"position\": \"relative\",\n      \"bottom\": \"0\",\n      \"left\": \"0\",\n      \"backgroundImage\": \"url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAKCAYAAAC0VX7mAAAACXBIWXMAABYlAAAWJQFJUiTwAAAB60lEQVQoz2WSzWtTQRTF80cJgpuKC3HnJ1iqLsSNKEJX3biUgu1WQUpX3VhBKmLBCkqtokhpIdXGQhWibZoaEk3e5+Tlzcz7eecljyZ64fLum7lz5sw9p5RlGS7ct8giXG20RhuNzYz8S1oz2LT9HArXXyqKo1WbH7ImJTODA+6TgpY27cqki4N1aa0ZIVIaZuZY5IeyfnNgFTvVTT6rHQ5o0PPKmPkZwuvX6D2YwTZrAigXpOkRYEFMqVieJ4X3HdNYpfZrgf1HE1TOnWFt+gQfkzHiznF4ewyen4Tl0yT7t/CznzntzPZfU0qSBM/rEPiKltmi7l8lbF0mOLwIXy9JjtPYHWNu9yZ3m0+Z4zGvmecdC8yyxFldZTEZjMoBhmFIvV6n3fb4oyu85BNT8QF32jUm9RYPecUiz7gnMDe+LTG1/ILxjQqn1ptc2AtYsz3aqcYUDIsnR3GESZxA8OYQptfh9jacX4GJJ3Blo0PNyqLfoRp4rJQb/PhQgUTRUwqt9ago3W6XIPSJolCaIpl0jBtOebXB/cky77/07ZLajFwJkd30YiIBK9j9Z5vcd8agZK5+EBIrsUdrDx3/FsFULpwSgDhP2RNw3/dHbFf619gujMjtZiurWDefzFlD55c5iwyHFXbDGH8BYhnpcUpuBnoAAAAASUVORK5CYII=')\",\n      \"backgroundSize\": \"cover\",\n      \"display\": \"block\"\n    }\n  })), \"\\n  \", mdx(\"img\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"alt\": \"Gradient_Descent\",\n    \"title\": \"Gradient_Descent\",\n    \"src\": \"/static/2d9c8a0aec77241f32679821b2fdd793/2bef9/W1_Gradient_Descent.png\",\n    \"srcSet\": [\"/static/2d9c8a0aec77241f32679821b2fdd793/6f3f2/W1_Gradient_Descent.png 256w\", \"/static/2d9c8a0aec77241f32679821b2fdd793/01e7c/W1_Gradient_Descent.png 512w\", \"/static/2d9c8a0aec77241f32679821b2fdd793/2bef9/W1_Gradient_Descent.png 1024w\", \"/static/2d9c8a0aec77241f32679821b2fdd793/71c1d/W1_Gradient_Descent.png 1536w\", \"/static/2d9c8a0aec77241f32679821b2fdd793/e67a8/W1_Gradient_Descent.png 1740w\"],\n    \"sizes\": \"(max-width: 1024px) 100vw, 1024px\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    },\n    \"loading\": \"lazy\"\n  })), \"\\n    \")), mdx(\"p\", null, \"\\uACBD\\uC0AC\\uD558\\uAC15\\uBC95\\uC740 \\uC704\\uC640 \\uAC19\\uC774 \\uC784\\uC758\\uC758 \\uC9C0\\uC810(\\uD30C\\uB77C\\uBBF8\\uD130 \\u03B8)\\uC73C\\uB85C\\uBD80\\uD130 \\uC2DC\\uC791\\uD574 \\uAC31\\uC2E0\\uD574\\uB098\\uAC00\\uBBC0\\uB85C, \\uC704\\uC758 \\uADF8\\uB9BC\\uACFC \\uAC19\\uC774 \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"\\uC9C0\\uC5ED\\uC801 \\uCD5C\\uC18C\\uAC12\"), \"\\uC5D0 \\uB3C4\\uB2EC\\uD558\\uAC8C \\uB41C\\uB2E4.\"), mdx(\"p\", null, \"\\uB2E8, \\uC120\\uD615\\uD68C\\uADC0\\uC5D0\\uC11C\\uC758 \\uBE44\\uC6A9\\uD568\\uC218\\uB294 \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"convex functino(\\uBCFC\\uB85D\\uD568\\uC218)\"), \"\\uC774 \\uB418\\uBBC0\\uB85C local optimum\\uC774 \\uC5C6\\uACE0, global optimum\\uB9CC \\uC874\\uC7AC\\uD55C\\uB2E4.\"), mdx(\"h3\", {\n    \"id\": \"algorithm\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h3\"\n  }, {\n    \"href\": \"#algorithm\",\n    \"aria-label\": \"algorithm permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"Algorithm\"), mdx(\"p\", null, mdx(\"span\", _extends({\n    parentName: \"p\"\n  }, {\n    \"className\": \"gatsby-resp-image-wrapper\",\n    \"style\": {\n      \"position\": \"relative\",\n      \"display\": \"block\",\n      \"marginLeft\": \"auto\",\n      \"marginRight\": \"auto\",\n      \"maxWidth\": \"546px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"24.609375%\",\n      \"position\": \"relative\",\n      \"bottom\": \"0\",\n      \"left\": \"0\",\n      \"backgroundImage\": \"url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAFCAYAAABFA8wzAAAACXBIWXMAABYlAAAWJQFJUiTwAAAAsklEQVQY04WQyQqEMBBE8/8fJZ4Ngh7EBUVE0ZsSXONW0s0EwizMgyKpVHfoRODFfd/4x3VdLJv3PtG2LZqmYbMsCxcURYFhGPjsOA5M08RrlmVY1xVd1yHPc/YE9ZPGcYSgwPM87PuOsiyhlILv+0jTFNu2scIwRJIkcByHL6NMSgnXdVFVFaIoQhAEqOsawkxGE9jM8/zh4zhG3/fsz/Pk55s6rTVL2H9Ae6Nv/hd2/gCf84CgFGUo/wAAAABJRU5ErkJggg==')\",\n      \"backgroundSize\": \"cover\",\n      \"display\": \"block\"\n    }\n  })), \"\\n  \", mdx(\"img\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"alt\": \"Gradient_Descent_Algorithm\",\n    \"title\": \"Gradient_Descent_Algorithm\",\n    \"src\": \"/static/03ae1b72ea0d821c5345ed42e0073476/76aed/W1_Gradient_Descent_Algorithm.png\",\n    \"srcSet\": [\"/static/03ae1b72ea0d821c5345ed42e0073476/6f3f2/W1_Gradient_Descent_Algorithm.png 256w\", \"/static/03ae1b72ea0d821c5345ed42e0073476/01e7c/W1_Gradient_Descent_Algorithm.png 512w\", \"/static/03ae1b72ea0d821c5345ed42e0073476/76aed/W1_Gradient_Descent_Algorithm.png 546w\"],\n    \"sizes\": \"(max-width: 546px) 100vw, 546px\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    },\n    \"loading\": \"lazy\"\n  })), \"\\n    \")), mdx(\"p\", null, \"\\uC774 \\uB54C, \", mdx(\"inlineCode\", {\n    parentName: \"p\"\n  }, \"\\u03B1\"), \"\\uB294 \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Learning Rate\"), \"\\uB97C, \\uADF8 \\uB4A4\\uB294 \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"\\uBBF8\\uBD84\\uACC4\\uC218(\\uD574\\uB2F9 \\uC810\\uC5D0\\uC11C\\uC758 tan\\uAC12)\"), \"\\uB97C \\uC758\\uBBF8\\uD55C\\uB2E4.\"), mdx(\"p\", null, mdx(\"inlineCode\", {\n    parentName: \"p\"\n  }, \"\\u03B1\"), \"\\uAC00 \\uB108\\uBB34 \\uC791\\uB2E4\\uBA74, Gradient Descent\\uB294 \\uB290\\uB9AC\\uAC8C \\uC9C4\\uD589\\uB41C\\uB2E4.\\n\\uBC18\\uB300\\uB85C \\uB108\\uBB34 \\uD06C\\uB2E4\\uBA74, minimum\\uC744 overshoot\\uD558\\uC5EC \\uC624\\uD788\\uB824 minimum\\uC5D0\\uC11C \\uC810\\uC810 \\uBA40\\uC5B4\\uC9C0\\uB294 \\uACB0\\uACFC\\uB97C \\uAC00\\uC838\\uC628\\uB2E4.\"), mdx(Warning, {\n    mdxType: \"Warning\"\n  }, \"\\uC5EC\\uB7EC\\uAC1C\\uC758 \\uD30C\\uB77C\\uBBF8\\uD130\\uB97C \\uAC31\\uC2E0\\uD560 \\uACBD\\uC6B0, \\uBC18\\uB4DC\\uC2DC \\uBAA8\\uB4E0 \\uD30C\\uB77C\\uBBF8\\uD130\\uB97C \\uD55C\\uBC88\\uC5D0 \\uBAB0\\uC544\\uC11C \\uAC31\\uC2E0\\uD574\\uC57C\\uD55C\\uB2E4. \\uC911\\uAC04\\uC911\\uAC04 \\uAC31\\uC2E0\\uD574\\uBC84\\uB9AC\\uBA74, Cost Function J\\uC758 \\uD30C\\uB77C\\uBBF8\\uD130\\uAC00 \\uB4A4\\uC11E\\uC774\\uAC8C \\uB41C\\uB2E4.\"), mdx(\"h3\", {\n    \"id\": \"batch-gradient-descentbgd\",\n    \"style\": {\n      \"position\": \"relative\"\n    }\n  }, mdx(\"a\", _extends({\n    parentName: \"h3\"\n  }, {\n    \"href\": \"#batch-gradient-descentbgd\",\n    \"aria-label\": \"batch gradient descentbgd permalink\",\n    \"className\": \"anchor-heading before\"\n  }), mdx(\"svg\", _extends({\n    parentName: \"a\"\n  }, {\n    \"aria-hidden\": \"true\",\n    \"focusable\": \"false\",\n    \"height\": \"16\",\n    \"version\": \"1.1\",\n    \"viewBox\": \"0 0 16 16\",\n    \"width\": \"16\"\n  }), mdx(\"path\", _extends({\n    parentName: \"svg\"\n  }, {\n    \"fillRule\": \"evenodd\",\n    \"d\": \"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n  })))), \"Batch Gradient Descent(BGD)\"), mdx(\"p\", null, \"Gradient Descent\\uC758 \\uB9E4 \\uB2E8\\uACC4\\uB97C batch\\uB2E8\\uC704\\uAC00 \\uC544\\uB2C8\\uB77C \\uC804\\uCCB4 \\uB370\\uC774\\uD130\\uC14B\\uC744 \\uB300\\uC0C1\\uC73C\\uB85C \\uC9C4\\uD589\\uD55C\\uB2E4.\", mdx(\"br\", {\n    parentName: \"p\"\n  }), \"\\n\", \"\\uC989, \\uAE30\\uC6B8\\uAE30\\uB97C \\uAD6C\\uD560 \\uB54C \\uD558\\uB098\\uC758 batch\\uC758 \\uC624\\uCC28\\uB97C \\uCE21\\uC815\\uD558\\uC9C0 \\uC54A\\uACE0, \\uBAA8\\uB4E0 \\uB370\\uC774\\uD130\\uC14B\\uC758 \\uC624\\uCC28\\uB97C \\uACC4\\uC0B0\\uD558\\uC5EC \\uD55C\\uBC88\\uB9CC \\uC5C5\\uB370\\uC774\\uD2B8\\uD55C\\uB2E4.\"), mdx(\"p\", null, \"\\uC774 \\uACBD\\uC6B0\\uB294 local minimum\\uC5D0 \\uBE60\\uC84C\\uC744 \\uB54C \\uBE60\\uC838\\uB098\\uC624\\uAE30 \\uD798\\uB4DC\\uBBC0\\uB85C, \\uB2E8 \\uD558\\uB098\\uC758 optima, global optima\\uB9CC \\uC874\\uC7AC\\uD558\\uB294 linear regression\\uC5D0\\uC11C \\uC0AC\\uC6A9\\uB41C\\uB2E4.\"));\n}\n;\nMDXContent.isMDXComponent = true;","excerpt":"Machine Learning Class by Andrew Ng, Stanford --Coursera Summary(20.12.07-21.03.01)","fields":{"slug":"/AI/Andrew_Ng/Machine-Learning/Week1/"},"frontmatter":{"title":"Machine Learning 수강 정리 - Week 1","date":"12/06/2020","tags":["ML","AI","Coursera","Andrew Ng","Stanford","Cost Function","Gradient Descent","BGD"]}}},"pageContext":{"slug":"/AI/Andrew_Ng/Machine-Learning/Week1/","next":{"fields":{"slug":"/Udemy/AWS/Developer_Associate/02_ELB_Fundamental/"},"frontmatter":{"title":"AWS Certified Developer Associate - ELB","tags":["Udemy","AWS","AWS Certified Developer Associate","ELB","SSL/TLS","SNI"],"date":"2020-12-08T00:00:00.000Z","excerpt":"Ultimate AWS Certified Developer Associate 2020 by Stephane Maarek","draft":null}},"prev":{"fields":{"slug":"/Udemy/AWS/Developer_Associate/01_IAM&EC2_Fundamental/"},"frontmatter":{"title":"AWS Certified Developer Associate - IAM & EC2","tags":["Udemy","AWS","AWS Certified Developer Associate","IAM","EC2","ENI","Security Groups"],"date":"2020-12-06T00:00:00.000Z","excerpt":"Ultimate AWS Certified Developer Associate 2020 by Stephane Maarek","draft":null}}}},"staticQueryHashes":["1703067421","3649515864","63159454"]}