update,
This commit is contained in:
louiscklaw
BIN
Man1130/task1/Man1130-python-comission/slides/Project.pdf
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BIN
Man1130/task1/Man1130-python-comission/slides/Project.pdf
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98
Man1130/task1/Man1130-python-comission/slides/academic.css
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Man1130/task1/Man1130-python-comission/slides/academic.css
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@@ -0,0 +1,98 @@
|
||||
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|
||||
|
||||
@import 'gaia';
|
||||
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|
||||
@import url('https://fonts.googleapis.com/css2?family=Source+Code+Pro&display=swap');
|
||||
|
||||
:root {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
section {
|
||||
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|
||||
font-family: 'Noto Sans JP', sans-serif;
|
||||
padding-top: 90px;
|
||||
padding-left: 40px;
|
||||
padding-right: 40px;
|
||||
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|
||||
|
||||
/* https://github.com/marp-team/marpit/issues/271 */
|
||||
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|
||||
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|
||||
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|
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|
||||
|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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BIN
Man1130/task1/Man1130-python-comission/slides/bg.jpg
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Man1130/task1/Man1130-python-comission/slides/bg.jpg
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Man1130/task1/Man1130-python-comission/slides/colors.css
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Man1130/task1/Man1130-python-comission/slides/colors.css
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Man1130/task1/Man1130-python-comission/slides/dracula.css
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Man1130/task1/Man1130-python-comission/slides/dracula.css
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|
||||
@charset "UTF-8";
|
||||
/*!
|
||||
* Marp Dracula theme.
|
||||
* @theme dracula
|
||||
* @author Daniel Nicolas Gisolfi
|
||||
*
|
||||
* @auto-scaling true
|
||||
* @size 4:3 960px 720px
|
||||
* @size 16:9 1280px 720px
|
||||
*/
|
||||
|
||||
@import url("https://fonts.googleapis.com/css?family=Lato:400,900|IBM+Plex+Sans:400,700");
|
||||
|
||||
:root {
|
||||
--dracula-background: #282a36;
|
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--dracula-current-line: #44475a;
|
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--dracula-foreground: #f8f8f2;
|
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--dracula-comment: #6272a4;
|
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--dracula-cyan: #8be9fd;
|
||||
--dracula-green: #50fa7b;
|
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--dracula-orange: #ffb86c;
|
||||
--dracula-pink: #ff79c6;
|
||||
--dracula-purple:#bd93f9;
|
||||
--dracula-red: #ff5555;
|
||||
--dracula-yellow: #f1fa8c;
|
||||
}
|
||||
|
||||
.hljs {
|
||||
display: block;
|
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overflow-x: auto;
|
||||
padding: 0.5em;
|
||||
background: var(--dracula-background);
|
||||
}
|
||||
|
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/* Dracula Foreground */
|
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.hljs,
|
||||
.hljs-subst,
|
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.hljs-typing,
|
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.hljs-variable,
|
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.hljs-template-variable {
|
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color: var(--dracula-foreground);
|
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|
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|
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/* Dracula Comment */
|
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.hljs-comment,
|
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.hljs-quote,
|
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|
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|
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|
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|
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|
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.hljs-meta .hljs-doctag,
|
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.hljs-built_in,
|
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.hljs-selector-tag,
|
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.hljs-section,
|
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.hljs-link,
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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.hljs-symbol,
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|
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|
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|
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color: var(--dracula-yellow);
|
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}
|
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.hljs-keyword,
|
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|
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|
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|
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|
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|
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h1,
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h2,
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h3,
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h4,
|
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h5,
|
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h6 {
|
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margin: 0.5em 0 0 0;
|
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color: var(--dracula-pink);
|
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|
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h1 strong,
|
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h2 strong,
|
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h3 strong,
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h4 strong,
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h5 strong,
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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p,
|
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|
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|
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|
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ul > li,
|
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ol > li {
|
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margin: 0.3em 0 0 0;
|
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color: var(--dracula-cyan);
|
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}
|
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ul > li > p,
|
||||
ol > li > p {
|
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margin: 0.6em 0 0 0;
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letter-spacing: 0;
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margin: -0.1em 0.15em;
|
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padding: 0.1em 0.2em;
|
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vertical-align: baseline;
|
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color: var(--dracula-green);
|
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|
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|
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pre {
|
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display: block;
|
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margin: 1em 0 0 0;
|
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min-height: 1em;
|
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overflow: visible;
|
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}
|
||||
pre code {
|
||||
box-sizing: border-box;
|
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margin: 0;
|
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min-width: 100%;
|
||||
padding: 0.5em;
|
||||
font-size: 0.7em;
|
||||
}
|
||||
pre code svg[data-marp-fitting=svg] {
|
||||
max-height: calc(580px - 1em);
|
||||
}
|
||||
|
||||
blockquote {
|
||||
margin: 1em 0 0 0;
|
||||
padding: 0 1em;
|
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position: relative;
|
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color: var(--dracula-orange);
|
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}
|
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blockquote::after, blockquote::before {
|
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content: "“";
|
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display: block;
|
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font-family: "Times New Roman", serif;
|
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font-weight: bold;
|
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position: absolute;
|
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color: var(--dracula-green);
|
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}
|
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blockquote::before {
|
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top: 0;
|
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left: 0;
|
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}
|
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blockquote::after {
|
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right: 0;
|
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|
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transform: rotate(180deg);
|
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}
|
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blockquote > *:first-child {
|
||||
margin-top: 0;
|
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}
|
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|
||||
mark {
|
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background: transparent;
|
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}
|
||||
|
||||
table {
|
||||
border-spacing: 0;
|
||||
border-collapse: collapse;
|
||||
margin: 1em 0 0 0;
|
||||
}
|
||||
table th,
|
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table td {
|
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padding: 0.2em 0.4em;
|
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border-width: 1px;
|
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border-style: solid;
|
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}
|
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|
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section {
|
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font-size: 35px;
|
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font-family: "IBM Plex Sans";
|
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line-height: 1.35;
|
||||
letter-spacing: 1.25px;
|
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padding: 70px;
|
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color: var(--dracula-foreground);
|
||||
background-color: var(--dracula-background);
|
||||
}
|
||||
section > *:first-child,
|
||||
section > header:first-child + * {
|
||||
margin-top: 0;
|
||||
}
|
||||
section a,
|
||||
section mark {
|
||||
color: var(--dracula-red);
|
||||
}
|
||||
section code {
|
||||
background: var(--dracula-current-line);
|
||||
color: var(--dracula-current-green);
|
||||
}
|
||||
section h1 strong,
|
||||
section h2 strong,
|
||||
section h3 strong,
|
||||
section h4 strong,
|
||||
section h5 strong,
|
||||
section h6 strong {
|
||||
color: var(--dracula-current-line);
|
||||
}
|
||||
section pre > code {
|
||||
background: var(--dracula-current-line);
|
||||
}
|
||||
section header,
|
||||
section footer,
|
||||
section section::after,
|
||||
section blockquote::before,
|
||||
section blockquote::after {
|
||||
color: var(--dracula-comment);
|
||||
}
|
||||
section table th,
|
||||
section table td {
|
||||
border-color: var(--dracula-current-line);
|
||||
}
|
||||
section table thead th {
|
||||
background: var(--dracula-current-line);
|
||||
color: var(--dracula-yellow);
|
||||
}
|
||||
section table tbody > tr:nth-child(even) td,
|
||||
section table tbody > tr:nth-child(even) th {
|
||||
background: var(--dracula-current-line);
|
||||
}
|
||||
|
||||
header,
|
||||
footer,
|
||||
section::after {
|
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box-sizing: border-box;
|
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font-size: 66%;
|
||||
height: 70px;
|
||||
line-height: 50px;
|
||||
overflow: hidden;
|
||||
padding: 10px 25px;
|
||||
position: absolute;
|
||||
color: var(--dracula-comment);
|
||||
}
|
||||
|
||||
header {
|
||||
left: 0;
|
||||
right: 0;
|
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top: 0;
|
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}
|
||||
|
||||
footer {
|
||||
left: 0;
|
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right: 0;
|
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bottom: 0;
|
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}
|
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|
||||
section::after {
|
||||
right: 0;
|
||||
bottom: 0;
|
||||
font-size: 80%;
|
||||
}
|
||||
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|
||||
npx @marp-team/marp-cli@latest -w slides.md
|
||||
|
||||
@REM -o slides.pdf
|
||||
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|
||||
---
|
||||
|
||||
marp: true
|
||||
title: Marp CLI example
|
||||
description: Hosting Marp slide deck on the web
|
||||
theme: uncover
|
||||
paginate: true
|
||||
_paginate: false
|
||||
|
||||
footer: '2022 project presentation'
|
||||
|
||||
style: |
|
||||
section {
|
||||
background-color: #ccc;
|
||||
}
|
||||
|
||||
footer {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
# <!--fit--> Marp CLI 例子
|
||||
|
||||
托管 Marp slide deck 在服务器上
|
||||
|
||||
https://github.com/yhatt/marp-cli-example
|
||||
|
||||
<style scoped>a { color: #eee; }</style>
|
||||
|
||||
<!-- 这是一个注释文件 This is presenter note. You can write down notes through HTML comment. -->
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
|
||||
##### <!--fit--> [Marp CLI](https://github.com/marp-team/marp-cli) + [GitHub Pages](https://github.com/pages) | [Netlify](https://www.netlify.com/) | [Vercel](https://vercel.com/)
|
||||
|
||||
##### <!--fit--> 👉 The easiest way to host<br />your Marp deck on the web
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
## **[GitHub Pages](https://github.com/pages)**
|
||||
|
||||
#### Ready to write & host your deck!
|
||||
|
||||
[](https://github.com/yhatt/marp-cli-example/generate)
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
## **[Netlify](https://www.netlify.com/)**
|
||||
|
||||
#### Ready to write & host your deck!
|
||||
|
||||
[](https://app.netlify.com/start/deploy?repository=https://github.com/yhatt/marp-cli-example)
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
## **[Vercel](https://vercel.com/)**
|
||||
|
||||
#### Ready to write & host your deck!
|
||||
|
||||
[](https://vercel.com/import/project?template=https://github.com/yhatt/marp-cli-example)
|
||||
|
||||
---
|
||||
|
||||
### <!--fit--> :ok_hand:
|
||||
|
||||
---
|
||||
|
||||
|
||||
|
||||
### Created by Yuki Hattori ([@yhatt](https://github.com/yhatt))
|
||||
|
||||
https://github.com/yhatt/marp-cli-example
|
||||
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|
||||
---
|
||||
|
||||
marp: true
|
||||
title: Marp CLI example
|
||||
description: Hosting Marp slide deck on the web
|
||||
theme: uncover
|
||||
paginate: true
|
||||
_paginate: false
|
||||
backgroundImage: url('https://www.google.com/url?sa=i&url=https%3A%2F%2Fcommons.wikimedia.org%2Fwiki%2FFile%3AHelloWorld.svg&psig=AOvVaw0d3lmyaMphPi0ANeGIEJOw&ust=1670049479380000&source=images&cd=vfe&ved=0CBAQjRxqFwoTCJjxx6Gp2vsCFQAAAAAdAAAAABAE')
|
||||
|
||||
footer: '2022 project presentation'
|
||||
|
||||
style: |
|
||||
section {
|
||||
background-color: #ccc;
|
||||
padding: 0 10vw;
|
||||
}
|
||||
|
||||
footer {
|
||||
text-align: center;
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
<!-- lead-invert-red -->
|
||||
|
||||
### **slide topic?**
|
||||
|
||||
##### overview of the dataset
|
||||
- Male and Female proportion
|
||||
- male by age
|
||||
- female by age
|
||||
- The types of chest pain experienced among the patients
|
||||
- pie chart
|
||||
- showing the correlation between
|
||||
chest pain and target Confusion Matrix ?
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
<!-- lead-invert-red -->
|
||||
|
||||
### **Decision Tree**
|
||||
|
||||
##### overview of the dataset
|
||||
- Decision Tree 果張圖
|
||||
- Performance Analysis
|
||||
- confusion matrix
|
||||
- ROC
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
<!-- lead-invert-red -->
|
||||
|
||||
### **Naive Bayes**
|
||||
|
||||
##### overview of the dataset
|
||||
- Naive Bayes 果張圖
|
||||
- Performance Analysis
|
||||
- confusion matrix
|
||||
- ROC
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
<!-- lead-invert-red -->
|
||||
|
||||
### **Logistic Regression**
|
||||
|
||||
##### overview of the dataset
|
||||
- Logistic Regression 果張圖
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
### **Performance Analysis**
|
||||
- Performance Analysis
|
||||
- confusion matrix
|
||||
- ROC
|
||||
|
||||
<!-- HTML comment recognizes as a presenter note per pages. -->
|
||||
<!-- You may place multiple comments in a single page. -->
|
||||
<!--
|
||||
Also supports multiline.
|
||||
We bet these comments would help your presentation...
|
||||
-->
|
||||
|
||||
---
|
||||
|
||||
### **Performance Analysis con't**
|
||||
- pick a sample, bayes modeling:
|
||||
- naive_bayes_scramble.ipynb
|
||||
- column selection
|
||||
- compare column vs accurancy
|
||||
|
||||
- the performance/accurancy of fewer column MAY BE better than more column
|
||||
- possible cause
|
||||
- column noise/ input data accurancy ?
|
||||
- modal overfitting ?
|
||||
- extreme case ?
|
||||
|
||||
<!-- - Q: Evaluation -->
|
||||
|
||||
---
|
||||
|
||||
### **Performance Analysis con't**
|
||||
- how to improve ?
|
||||
- the choice of the columns may be better if other facuity involved.
|
||||
- more labelled data improves accuracy
|
||||
|
||||
---
|
||||
|
||||
### **Performance Analysis con't**
|
||||
- disclaimer ?
|
||||
- no model can introduce 100% accurancy
|
||||
- why ?
|
||||
- extreme case
|
||||
- chaos theory ?
|
||||
- will never take all ~~ervery~~ matters into account
|
||||
|
||||
- however, the model can be considered if accuracy above nn% in general
|
||||
|
||||
<!-- Q:What’s the problem you are trying to solve -->
|
||||
|
||||
|
||||
<!-- - How’s your program solve the problem -->
|
||||
|
||||
|
||||
<!-- - Demonstrate how your program work/ Go through the data analysis -->
|
||||
<!-- - What are the interesting libraries that you used? -->
|
||||
<!-- - Any complex logic in your project that you want to show off -->
|
||||
<!-- - Any other interesting thing -->
|
||||
<!-- - It’s not a must for you to show your code in presentation. -->
|
||||
<!-- - Do that if you think that help you to explain things -->
|
||||
<!-- - We don’t need everyone to speak. The whole group share the same score. -->
|
||||
<!-- - Evaluation -->
|
||||
<!-- - Good presentation flow, Clarity of your points, Pace of your presentation -->
|
||||
---
|
||||
|
||||
### **why these method ?**
|
||||
- [x]unsupervised modeling
|
||||
- cross out reason
|
||||
- data is already labelled
|
||||
- data is small amount and discrete
|
||||
|
||||
- [x] Knn
|
||||
- [x] Kmeans
|
||||
|
||||
<!-- ref: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning -->
|
||||
|
||||
---
|
||||
|
||||
### **why these method ?**
|
||||
- supervised modeling / supervised learning
|
||||
- data is already labelled
|
||||
- []Decision Tree
|
||||
- []Naive Bayes
|
||||
- performance ? accuracy ? ROI ?
|
||||
|
||||
- [x]multi dimensional/column difficult to understand/maintain
|
||||
- [x]Logistic Regression
|
||||
- [x]SVM
|
||||
|
||||
<!-- ref: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning -->
|
||||
|
||||
---
|
||||
|
||||
### notes only
|
||||
|
||||
<!-- Q: What’s the problem you are trying to solve -->
|
||||
<!-- Q: How’s your program solve the problem -->
|
||||
<!-- A: to predict humans heart disease possiblilies by modelling -->
|
||||
|
||||
<!-- Q: Demonstrate how your program work/ Go through the data analysis -->
|
||||
<!-- 可能你要捉一捉佢路, if i were you, i won't go through the data analysis, -->
|
||||
<!-- because the sample steps every one can google e.g. data cleaning, training model, performance analysis, not the main point -->
|
||||
|
||||
<!-- Q: What are the interesting libraries that you used? -->
|
||||
<!-- Q: Any complex logic in your project that you want to show off -->
|
||||
<!-- Q: Any other interesting thing -->
|
||||
<!-- bayes scrambling can help -->
|
||||
|
||||
|
||||
<!-- Q: Evaluation -->
|
||||
<!-- Performance Analysis ? -->
|
||||
|
||||
<!-- X -->
|
||||
<!-- Q: It’s not a must for you to show your code in presentation. -->
|
||||
<!-- - ? Do that if you think that help you to explain things -->
|
||||
|
||||
<!-- Q: We don’t need everyone to speak. The whole group share the same score. -->
|
||||
|
||||
<!-- Q: Good presentation flow, Clarity of your points, Pace of your presentation -->
|
||||
BIN
Man1130/task1/Man1130-python-comission/slides/slides.pdf
Normal file
BIN
Man1130/task1/Man1130-python-comission/slides/slides.pdf
Normal file
Binary file not shown.
Reference in New Issue
Block a user