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.icon-hamburger-1 svg.icon-hamburger { display: block; } .icon-hamburger-1 .fill-color.icon-hamburger { fill: #fff; }.login-button-1 .dropdown-toggle.login-button { display: block; height: 4rem; font-size: 1.6rem; text-transform: uppercase; text-decoration: none; color: #ccc; cursor: pointer; } .login-button-1 .dropdown-toggle.login-button .fill-color.login-button { fill: #999; } .login-button-1 .dropdown-toggle.login-button:active .fill-color.login-button,.login-button-1 .dropdown-toggle.login-button:focus .fill-color.login-button,.login-button-1 .dropdown-toggle.login-button:hover .fill-color.login-button { fill: #fff; } .login-button-1 .active.login-button { border-radius: 1rem 1rem 0 0; background: #333; } .login-button-1 .active.login-button .fill-color.login-button { fill: #fff; } .login-button-1 span.login-button { display: none; font-size: 1.4rem; text-transform: uppercase; color: #999; } .login-button-1 span.login-button a.login-button { color: inherit; text-decoration: none; } .login-button-1 a.login-button:hover,.login-button-1 a.login-button:active,.login-button-1 a.login-button:focus { color: #fff; } @media (min-width: 890px) { .login-button-1 .logged-out-toolbar.login-button { transform: translateY(-.5rem); } .login-button-1 .active.login-button { background: transparent; } .login-button-1 .dropdown-toggle.login-button { display: inline-block; vertical-align: middle; } .login-button-1 span.login-button { display: inline; vertical-align: middle; } }.nav-search-1 input.nav-search:focus { outline: none; } .nav-search-1 button.nav-search { background: none; color: inherit; border: none; font: inherit; cursor: pointer; } .nav-search-1 button.nav-search:focus { outline: none; } .nav-search-1 .search.nav-search { padding-top: 0; margin-right: .5rem; } .nav-search-1 .search.nav-search svg.nav-search { position: relative; top: -5px; right: -3px; fill:; } .nav-search-1 .search-activated.nav-search { display: -webkit-box; display: -ms-flexbox; display: flex; position: absolute; top: 0; right: 5rem; bottom: 0; left: 5rem; z-index: 3; padding: 0.5rem 0.2rem; border-radius: 1rem 1rem 0 0; background: #333; } .nav-search-1 .search-inactive.nav-search { display: none; } .nav-search-1 .search-activated.nav-search .highlight.nav-search,.nav-search-1 .search-activated.nav-search .search.nav-search { background: #fff; border-radius: 0.5rem; } .nav-search-1 .search-activated.nav-search .highlight.nav-search { display: -webkit-box; display: -ms-flexbox; display: flex; width: 100%; margin: 0 .5rem; } .nav-search-1 .search-activated.nav-search .search.nav-search { height: 100%; padding: 0; margin-right: 0; -ms-flex-item-align: center; -ms-grid-row-align: center; align-self: center; } .nav-search-1 .search-activated.nav-search .search-field.nav-search { width: 100%; height: 100%; box-sizing: border-box; padding-left: 1rem; border-radius: 0.5rem; border: none; font-size: 1.6rem; text-align: center; } .nav-search-1 .search-activated.nav-search .search-field.nav-search:focus { outline: none; } @keyframes fade-in-nav-search-1 { 0% { opacity: 0; } 100% { opacity: 1; } } .nav-search-1 .fade-in.nav-search { animation: fade-in-nav-search-1 .2s forwards; } @media (min-width: 890px) { .nav-search-1 .search.nav-search svg.nav-search { display: inline; width: 28px; height: 28px; vertical-align: -14px; } .nav-search-1 .search.nav-search path.nav-search { fill: #333; } .nav-search-1 .search-inactive.nav-search,.nav-search-1 .search-activated.nav-search { display: block; position: static; padding: 1.2rem .2rem; background: transparent; } .nav-search-1 .search-activated.nav-search .highlight.nav-search { width: 13rem; height: 2.8rem; -webkit-box-orient: horizontal; -webkit-box-direction: reverse; -ms-flex-direction: row-reverse; flex-direction: row-reverse; } .nav-search-1 .search-activated.nav-search .search-field.nav-search { width: calc(100% - 28px); height: 100%; padding-left: 0; font-size: 1.4rem; text-align: left; } .nav-search-1 .search-activated.nav-search .search.nav-search svg.nav-search { width: 28px; height: 28px; } }.media-button-1 a.media-button { display: inline-block; text-decoration: none; } .media-button-1 .menu-item.media-button { display: inline-flex; width: 100%; padding: 0; font-size: 1.6rem; text-align: left; background: transparent; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } .media-button-1 .menu-item.media-button:focus { outline: none; } .media-button-1 .label.media-button { display: inline-block; padding: 0; font-weight: 400; color: #fff; text-align: left; vertical-align: middle; } .media-button-1 .menu-item.media-button > .icon.media-button { display: inline-flex; width: 42px; height: 42px; vertical-align: middle; -webkit-box-align: center; -ms-flex-align: center; align-items: center; -webkit-box-pack: center; -ms-flex-pack: center; justify-content: center; } .media-button-1 .menu-item.selected.media-button .icon.media-button { background-color: #333; border-radius: 1rem 0 0 1rem; } .media-button-1 .icon.media-button .fill-color.media-button { fill: #999; } .media-button-1 .icon.active.media-button .fill-color.media-button { fill: #fff; } .media-button-1 .donate.media-button .fill-color.media-button { fill: #f00; } @media (min-width: 890px) { .media-button-1 .menu-item.media-button { width: auto; height: 5rem; color: #999; } .media-button-1 .menu-item.media-button:hover,.media-button-1 .menu-item.media-button:active,.media-button-1 .menu-item.media-button:focus { color: #fff; } .media-button-1 .menu-item.media-button:hover .fill-color.media-button,.media-button-1 .menu-item.media-button:active .fill-color.media-button,.media-button-1 .menu-item.media-button:focus .fill-color.media-button { fill: #fff; } .media-button-1 .label.media-button { display: none; } .media-button-1 .label.media-button,.media-button-1 .web.media-button:after { padding-right: 1rem; font-size: 1.3rem; text-transform: uppercase; color: inherit; } .media-button-1 .web.media-button:after { display: none; content: "web"; } .media-button-1 .donate.media-button,.media-button-1 .more.media-button { display: none; } .media-button-1 .menu-item.selected.media-button { background: #474747; } .media-button-1 .menu-item.selected.media-button .label.media-button,.media-button-1 .menu-item.selected.web.media-button:after { color: #fff; } .media-button-1 .menu-item.selected.media-button .icon.media-button { background: transparent; } .media-button-1 .web.selected.media-button .fill-color.media-button { fill: #ffcd27; } .media-button-1 .texts.selected.media-button .fill-color.media-button { fill: #faab3c; } .media-button-1 .video.selected.media-button .fill-color.media-button { fill: #f1644b; } .media-button-1 .audio.selected.media-button .fill-color.media-button { fill: #00adef; } .media-button-1 .software.selected.media-button .fill-color.media-button { fill: #9ecc4f; } .media-button-1 .images.selected.media-button .fill-color.media-button { fill: #aa99c9; } } @media (min-width: 1300px) { .media-button-1 .label.media-button,.media-button-1 .web.media-button:after { display: inline; } .media-button-1 .web.media-button .label.media-button { display: none; } }.media-menu-1 { outline: none; } .media-menu-1 .media-menu.media-menu { position: absolute; z-index: -1; top: -100vh; width: 100%; background-color: #222; margin: 0; overflow: hidden; } .media-menu-1 .media-menu.tx-slide.media-menu { transition-property: top; transition-duration: 0.2s; transition-timing-function: ease; } .media-menu-1 .media-menu.tx-slide.open.media-menu { top: 100%; } .media-menu-1 .media-menu.tx-slide.closed.media-menu { top: -100vh; } .media-menu-1 .media-menu.tx-slide.closed.media-menu { transition-duration: 0.2s; } .media-menu-1 .menu-group.media-menu { position: relative; line-height: normal; } @media (min-width: 890px) { .media-menu-1 .media-menu.media-menu { display: inline-block; position: static; width: auto; height: 5rem; } .media-menu-1 .media-menu.tx-slide.media-menu { transition-property: none; } .media-menu-1 .media-menu.tx-slide.open.media-menu,.media-menu-1 .media-menu.tx-slide.closed.media-menu { top: 0; } .media-menu-1 .menu-group.media-menu { font-size: 0; } }.primary-nav-1 button.primary-nav:focus,.primary-nav-1 a.primary-nav:focus,.primary-nav-1 input.primary-nav:focus { outline: none; } .primary-nav-1 nav.primary-nav { position: relative; display: -ms-grid; display: grid; height: 4rem; grid-template-areas: "hamburger empty search user"; -ms-grid-columns: 4rem minmax(1rem, 100%) 4.3rem 5rem; grid-template-columns: 4rem auto 4.3rem 5rem; -ms-grid-rows: 100%; grid-template-rows: 100%; background: #222; border-bottom: 1px solid #333; } .primary-nav-1 button.primary-nav { background: none; color: inherit; border: none; font: inherit; cursor: pointer; } .primary-nav-1 .link-home.primary-nav { position: absolute; top: 50%; left: 50%; z-index: 2; text-decoration: none; -webkit-transform: translate(-50%, -50%); -ms-transform: translate(-50%, -50%); transform: translate(-50%, -50%); } .primary-nav-1 media-menu.primary-nav { grid-column-start: hamburger-start; grid-column-end: user-end; } .primary-nav-1 .ia-logo.primary-nav { display: block; } .primary-nav-1 .ia-wordmark.primary-nav { display: none; } .primary-nav-1 .hamburger.primary-nav { -ms-grid-row: 1; -ms-grid-column: 1; grid-area: hamburger; padding: 0; } .primary-nav-1 .search-trigger.primary-nav { -ms-grid-row: 1; -ms-grid-column: 3; grid-area: search; position: relative; padding: 0; z-index: 1; width: 100%; text-align: right; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } .primary-nav-1 .search-trigger.primary-nav .fill-color.primary-nav { fill: #999; } .primary-nav-1 .search-activated.primary-nav { position: relative; z-index: 3; } .primary-nav-1 .upload.primary-nav { display: none; } .primary-nav-1 .upload.primary-nav span.primary-nav { display: none; } .primary-nav-1 .user-info.primary-nav { -ms-grid-row: 1; -ms-grid-column: 4; grid-area: user; -ms-grid-row-align: stretch; align-self: stretch; -ms-grid-column-align: end; justify-self: end; } .primary-nav-1 .username.primary-nav { display: none; font-size: 1.3rem; vertical-align: middle; text-transform: uppercase; } .primary-nav-1 .user-menu.primary-nav { height: 100%; padding: .5rem 1rem; color: #999; } .primary-nav-1 .user-menu.primary-nav:hover { color: #fff; } .primary-nav-1 .user-menu.active.primary-nav { border-radius: 1rem 1rem 0 0; background: #333; } .primary-nav-1 .user-menu.primary-nav img.primary-nav { display: block; width: 30px; height: 30px; } @media (min-width: 890px) { .primary-nav-1 { ; } .primary-nav-1 nav.primary-nav { display: block; z-index: 2; height: 5rem; padding-right: 1.5rem; } .primary-nav-1 .link-home.primary-nav { position: static; float: left; margin-top: 1rem; padding: 0 10px 0 13px; -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); transform: translate(0, 0); } .primary-nav-1 .ia-logo.primary-nav,.primary-nav-1 .ia-wordmark.primary-nav { display: inline-block; vertical-align: middle; } .primary-nav-1 .ia-wordmark.primary-nav { margin-left: 1rem; } .primary-nav-1 .hamburger.primary-nav,.primary-nav-1 .search-trigger.primary-nav { display: none; } .primary-nav-1 .user-info.primary-nav { float: right; padding-top: 1rem; } .primary-nav-1 .user-menu.primary-nav { padding-top: 0; } .primary-nav-1 .user-menu.active.primary-nav { background: transparent; } .primary-nav-1 .user-menu.primary-nav img.primary-nav { display: inline-block; vertical-align: middle; } .primary-nav-1 .upload.primary-nav { display: block; float: right; margin-top: 1rem; font-size: 1.4rem; text-transform: uppercase; text-decoration: none; color: #999; } .primary-nav-1 .upload.primary-nav:active,.primary-nav-1 .upload.primary-nav:focus,.primary-nav-1 .upload.primary-nav:hover { color: #fff; } .primary-nav-1 .upload.primary-nav svg.primary-nav { width: 32px; height: 32px; vertical-align: middle; fill: #999; } .primary-nav-1 .upload.primary-nav:hover svg.primary-nav,.primary-nav-1 .upload.primary-nav:focus svg.primary-nav,.primary-nav-1 .upload.primary-nav:active svg.primary-nav { fill: #fff; } .primary-nav-1 nav-search.primary-nav { float: right; margin-left: 1rem; } .primary-nav-1 login-button.primary-nav { display: block; margin-right: 1rem; } } @media (min-width: 990px) { .primary-nav-1 .username.primary-nav { display: inline-block; } .primary-nav-1 .upload.primary-nav span.primary-nav { display: inline; } }.search-menu-1 { ; } .search-menu-1 button.search-menu:focus,.search-menu-1 input.search-menu:focus { outline-color: #428bca; outline-width: 0.16rem; outline-style: auto; } .search-menu-1 .search-menu.search-menu { position: absolute; top: -800px; right: 0; left: 0; z-index: 1; padding: 0 4.5rem; font-size: 1.6rem; background-color: #333; } .search-menu-1 .tx-slide.search-menu { overflow: hidden; transition-property: top; transition-duration: 0.2s; transition-timing-function: ease; } .search-menu-1 .initial.search-menu,.search-menu-1 .closed.search-menu { top: -800px; } .search-menu-1 .closed.search-menu { transition-duration: 0.2s; } .search-menu-1 .open.search-menu { top: 4rem; } .search-menu-1 label.search-menu,.search-menu-1 a.search-menu { padding: 1rem; display: block; } .search-menu-1 .advanced-search.search-menu { text-decoration: none; color: #428bca; } @media (min-width: 890px) { .search-menu-1 .search-menu.search-menu { overflow: visible; top: -400px; right: 2rem; left: auto; z-index: 5; padding: 1rem 2rem; transition: opacity .2s ease-in-out; font-size: 1.4rem; color: #333; border-radius: 2px; background: #fff; box-shadow: 0 1px 2px 1px rgba(0, 0, 0, .15); } .search-menu-1 .search-menu.search-menu:after { position: absolute; right: 7px; top: -7px; width: 12px; height: 7px; box-sizing: border-box; color: #fff; content: ""; border-bottom: 7px solid currentColor; border-left: 6px solid transparent; border-right: 6px solid transparent; } .search-menu-1 .initial.search-menu,.search-menu-1 .closed.search-menu { opacity: 0; transition-duration: .2s; } .search-menu-1 .open.search-menu { top: 5.1rem; opacity: 1; } .search-menu-1 label.search-menu { padding: 0; } .search-menu-1 label.search-menu + label.search-menu { padding-top: 7px; } .search-menu-1 a.search-menu { padding: 1rem 0 0 0; } }.media-subnav-1 a.media-subnav { text-decoration: none; color: #fff; } .media-subnav-1 img.media-subnav { display: block; width: 90px; height: 90px; margin: 0 auto 1rem auto; border-radius: 45px; } .media-subnav-1 h3.media-subnav { margin-top: 0; font-size: 1.8rem; } .media-subnav-1 h4.media-subnav { font-size: 1.6rem; } .media-subnav-1 ul.media-subnav { padding: 0; margin: 0; list-style: none; } .media-subnav-1 li.media-subnav + li.media-subnav { padding-top: 1.5rem; } .media-subnav-1 .icon-links.media-subnav { display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-pack: space-evenly; -ms-flex-pack: space-evenly; justify-content: space-evenly; text-align: center; } .media-subnav-1 .icon-links.media-subnav a.media-subnav { display: inline-block; width: 120px; margin-bottom: 1.5rem; overflow: hidden; white-space: nowrap; text-align: center; text-overflow: ellipsis; } .media-subnav-1 .icon-links.media-subnav a.media-subnav + a.media-subnav { margin-left: 2rem; } .media-subnav-1 .featured.media-subnav h4.media-subnav { display: none; } @media (min-width: 890px) { .media-subnav-1 { display: -ms-grid; display: grid; -ms-grid-columns: 40% 20% 40%; grid-template-columns: 40% 20% 40%; } .media-subnav-1 .wayback-search.media-subnav { -ms-grid-column: 1; -ms-grid-column-span: 3; grid-column: 1 / 4; } .media-subnav-1 h3.media-subnav { display: none; } .media-subnav-1 h4.media-subnav { margin: 0 0 1rem 0; font-weight: 100; } .media-subnav-1 ul.media-subnav { font-size: 1.3rem; } .media-subnav-1 li.media-subnav { padding-bottom: .5rem; } .media-subnav-1 li.media-subnav + li.media-subnav { padding-top: 0; } .media-subnav-1 li.media-subnav a.media-subnav { display: block; overflow: hidden; white-space: nowrap; text-overflow: ellipsis; } .media-subnav-1 .icon-links.media-subnav { -ms-grid-column: 1; } .media-subnav-1 .icon-links.media-subnav a.media-subnav { padding-top: 3.5rem; max-width: 160px; } .media-subnav-1 .links.media-subnav { padding: 0 1.5rem; } .media-subnav-1 .featured.media-subnav { -ms-grid-column: 2; } .media-subnav-1 .featured.media-subnav h4.media-subnav { display: block; } .media-subnav-1 .top.media-subnav { -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav { display: -ms-grid; display: grid; -ms-grid-columns: 50% 3rem 50%; grid-template-columns: 50% 50%; -ms-grid-rows: (auto)[7]; grid-template-rows: repeat(7, auto); grid-column-gap: 3rem; grid-auto-flow: column; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(1) { -ms-grid-row: 1; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(2) { -ms-grid-row: 2; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(3) { -ms-grid-row: 3; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(4) { -ms-grid-row: 4; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(5) { -ms-grid-row: 5; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(6) { -ms-grid-row: 6; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(7) { -ms-grid-row: 7; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(8) { -ms-grid-row: 1; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(9) { -ms-grid-row: 2; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(10) { -ms-grid-row: 3; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(11) { -ms-grid-row: 4; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(12) { -ms-grid-row: 5; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(13) { -ms-grid-row: 6; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(14) { -ms-grid-row: 7; -ms-grid-column: 3; } }.media-slider-1 .overflow-clip.media-slider { display: none; position: absolute; top: 4rem; right: 0; left: 4rem; height: 368px; overflow-x: hidden; } .media-slider-1 .information-menu.media-slider { position: absolute; top: 0; right: 0; left: 0; padding: 0; height: 368px; overflow-x: hidden; font-size: 1.4rem; background: #333; } .media-slider-1 .open.media-slider { display: block; } .media-slider-1 .info-box.media-slider { padding: 1rem; } @media (min-width: 890px) { .media-slider-1 .overflow-clip.media-slider { display: block; top: 0; left: 0; height: auto; overflow-x: visible; transform: translate(0, -100%); transition: transform .2s ease; } .media-slider-1 .information-menu.media-slider { left: 0; z-index: 1; height: auto; min-height: 21rem; background: #474747; transform: translate(0, -100%); transition: transform .2s ease; } .media-slider-1 .overflow-clip.open.media-slider { transform: translate(0, 8rem); } .media-slider-1 .information-menu.open.media-slider { transform: translate(0, 0); } .media-slider-1 .info-box.media-slider { max-width: 1000px; padding: 1.5rem 0; margin: 0 auto; } }.desktop-subnav-1 ul.desktop-subnav { position: relative; z-index: 3; padding: .8rem 0; margin: 0; font-size: 1.2rem; text-transform: uppercase; text-align: center; background: #333; } .desktop-subnav-1 li.desktop-subnav { display: inline-block; padding: 0 15px; } .desktop-subnav-1 a.desktop-subnav { text-decoration: none; color: #aaa; } .desktop-subnav-1 a.desktop-subnav:hover,.desktop-subnav-1 a.desktop-subnav:active,.desktop-subnav-1 a.desktop-subnav:focus { color: #fff; } .desktop-subnav-1 .donate.desktop-subnav svg.desktop-subnav { width: 16px; height: 16px; vertical-align: -4px; fill: #f00; }.signed-out-dropdown-1 { ; } .signed-out-dropdown-1 nav.signed-out-dropdown { position: absolute; top: -1500px; right: 0; z-index: 1; overflow: hidden; font-size: 1.6rem; background-color: #333; transition-property: top; transition-duration: 0.2s; transition-timing-function: ease; } .signed-out-dropdown-1 .initial.signed-out-dropdown,.signed-out-dropdown-1 .closed.signed-out-dropdown { top: -1500px; } .signed-out-dropdown-1 .closed.signed-out-dropdown { transition-duration: 0.5s; } .signed-out-dropdown-1 .open.signed-out-dropdown { top: 4rem; max-width: 100vw; overflow: auto; } .signed-out-dropdown-1 h3.signed-out-dropdown { padding: 0.6rem 2rem; margin: 0; font-size: inherit; overflow: hidden; text-overflow: ellipsis; } .signed-out-dropdown-1 ul.signed-out-dropdown { padding: 0.4rem 0 0.7rem 0; margin: 0; list-style: none; max-height: calc(100vh - 7.2rem + 1px); overflow: auto; box-sizing: border-box; } .signed-out-dropdown-1 .divider.signed-out-dropdown { margin: .5rem 0; border-bottom: 1px solid #666; } .signed-out-dropdown-1 a.signed-out-dropdown,.signed-out-dropdown-1 .info-item.signed-out-dropdown { display: block; color: #fff; text-decoration: none; padding: 1rem 2rem; } .signed-out-dropdown-1 .info-item.signed-out-dropdown { font-size: .8em; color: #999; } @media (min-width: 890px) { .signed-out-dropdown-1 nav.signed-out-dropdown { overflow: visible; top: calc(100% + 7px); left: auto; z-index: 5; transition: opacity .2s ease-in-out; font-size: 1.4rem; border-radius: 2px; background: #fff; box-shadow: 0 1px 2px 1px rgba(0, 0, 0, .15); } .signed-out-dropdown-1 nav.signed-out-dropdown:after { position: absolute; right: 7px; top: -7px; width: 12px; height: 7px; box-sizing: border-box; color: #fff; content: ""; border-bottom: 7px solid currentColor; border-left: 6px solid transparent; border-right: 6px solid transparent; } .signed-out-dropdown-1 h3.signed-out-dropdown { display: none; } .signed-out-dropdown-1 ul.signed-out-dropdown { max-height: calc(100vh - 8.5rem + 1px); } .signed-out-dropdown-1 .divider.signed-out-dropdown { border-bottom-color: #666; } .signed-out-dropdown-1 a.signed-out-dropdown { padding: .5rem 2rem; color: #333; transition: background .1s ease-out, color .1s ease-out; } .signed-out-dropdown-1 .info-item.signed-out-dropdown { padding: .5rem 2rem; font-size: .8em; } .signed-out-dropdown-1 a.signed-out-dropdown:hover,.signed-out-dropdown-1 a.signed-out-dropdown:active,.signed-out-dropdown-1 a.signed-out-dropdown:focus { color: #fff; 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[Coursera] Coding the Matrix: Linear Algebra through Computer Science Applications

by: Philip Klein (Brown University)

Publication date: 2015

Publisher: Academic Torrents

Contributor: Academic Torrents

When you take a digital photo with your phone or transform the image in Photoshop, when you play a video game or watch a movie with digital effects, when you do a web search or make a phone call, you are using technologies that build upon linear algebra. Linear algebra provides concepts that are crucial to many areas of computer science, including graphics, image processing, cryptography, machine learning, computer vision, optimization, graph algorithms, quantum computation, computational biology, information retrieval and web search. Linear algebra in turn is built on two basic elements, the matrix and the vector.

In this class, you will learn the concepts and methods of linear algebra, and how to use them to think about problems arising in computer science. You will write small programs in the programming language Python to implement basic matrix and vector functionality and algorithms, and use these to process real-world data to achieve such tasks as: two-dimensional graphics transformations, face morphing, face detection, image transformations such as blurring and edge detection, image perspective removal, audio and image compression, searching within an image or an audio clip, classification of tumors as malignant or benign, integer factorization, error-correcting codes, secret-sharing, network layout, document classification, and computing Pagerank (Google's ranking method).

Academictorrents_collection: video-lectures

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External-identifier: urn:academictorrents:54cd86f3038dfd446b037891406ba4e0b1200d5a

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Source: http://academictorrents.com/details/54cd86f3038dfd446b037891406ba4e0b1200d5a
torrent:urn:sha1:54cd86f3038dfd446b037891406ba4e0b1200d5a

Year: 2015

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ITEM TILE

MPEG4

8.8M

Coding the Matrix Linear Algebra through Computer Science Applications 8.0 How to submit assignments.mp4

124.0M

Coding the Matrix Linear Algebra through Computer Science Applications 0.0 Course Introduction Part 1 (953).mp4

86.6M

Coding the Matrix Linear Algebra through Computer Science Applications 0.1 Course Introduction Part 2 (849).mp4

141.4M

Coding the Matrix Linear Algebra through Computer Science Applications 0.2 The Function The function and other preliminaries (2055).mp4

34.9M

Coding the Matrix Linear Algebra through Computer Science Applications 0.3 The Field Introduction to complex numbers (552).mp4

87.9M

Coding the Matrix Linear Algebra through Computer Science Applications 0.4 The Field Playing with C (1519).mp4

68.1M

Coding the Matrix Linear Algebra through Computer Science Applications 0.5 The Field Playing with GF(2) (1028).mp4

49.0M

Coding the Matrix Linear Algebra through Computer Science Applications 1.0 The Vector What is a vector (820).mp4

60.6M

Coding the Matrix Linear Algebra through Computer Science Applications 1.1 The Vector Vector addition and scalar-vector multiplication (1016).mp4

55.0M

Coding the Matrix Linear Algebra through Computer Science Applications 1.2 The Vector Dictionary-based representations of vectors (910).mp4

54.1M

Coding the Matrix Linear Algebra through Computer Science Applications 1.3 The Vector Vectors over GF(2) (918).mp4

51.6M

Coding the Matrix Linear Algebra through Computer Science Applications 1.4 The Vector Dot-product (849).mp4

12.9M

Coding the Matrix Linear Algebra through Computer Science Applications 1.5 The Vector Dot-product of vectors over GF(2) (444).mp4

9.6M

Coding the Matrix Linear Algebra through Computer Science Applications 1.6 The Vector Solving a triangular system of linear equations (400).mp4

28.0M

Coding the Matrix Linear Algebra through Computer Science Applications 2.0 The Vector Space Linear combinations.mp4

21.5M

Coding the Matrix Linear Algebra through Computer Science Applications 2.1 The Vector Space Span.mp4

76.5M

Coding the Matrix Linear Algebra through Computer Science Applications 2.2 The Vector Space Geometry of Sets of Vectors.mp4

30.8M

Coding the Matrix Linear Algebra through Computer Science Applications 2.3 The Vector Space Vector spaces.mp4

9.9M

Coding the Matrix Linear Algebra through Computer Science Applications 2.4 The Vector Space Checksum function.mp4

81.4M

Coding the Matrix Linear Algebra through Computer Science Applications 3.0 The Matrix What is a matrix.mp4

32.0M

Coding the Matrix Linear Algebra through Computer Science Applications 3.1 The Matrix Matrix-vector and vector-matrix multiplication.mp4

31.4M

Coding the Matrix Linear Algebra through Computer Science Applications 3.2 The Matrix Matrix-vector multiplication in terms of dot-products.mp4

10.7M

Coding the Matrix Linear Algebra through Computer Science Applications 3.3 The Matrix Null space.mp4

13.8M

Coding the Matrix Linear Algebra through Computer Science Applications 3.4 The Matrix Error-correcting codes.mp4

56.8M

Coding the Matrix Linear Algebra through Computer Science Applications 3.5 The Matrix Matrices and their functions.mp4

85.7M

Coding the Matrix Linear Algebra through Computer Science Applications 3.6 The Matrix Linear functions.mp4

67.3M

Coding the Matrix Linear Algebra through Computer Science Applications 3.7 The Matrix Matrix-matrix multiplication.mp4

18.5M

Coding the Matrix Linear Algebra through Computer Science Applications 3.8 The Matrix Matrix-matrix multiplication and function composition.mp4

74.1M

Coding the Matrix Linear Algebra through Computer Science Applications 3.9 The Matrix Matrix inverse.mp4

10.4M

Coding the Matrix Linear Algebra through Computer Science Applications 4.0 The Basis Coordinate systems.mp4

10.9M

Coding the Matrix Linear Algebra through Computer Science Applications 4.1 The Basis Lossy compression.mp4

26.8M

Coding the Matrix Linear Algebra through Computer Science Applications 4.10 The Basis The Exchange Lemma.mp4

12.3M

Coding the Matrix Linear Algebra through Computer Science Applications 4.2 The Basis Algorithms for finding a set of generators.mp4

72.0M

Coding the Matrix Linear Algebra through Computer Science Applications 4.3 The Basis Minimum spanning forest.mp4

102.3M

Coding the Matrix Linear Algebra through Computer Science Applications 4.4 The Basis Linear dependence.mp4

23.1M

Coding the Matrix Linear Algebra through Computer Science Applications 4.5 The Basis Basis.mp4

6.9M

Coding the Matrix Linear Algebra through Computer Science Applications 4.6 The Basis Unique representation.mp4

13.8M

Coding the Matrix Linear Algebra through Computer Science Applications 4.7 The Basis Change of basis.mp4

31.4M

Coding the Matrix Linear Algebra through Computer Science Applications 4.8 The Basis Perspective rendering.mp4

63.1M

Coding the Matrix Linear Algebra through Computer Science Applications 4.9 The Basis Perspective rectification.mp4

34.5M

Coding the Matrix Linear Algebra through Computer Science Applications 5.0 Dimension The size of a basis.mp4

17.8M

Coding the Matrix Linear Algebra through Computer Science Applications 5.1 Dimension Dimension and rank I.mp4

104.8M

Coding the Matrix Linear Algebra through Computer Science Applications 5.2 Dimension Dimension and rank II.mp4

29.3M

Coding the Matrix Linear Algebra through Computer Science Applications 5.3 Dimension Direct sum.mp4

44.5M

Coding the Matrix Linear Algebra through Computer Science Applications 5.4 Dimension Dimension and linear functions I.mp4

28.5M

Coding the Matrix Linear Algebra through Computer Science Applications 5.5 Dimension Dimension and linear functions II.mp4

37.8M

Coding the Matrix Linear Algebra through Computer Science Applications 5.6 Dimension Two representations of vector spaces.mp4

23.6M

Coding the Matrix Linear Algebra through Computer Science Applications 5.7 Dimension Threshold secret sharing.mp4

15.4M

Coding the Matrix Linear Algebra through Computer Science Applications 6.0 Gaussian Elimination Echelon form.mp4

36.1M

Coding the Matrix Linear Algebra through Computer Science Applications 6.1 Gaussian Elimination Transforming a matrix to echelon form.mp4

34.0M

Coding the Matrix Linear Algebra through Computer Science Applications 6.2 Gaussian Elimination Using Gaussian elimination to solve a system of equations.mp4

108.4M

Coding the Matrix Linear Algebra through Computer Science Applications 6.3 Gaussian Elimination Factoring integers.mp4

17.7M

Coding the Matrix Linear Algebra through Computer Science Applications 6.4 The Inner Product The inner product.mp4

101.7M

Coding the Matrix Linear Algebra through Computer Science Applications 6.5 The Inner Product Orthogonality.mp4

14.8M

Coding the Matrix Linear Algebra through Computer Science Applications 7.0 Orthogonalization Finding the closest point in a plane.mp4

117.0M

Coding the Matrix Linear Algebra through Computer Science Applications 7.1 Orthogonalization Projection orthogonal to multiple vectors.mp4

39.7M

Coding the Matrix Linear Algebra through Computer Science Applications 7.2 Orthogonalization Building an orthogonal set of generators.mp4

26.3M

Coding the Matrix Linear Algebra through Computer Science Applications 7.3 Orthogonalization Computing a basis.mp4

35.5M

Coding the Matrix Linear Algebra through Computer Science Applications 7.4 Orthogonalization Orthogonal complement.mp4

19.6M

Coding the Matrix Linear Algebra through Computer Science Applications 7.5 Orthogonalization Two ways to find a basis for the null space.mp4

19.5M

Coding the Matrix Linear Algebra through Computer Science Applications 7.6 Orthogonalization The QR factorization.mp4

39.9M

Coding the Matrix Linear Algebra through Computer Science Applications 7.7 Orthogonalization Using the QR factorization to solve a matrix equation Ax = b.mp4

32.4M

Coding the Matrix Linear Algebra through Computer Science Applications 7.8 Orthogonalization Applications of least squares.mp4

13.2M

Coding the Matrix Linear Algebra through Computer Science Applications 8.0 How to submit assignments.ogv

42.1M

Coding the Matrix Linear Algebra through Computer Science Applications 0.0 Course Introduction Part 1 (953).ogv

37.5M

Coding the Matrix Linear Algebra through Computer Science Applications 0.1 Course Introduction Part 2 (849).ogv

74.6M

Coding the Matrix Linear Algebra through Computer Science Applications 0.2 The Function The function and other preliminaries (2055).ogv

25.1M

Coding the Matrix Linear Algebra through Computer Science Applications 0.3 The Field Introduction to complex numbers (552).ogv

65.3M

Coding the Matrix Linear Algebra through Computer Science Applications 0.4 The Field Playing with C (1519).ogv

45.2M

Coding the Matrix Linear Algebra through Computer Science Applications 0.5 The Field Playing with GF(2) (1028).ogv

36.8M

Coding the Matrix Linear Algebra through Computer Science Applications 1.0 The Vector What is a vector (820).ogv

45.3M

Coding the Matrix Linear Algebra through Computer Science Applications 1.1 The Vector Vector addition and scalar-vector multiplication (1016).ogv

38.8M

Coding the Matrix Linear Algebra through Computer Science Applications 1.2 The Vector Dictionary-based representations of vectors (910).ogv

40.5M

Coding the Matrix Linear Algebra through Computer Science Applications 1.3 The Vector Vectors over GF(2) (918).ogv

38.0M

Coding the Matrix Linear Algebra through Computer Science Applications 1.4 The Vector Dot-product (849).ogv

20.3M

Coding the Matrix Linear Algebra through Computer Science Applications 1.5 The Vector Dot-product of vectors over GF(2) (444).ogv

17.2M

Coding the Matrix Linear Algebra through Computer Science Applications 1.6 The Vector Solving a triangular system of linear equations (400).ogv

21.3M

Coding the Matrix Linear Algebra through Computer Science Applications 2.0 The Vector Space Linear combinations.ogv

33.2M

Coding the Matrix Linear Algebra through Computer Science Applications 2.1 The Vector Space Span.ogv

56.0M

Coding the Matrix Linear Algebra through Computer Science Applications 2.2 The Vector Space Geometry of Sets of Vectors.ogv

52.5M

Coding the Matrix Linear Algebra through Computer Science Applications 2.3 The Vector Space Vector spaces.ogv

14.6M

Coding the Matrix Linear Algebra through Computer Science Applications 2.4 The Vector Space Checksum function.ogv

58.0M

Coding the Matrix Linear Algebra through Computer Science Applications 3.0 The Matrix What is a matrix.ogv

61.5M

Coding the Matrix Linear Algebra through Computer Science Applications 3.1 The Matrix Matrix-vector and vector-matrix multiplication.ogv

55.2M

Coding the Matrix Linear Algebra through Computer Science Applications 3.2 The Matrix Matrix-vector multiplication in terms of dot-products.ogv

18.2M

Coding the Matrix Linear Algebra through Computer Science Applications 3.3 The Matrix Null space.ogv

22.3M

Coding the Matrix Linear Algebra through Computer Science Applications 3.4 The Matrix Error-correcting codes.ogv

42.0M

Coding the Matrix Linear Algebra through Computer Science Applications 3.5 The Matrix Matrices and their functions.ogv

63.5M

Coding the Matrix Linear Algebra through Computer Science Applications 3.6 The Matrix Linear functions.ogv

50.1M

Coding the Matrix Linear Algebra through Computer Science Applications 3.7 The Matrix Matrix-matrix multiplication.ogv

27.1M

Coding the Matrix Linear Algebra through Computer Science Applications 3.8 The Matrix Matrix-matrix multiplication and function composition.ogv

63.4M

Coding the Matrix Linear Algebra through Computer Science Applications 3.9 The Matrix Matrix inverse.ogv

15.5M

Coding the Matrix Linear Algebra through Computer Science Applications 4.0 The Basis Coordinate systems.ogv

17.9M

Coding the Matrix Linear Algebra through Computer Science Applications 4.1 The Basis Lossy compression.ogv

39.1M

Coding the Matrix Linear Algebra through Computer Science Applications 4.10 The Basis The Exchange Lemma.ogv

21.5M

Coding the Matrix Linear Algebra through Computer Science Applications 4.2 The Basis Algorithms for finding a set of generators.ogv

52.5M

Coding the Matrix Linear Algebra through Computer Science Applications 4.3 The Basis Minimum spanning forest.ogv

75.0M

Coding the Matrix Linear Algebra through Computer Science Applications 4.4 The Basis Linear dependence.ogv

35.6M

Coding the Matrix Linear Algebra through Computer Science Applications 4.5 The Basis Basis.ogv

11.1M

Coding the Matrix Linear Algebra through Computer Science Applications 4.6 The Basis Unique representation.ogv

20.9M

Coding the Matrix Linear Algebra through Computer Science Applications 4.7 The Basis Change of basis.ogv

56.0M

Coding the Matrix Linear Algebra through Computer Science Applications 4.8 The Basis Perspective rendering.ogv

45.6M

Coding the Matrix Linear Algebra through Computer Science Applications 4.9 The Basis Perspective rectification.ogv

28.4M

Coding the Matrix Linear Algebra through Computer Science Applications 5.0 Dimension The size of a basis.ogv

31.2M

Coding the Matrix Linear Algebra through Computer Science Applications 5.1 Dimension Dimension and rank I.ogv

76.2M

Coding the Matrix Linear Algebra through Computer Science Applications 5.2 Dimension Dimension and rank II.ogv

50.8M

Coding the Matrix Linear Algebra through Computer Science Applications 5.3 Dimension Direct sum.ogv

67.8M

Coding the Matrix Linear Algebra through Computer Science Applications 5.4 Dimension Dimension and linear functions I.ogv

45.5M

Coding the Matrix Linear Algebra through Computer Science Applications 5.5 Dimension Dimension and linear functions II.ogv

70.1M

Coding the Matrix Linear Algebra through Computer Science Applications 5.6 Dimension Two representations of vector spaces.ogv

35.8M

Coding the Matrix Linear Algebra through Computer Science Applications 5.7 Dimension Threshold secret sharing.ogv

24.4M

Coding the Matrix Linear Algebra through Computer Science Applications 6.0 Gaussian Elimination Echelon form.ogv

58.8M

Coding the Matrix Linear Algebra through Computer Science Applications 6.1 Gaussian Elimination Transforming a matrix to echelon form.ogv

49.3M

Coding the Matrix Linear Algebra through Computer Science Applications 6.2 Gaussian Elimination Using Gaussian elimination to solve a system of equations.ogv

75.5M

Coding the Matrix Linear Algebra through Computer Science Applications 6.3 Gaussian Elimination Factoring integers.ogv

29.1M

Coding the Matrix Linear Algebra through Computer Science Applications 6.4 The Inner Product The inner product.ogv

75.6M

Coding the Matrix Linear Algebra through Computer Science Applications 6.5 The Inner Product Orthogonality.ogv

22.1M

Coding the Matrix Linear Algebra through Computer Science Applications 7.0 Orthogonalization Finding the closest point in a plane.ogv

81.7M

Coding the Matrix Linear Algebra through Computer Science Applications 7.1 Orthogonalization Projection orthogonal to multiple vectors.ogv

57.6M

Coding the Matrix Linear Algebra through Computer Science Applications 7.2 Orthogonalization Building an orthogonal set of generators.ogv

42.2M

Coding the Matrix Linear Algebra through Computer Science Applications 7.3 Orthogonalization Computing a basis.ogv

51.2M

Coding the Matrix Linear Algebra through Computer Science Applications 7.4 Orthogonalization Orthogonal complement.ogv

28.9M

Coding the Matrix Linear Algebra through Computer Science Applications 7.5 Orthogonalization Two ways to find a basis for the null space.ogv

29.3M

Coding the Matrix Linear Algebra through Computer Science Applications 7.6 Orthogonalization The QR factorization.ogv

59.1M

Coding the Matrix Linear Algebra through Computer Science Applications 7.7 Orthogonalization Using the QR factorization to solve a matrix equation Ax = b.ogv

52.8M

Coding the Matrix Linear Algebra through Computer Science Applications 7.8 Orthogonalization Applications of least squares.ogv

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python_lab4.pdf

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python_lab5.pdf

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python_lab7.pdf

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python_lab8.pdf

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python_lab9.pdf

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python_lab1_jp2.zip

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