File size: 113,449 Bytes
a67be9a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297 4298 4299 4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 4336 4337 4338 4339 4340 4341 4342 4343 4344 4345 4346 4347 4348 4349 4350 4351 4352 4353 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363 4364 4365 4366 4367 4368 4369 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379 4380 4381 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 4392 4393 4394 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405 4406 4407 4408 4409 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433 4434 4435 4436 4437 4438 4439 4440 4441 4442 4443 4444 4445 4446 4447 4448 4449 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 4462 4463 4464 4465 4466 4467 4468 4469 4470 4471 4472 4473 4474 4475 4476 4477 4478 4479 4480 4481 4482 4483 4484 4485 4486 4487 4488 4489 4490 4491 4492 4493 4494 4495 4496 4497 4498 4499 4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541 4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 4556 4557 4558 4559 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583 4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613 4614 4615 4616 4617 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627 4628 4629 4630 4631 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641 4642 4643 4644 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655 4656 4657 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667 4668 4669 4670 4671 4672 4673 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683 4684 4685 4686 4687 4688 4689 4690 4691 4692 4693 4694 4695 4696 4697 4698 4699 4700 4701 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713 4714 4715 4716 4717 4718 4719 4720 4721 4722 4723 4724 4725 4726 4727 4728 4729 4730 4731 4732 4733 4734 4735 4736 4737 4738 4739 4740 4741 4742 4743 4744 4745 4746 4747 4748 4749 4750 4751 4752 4753 4754 4755 4756 4757 4758 4759 4760 4761 4762 4763 4764 4765 4766 4767 4768 4769 4770 4771 4772 4773 4774 4775 4776 4777 4778 4779 4780 4781 4782 4783 4784 4785 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796 4797 4798 4799 4800 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810 4811 4812 4813 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824 4825 4826 4827 4828 4829 4830 4831 4832 4833 4834 4835 4836 4837 4838 4839 4840 4841 4842 4843 4844 4845 4846 4847 4848 4849 4850 4851 4852 4853 4854 4855 4856 4857 4858 4859 4860 4861 4862 4863 4864 4865 4866 4867 4868 4869 4870 4871 4872 4873 4874 4875 4876 4877 4878 4879 4880 4881 4882 4883 4884 4885 4886 4887 4888 4889 4890 4891 4892 4893 4894 4895 4896 4897 4898 4899 4900 4901 4902 4903 4904 4905 4906 4907 4908 4909 4910 4911 4912 4913 4914 4915 4916 4917 4918 4919 4920 4921 4922 4923 4924 4925 4926 4927 4928 4929 4930 4931 4932 4933 4934 4935 4936 4937 4938 4939 4940 4941 4942 4943 4944 4945 4946 4947 4948 4949 4950 4951 4952 4953 4954 4955 4956 4957 4958 4959 4960 4961 4962 4963 4964 4965 4966 4967 4968 4969 4970 4971 4972 4973 4974 4975 4976 4977 4978 4979 4980 4981 4982 4983 4984 4985 4986 4987 4988 4989 4990 4991 4992 4993 4994 4995 4996 4997 4998 4999 5000 5001 5002 5003 5004 5005 5006 5007 5008 5009 5010 5011 5012 5013 5014 5015 5016 5017 5018 5019 5020 5021 5022 5023 5024 5025 5026 5027 5028 5029 5030 5031 5032 5033 5034 5035 5036 5037 5038 5039 5040 5041 5042 5043 5044 5045 5046 5047 5048 5049 5050 5051 5052 5053 5054 5055 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069 5070 5071 5072 5073 5074 5075 5076 5077 5078 5079 5080 5081 5082 5083 5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096 5097 5098 5099 5100 5101 5102 5103 5104 5105 5106 5107 5108 5109 5110 5111 5112 5113 5114 5115 5116 5117 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133 5134 5135 5136 5137 5138 5139 5140 5141 5142 5143 5144 5145 5146 5147 5148 5149 5150 5151 5152 5153 5154 5155 5156 5157 5158 5159 5160 5161 5162 5163 5164 5165 5166 5167 5168 5169 5170 5171 5172 5173 5174 5175 5176 5177 5178 5179 5180 5181 5182 5183 5184 5185 5186 5187 5188 5189 5190 5191 5192 5193 5194 5195 5196 5197 5198 5199 5200 5201 5202 5203 5204 5205 5206 5207 5208 5209 5210 5211 5212 5213 5214 5215 5216 5217 5218 5219 5220 5221 5222 5223 5224 5225 5226 5227 5228 5229 5230 5231 5232 5233 5234 5235 5236 5237 5238 5239 5240 5241 5242 5243 5244 5245 5246 5247 5248 5249 5250 5251 5252 5253 5254 5255 5256 5257 5258 5259 5260 5261 5262 5263 5264 5265 5266 5267 5268 5269 5270 5271 5272 5273 5274 5275 5276 5277 5278 5279 5280 5281 5282 5283 5284 5285 5286 5287 5288 5289 5290 5291 5292 5293 5294 5295 5296 5297 5298 5299 5300 5301 5302 5303 5304 5305 5306 5307 5308 5309 5310 5311 5312 5313 5314 5315 5316 5317 5318 5319 5320 5321 5322 5323 5324 5325 5326 5327 5328 5329 5330 5331 5332 5333 5334 5335 5336 5337 5338 5339 5340 5341 5342 5343 5344 5345 5346 5347 5348 5349 5350 5351 5352 5353 5354 5355 5356 5357 5358 5359 5360 5361 5362 5363 5364 5365 5366 5367 5368 5369 5370 5371 5372 5373 5374 5375 5376 5377 5378 5379 5380 5381 5382 5383 5384 5385 5386 5387 5388 5389 5390 5391 5392 5393 5394 5395 5396 5397 5398 5399 5400 5401 5402 5403 5404 5405 5406 5407 5408 5409 5410 5411 5412 5413 5414 5415 5416 5417 5418 5419 5420 5421 5422 5423 5424 5425 5426 5427 5428 5429 5430 5431 5432 5433 5434 5435 5436 5437 5438 5439 5440 5441 5442 5443 5444 5445 5446 5447 5448 5449 5450 5451 5452 5453 5454 5455 5456 5457 5458 5459 5460 5461 5462 5463 5464 5465 5466 5467 5468 5469 5470 5471 5472 5473 5474 5475 5476 5477 5478 5479 5480 5481 5482 5483 5484 5485 5486 5487 5488 5489 5490 5491 5492 5493 |
WEBVTT Kind: captions; Language: en-US
NOTE
Created on 2024-02-07T20:56:18.4162344Z by ClassTranscribe
00:01:04.700 --> 00:01:05.450
All right.
00:01:05.450 --> 00:01:06.810
Good morning, everybody.
00:01:07.920 --> 00:01:08.950
Hope you're doing well.
00:01:10.010 --> 00:01:10.940
So.
00:01:11.010 --> 00:01:14.260
And so I'll jump into it.
00:01:15.610 --> 00:01:16.000
All right.
00:01:16.000 --> 00:01:18.966
So previously we learned about a lot of
00:01:18.966 --> 00:01:21.260
different individual models, logistic
00:01:21.260 --> 00:01:23.000
regression, Keenan and so on.
00:01:23.000 --> 00:01:25.140
We also learned about trees that are
00:01:25.140 --> 00:01:27.683
able to learn features and split the
00:01:27.683 --> 00:01:29.410
feature space into different chunks and
00:01:29.410 --> 00:01:31.170
then make decisions and those different
00:01:31.170 --> 00:01:32.500
parts of the feature space.
00:01:33.300 --> 00:01:34.980
And then in the last class we learned
00:01:34.980 --> 00:01:37.884
about the bias variance tradeoff, that
00:01:37.884 --> 00:01:41.310
you can have a very complex classifier
00:01:41.310 --> 00:01:42.856
that requires a lot of data to learn
00:01:42.856 --> 00:01:44.919
and that might have low bias that can
00:01:44.920 --> 00:01:47.190
fit the training data really well, but
00:01:47.190 --> 00:01:48.720
high variance that you might get
00:01:48.720 --> 00:01:50.240
different classifiers with different
00:01:50.240 --> 00:01:50.980
samples of data.
00:01:51.730 --> 00:01:53.865
Or you can have a low bias.
00:01:53.865 --> 00:01:56.170
Or you can have a high bias, low
00:01:56.170 --> 00:01:59.429
variance classifier, a short tree, or a
00:01:59.430 --> 00:02:01.390
linear model that might not be able to
00:02:01.390 --> 00:02:03.430
fit the training data perfectly, but
00:02:03.430 --> 00:02:05.648
we'll do similarly on the test data to
00:02:05.648 --> 00:02:06.380
the training data.
00:02:07.250 --> 00:02:10.670
And then the escape of that is using.
00:02:10.670 --> 00:02:12.440
So usually you have this tradeoff where
00:02:12.440 --> 00:02:14.020
you have to choose one or the other,
00:02:14.020 --> 00:02:16.880
but ensembles are able to escape that
00:02:16.880 --> 00:02:19.360
tradeoff by combining multiple
00:02:19.360 --> 00:02:21.390
classifiers to either reduce the
00:02:21.390 --> 00:02:24.070
variance of each or reduce the bias of
00:02:24.070 --> 00:02:24.540
them.
00:02:26.500 --> 00:02:30.250
So this is so we also we talked
00:02:30.250 --> 00:02:32.400
particularly about boosted, boosted
00:02:32.400 --> 00:02:34.390
trees and random forests, which are two
00:02:34.390 --> 00:02:37.180
of the most powerful and widely useful
00:02:37.180 --> 00:02:40.130
classifiers and regressors and machine
00:02:40.130 --> 00:02:40.870
learning.
00:02:40.990 --> 00:02:43.740
The other is what we're starting to get
00:02:43.740 --> 00:02:44.197
into.
00:02:44.197 --> 00:02:46.800
We're starting to work our way towards
00:02:46.800 --> 00:02:50.030
neural networks, which as you know is
00:02:50.030 --> 00:02:52.300
like the is the dominant approach right
00:02:52.300 --> 00:02:53.270
now in machine learning.
00:02:54.260 --> 00:02:56.530
But before we get there, I want to
00:02:56.530 --> 00:03:00.630
introduce one more individual model
00:03:00.630 --> 00:03:02.630
which is the support vector machine.
00:03:03.410 --> 00:03:05.255
Support vector machines or SVM.
00:03:05.255 --> 00:03:06.790
So usually you'll just see people call
00:03:06.790 --> 00:03:08.660
it SVM without writing out the full
00:03:08.660 --> 00:03:08.940
name.
00:03:09.580 --> 00:03:11.652
They are developed in 1990s by Vapnik
00:03:11.652 --> 00:03:15.170
and his colleagues AT&T Bell Labs and
00:03:15.170 --> 00:03:16.870
it was based on statistical learning
00:03:16.870 --> 00:03:18.573
theory, so that their learning theory
00:03:18.573 --> 00:03:21.050
was actually developed by Vapnik and
00:03:21.050 --> 00:03:23.820
independently by others as early as the
00:03:23.820 --> 00:03:25.000
40s or 50s.
00:03:25.860 --> 00:03:28.020
But that led to the SVM algorithm in
00:03:28.020 --> 00:03:28.730
the 90s.
00:03:29.840 --> 00:03:32.780
And SVMS for a while were the most
00:03:32.780 --> 00:03:35.320
popular machine learning algorithm,
00:03:35.320 --> 00:03:37.740
mainly because they have a really good
00:03:37.740 --> 00:03:39.420
justification in terms of
00:03:39.420 --> 00:03:42.500
generalization, theory, theory and they
00:03:42.500 --> 00:03:44.820
can be optimized.
00:03:45.420 --> 00:03:49.000
And so for a while, people felt like
00:03:49.000 --> 00:03:51.100
Anna's were kind of a dead end.
00:03:51.900 --> 00:03:54.400
That's artificial neural networks are a
00:03:54.400 --> 00:03:56.117
dead end because they're a black box.
00:03:56.117 --> 00:03:57.216
They're hard to understand, they're
00:03:57.216 --> 00:03:59.980
hard to optimize, and VMS were able to
00:03:59.980 --> 00:04:02.780
get like similar performance, but are
00:04:02.780 --> 00:04:03.780
much better understood.
00:04:06.080 --> 00:04:08.110
So SVMS are kind of worth knowing and
00:04:08.110 --> 00:04:09.170
their own right?
00:04:09.170 --> 00:04:12.390
But actually the main reason that I'm
00:04:12.390 --> 00:04:15.440
decided to teach about SVMS is because
00:04:15.440 --> 00:04:17.320
there's a lot of other concepts
00:04:17.320 --> 00:04:19.710
associated with SVMS that are widely
00:04:19.710 --> 00:04:21.718
applicable that are worth knowing.
00:04:21.718 --> 00:04:24.245
So one is the generalization properties
00:04:24.245 --> 00:04:26.370
that they try to, for example, achieve
00:04:26.370 --> 00:04:27.240
a big margin.
00:04:27.240 --> 00:04:28.670
I'll explain what that means and.
00:04:29.460 --> 00:04:31.400
And have a decision that relies on
00:04:31.400 --> 00:04:33.700
limited training data, which is called
00:04:33.700 --> 00:04:35.250
structural risk minimization.
00:04:36.110 --> 00:04:38.560
Another is you can incorporate the idea
00:04:38.560 --> 00:04:40.800
of kernels, which is that you can
00:04:40.800 --> 00:04:44.670
define how 2 examples are similar and
00:04:44.670 --> 00:04:48.470
then use that as a basis of training a
00:04:48.470 --> 00:04:48.930
model.
00:04:49.660 --> 00:04:51.370
And related to that.
00:04:52.120 --> 00:04:54.940
We can see how you can formulate the
00:04:54.940 --> 00:04:56.600
same problem in different ways.
00:04:56.600 --> 00:04:59.560
So for SVMS, you can formulate it in
00:04:59.560 --> 00:05:01.550
what's called the primal, which just
00:05:01.550 --> 00:05:04.050
means that for a linear model you're
00:05:04.050 --> 00:05:06.259
saying that the model is a of all the
00:05:06.260 --> 00:05:07.030
features.
00:05:07.030 --> 00:05:09.670
Or you can formulate it in the dual,
00:05:09.670 --> 00:05:12.180
which is that you say that the weights
00:05:12.180 --> 00:05:14.485
are actually a sum of all the training
00:05:14.485 --> 00:05:16.220
examples, a of all the training
00:05:16.220 --> 00:05:16.509
examples.
00:05:17.300 --> 00:05:18.340
And I think it's just kind of
00:05:18.340 --> 00:05:19.230
interesting that.
00:05:20.170 --> 00:05:21.910
You can show that for many linear
00:05:21.910 --> 00:05:24.010
models, we tend to think of them as
00:05:24.010 --> 00:05:26.150
like it's that the linear model
00:05:26.150 --> 00:05:27.780
corresponds to feature importance, and
00:05:27.780 --> 00:05:28.940
you're learning a value for each
00:05:28.940 --> 00:05:33.680
feature, which is true, but the optimal
00:05:33.680 --> 00:05:36.570
linear model can often be expressed as
00:05:36.570 --> 00:05:38.575
just a combination of the training
00:05:38.575 --> 00:05:39.740
examples directly a weighted
00:05:39.740 --> 00:05:41.250
combination of the training examples.
00:05:41.870 --> 00:05:43.770
So it gives an interesting perspective
00:05:43.770 --> 00:05:44.660
I think.
00:05:44.660 --> 00:05:46.630
And then finally there's an
00:05:46.630 --> 00:05:49.240
optimization method for SVMS that was
00:05:49.240 --> 00:05:52.430
proposed that is called sub gradient,
00:05:52.430 --> 00:05:54.520
subgradient method and.
00:05:55.260 --> 00:05:57.150
Particularly it's called the general
00:05:57.150 --> 00:05:58.680
method is called sarcastic gradient
00:05:58.680 --> 00:06:01.780
descent and this is how optimization is
00:06:01.780 --> 00:06:03.380
done for neural networks.
00:06:03.380 --> 00:06:05.450
So I wanted to introduce it in the case
00:06:05.450 --> 00:06:08.310
of the SVMS where it's a little bit
00:06:08.310 --> 00:06:10.530
simpler before I get into.
00:06:11.480 --> 00:06:15.050
Perceptrons and MLPS multilayer
00:06:15.050 --> 00:06:15.780
perceptrons.
00:06:18.250 --> 00:06:21.980
So there's so there's three parts of
00:06:21.980 --> 00:06:22.620
this lecture.
00:06:22.620 --> 00:06:24.290
First, I'm going to talk about linear
00:06:24.290 --> 00:06:24.850
SVMS.
00:06:25.560 --> 00:06:27.660
And then I'm going to talk about
00:06:27.660 --> 00:06:29.900
kernels and nonlinear SVMS.
00:06:30.550 --> 00:06:33.000
And then finally the SVM optimization
00:06:33.000 --> 00:06:34.010
and.
00:06:34.700 --> 00:06:36.560
I might not get to the third part
00:06:36.560 --> 00:06:39.160
today, we'll see, but I don't want to
00:06:39.160 --> 00:06:40.395
rush it too much.
00:06:40.395 --> 00:06:43.090
But even if not, this leads naturally
00:06:43.090 --> 00:06:45.040
into the next lecture, which would
00:06:45.040 --> 00:06:47.220
basically be SGD on perceptrons, so.
00:06:51.360 --> 00:06:55.065
Alright, so SVMS are kind of pose a
00:06:55.065 --> 00:06:56.390
different answer to what's the best
00:06:56.390 --> 00:06:57.710
linear classifier.
00:06:57.710 --> 00:07:00.625
As we discussed previously, if you have
00:07:00.625 --> 00:07:03.260
a set of linearly separated data, these
00:07:03.260 --> 00:07:05.540
Red X's and Green OS, then there's
00:07:05.540 --> 00:07:06.939
actually a bunch of different linear
00:07:06.940 --> 00:07:09.610
models that could separate the X's from
00:07:09.610 --> 00:07:10.170
the O's.
00:07:11.540 --> 00:07:13.860
So logistic regression has one way of
00:07:13.860 --> 00:07:16.240
choosing the best model, which is
00:07:16.240 --> 00:07:18.020
you're maximizing the expected log
00:07:18.020 --> 00:07:20.564
likelihood of the labels given the
00:07:20.564 --> 00:07:20.934
data.
00:07:20.934 --> 00:07:23.620
So for given some boundary, it implies
00:07:23.620 --> 00:07:25.414
some probability for each of the data
00:07:25.414 --> 00:07:25.612
points.
00:07:25.612 --> 00:07:26.990
The data points that are really far
00:07:26.990 --> 00:07:29.260
from the boundary have like a really
00:07:29.260 --> 00:07:30.970
high confidence, and if that's correct,
00:07:30.970 --> 00:07:32.962
it means they have a low loss, and
00:07:32.962 --> 00:07:36.025
labels that are on the wrong side of
00:07:36.025 --> 00:07:37.475
the boundary or close to the boundary
00:07:37.475 --> 00:07:38.650
have a higher loss.
00:07:39.880 --> 00:07:42.870
And so as a result of that objective,
00:07:42.870 --> 00:07:45.580
the logistic regression depends on all
00:07:45.580 --> 00:07:46.760
the training examples.
00:07:46.760 --> 00:07:48.550
Even examples that are very confidently
00:07:48.550 --> 00:07:51.270
correct will contribute a little bit to
00:07:51.270 --> 00:07:53.470
the loss of the optimization.
00:07:54.980 --> 00:07:57.210
On the other hand, SVM makes a very
00:07:57.210 --> 00:07:59.010
different kind of decision.
00:07:59.010 --> 00:08:02.455
So SVM the goal is to make all of the
00:08:02.455 --> 00:08:04.545
examples at least minimally confident.
00:08:04.545 --> 00:08:06.800
So you want all the examples to be at
00:08:06.800 --> 00:08:08.560
least some distance from the boundary.
00:08:09.770 --> 00:08:11.430
And then the decision is based on a
00:08:11.430 --> 00:08:14.040
minimum set of examples, so that even
00:08:14.040 --> 00:08:15.875
if you were to remove a lot of the
00:08:15.875 --> 00:08:17.243
examples that want to actually change
00:08:17.243 --> 00:08:17.929
the decision.
00:08:22.350 --> 00:08:24.840
So this is so there's a little bit of
00:08:24.840 --> 00:08:26.980
terminology that comes with SVMS that's
00:08:26.980 --> 00:08:29.860
worth being careful about.
00:08:30.600 --> 00:08:31.960
One is the margin.
00:08:31.960 --> 00:08:34.680
So the margin is just the distance from
00:08:34.680 --> 00:08:36.950
the boundary of an example.
00:08:36.950 --> 00:08:39.530
So in this case this is an SVM fit to
00:08:39.530 --> 00:08:43.030
these examples and this is like the
00:08:43.030 --> 00:08:45.479
minimum margin of any of the examples.
00:08:45.480 --> 00:08:47.488
But the margin is just the distance
00:08:47.488 --> 00:08:49.330
from this boundary in the correct
00:08:49.330 --> 00:08:49.732
direction.
00:08:49.732 --> 00:08:53.180
So if an ex were over here, it would
00:08:53.180 --> 00:08:55.985
have like a negative margin because it
00:08:55.985 --> 00:08:57.629
would be on the wrong side of the
00:08:57.629 --> 00:09:00.130
boundary and if X is really far in this
00:09:00.130 --> 00:09:00.450
direction.
00:09:00.510 --> 00:09:04.050
Then it has a high positive margin.
00:09:04.900 --> 00:09:07.935
And the margin is normalized by the
00:09:07.935 --> 00:09:09.380
weight length.
00:09:09.380 --> 00:09:11.490
This is the L2 length of the weight.
00:09:13.340 --> 00:09:17.530
Because if you were if the data is
00:09:17.530 --> 00:09:20.140
linearly separable and you arbitrarily
00:09:20.140 --> 00:09:21.940
if you just like increase W if you
00:09:21.940 --> 00:09:26.440
multiply it by 1000 then this then the
00:09:26.440 --> 00:09:29.155
score of each data point will just
00:09:29.155 --> 00:09:31.640
linearly increase with the length of W
00:09:31.640 --> 00:09:33.329
so you need to normalize it by W.
00:09:34.280 --> 00:09:36.960
So mathematically the margin is just.
00:09:36.960 --> 00:09:40.170
This is the linear model W transpose X
00:09:40.170 --> 00:09:42.920
of weights times X plus some bias term
00:09:42.920 --> 00:09:43.150
B.
00:09:44.460 --> 00:09:47.820
I just want to note that bias term like
00:09:47.820 --> 00:09:50.131
in this context is not the same as
00:09:50.131 --> 00:09:51.756
classifier bias like.
00:09:51.756 --> 00:09:54.440
Classifier bias means that you can't
00:09:54.440 --> 00:09:57.110
fit like some kinds of decision
00:09:57.110 --> 00:10:00.000
boundaries, but the bias term is just
00:10:00.000 --> 00:10:02.260
adding a constant to your prediction.
00:10:04.440 --> 00:10:06.470
So we have a linear model here.
00:10:06.470 --> 00:10:07.605
It gets multiplied by Y.
00:10:07.605 --> 00:10:09.420
So in other words, if this is positive
00:10:09.420 --> 00:10:10.930
then I made a correct decision.
00:10:11.510 --> 00:10:13.660
And if this is negative, then I made an
00:10:13.660 --> 00:10:14.660
incorrect decision.
00:10:14.660 --> 00:10:17.280
If Y is -, 1 for example, but the model
00:10:17.280 --> 00:10:20.840
predicts A2, then this will be -, 2 and
00:10:20.840 --> 00:10:22.710
that's that means that I'm like kind of
00:10:22.710 --> 00:10:24.010
confidently incorrect.
00:10:26.690 --> 00:10:27.530
OK.
00:10:27.530 --> 00:10:30.575
And then the second term is a support
00:10:30.575 --> 00:10:32.490
vector, so support vector machines that
00:10:32.490 --> 00:10:33.740
has it in the title.
00:10:33.740 --> 00:10:36.370
A support vector is an example that
00:10:36.370 --> 00:10:41.290
lies on the margin of 1, so on that
00:10:41.290 --> 00:10:42.250
minimum margin.
00:10:43.100 --> 00:10:45.480
So the points that lie within a margin
00:10:45.480 --> 00:10:47.200
of one are the support vectors, and
00:10:47.200 --> 00:10:48.800
actually the decision only depends on
00:10:48.800 --> 00:10:50.310
those support vectors at the end.
00:10:53.170 --> 00:10:56.140
So the objective of the SVM is to try
00:10:56.140 --> 00:10:59.080
to minimize the sum of squared weights
00:10:59.080 --> 00:11:01.970
while preserving a margin of 1 S you
00:11:01.970 --> 00:11:05.340
could also cast it as that your weight
00:11:05.340 --> 00:11:06.930
vector is constrained to be unit
00:11:06.930 --> 00:11:08.515
length, but you want to maximize the
00:11:08.515 --> 00:11:08.770
margin.
00:11:08.770 --> 00:11:11.590
Those are just equivalent formulations.
00:11:13.240 --> 00:11:15.740
So here's so here's an example of an
00:11:15.740 --> 00:11:16.640
optimized model.
00:11:16.640 --> 00:11:18.560
Now here I added like a big probability
00:11:18.560 --> 00:11:21.470
mass of X's over here, and note that
00:11:21.470 --> 00:11:23.450
the SVM doesn't care about them at all.
00:11:23.450 --> 00:11:25.680
It only cares about these examples that
00:11:25.680 --> 00:11:27.720
are really close to this decision
00:11:27.720 --> 00:11:29.769
boundary between the O's and the ex's.
00:11:30.420 --> 00:11:35.060
So these three examples that are an
00:11:35.060 --> 00:11:37.760
equidistant from the decision boundary
00:11:37.760 --> 00:11:39.717
have they have like determined the
00:11:39.717 --> 00:11:40.193
decision boundary.
00:11:40.193 --> 00:11:42.094
These are the X's that are closest to
00:11:42.094 --> 00:11:44.320
the O's and the O that's closest to the
00:11:44.320 --> 00:11:46.260
X's, while these ones that are have a
00:11:46.260 --> 00:11:48.280
higher margin have not influenced the
00:11:48.280 --> 00:11:48.960
decision boundary.
00:11:51.590 --> 00:11:55.200
In fact, if you have a two, if the data
00:11:55.200 --> 00:11:58.532
is linearly separable and you have two
00:11:58.532 --> 00:12:00.140
two-dimensional features like I have
00:12:00.140 --> 00:12:02.690
here, these are the features X1 and X2,
00:12:02.690 --> 00:12:04.580
then there will always be 3 support
00:12:04.580 --> 00:12:05.890
vectors.
00:12:05.890 --> 00:12:06.340
Question.
00:12:08.680 --> 00:12:10.170
So yeah, good question.
00:12:10.170 --> 00:12:12.900
So the decision boundary is if the
00:12:12.900 --> 00:12:15.207
features are on one side of the
00:12:15.207 --> 00:12:16.656
boundary, then it's going to be one
00:12:16.656 --> 00:12:18.155
class, and if they're on the other side
00:12:18.155 --> 00:12:19.718
of the boundary then it will be the
00:12:19.718 --> 00:12:20.210
other class.
00:12:21.130 --> 00:12:23.380
And in terms of the linear model, if
00:12:23.380 --> 00:12:26.850
you have your model is W transpose X +
00:12:26.850 --> 00:12:29.435
B, so it's like a of the features plus
00:12:29.435 --> 00:12:30.260
the bias term.
00:12:31.120 --> 00:12:33.030
The decision boundary is where that
00:12:33.030 --> 00:12:34.440
value is 0.
00:12:34.440 --> 00:12:37.610
So if this value W transpose X + B.
00:12:38.300 --> 00:12:40.460
Is greater than zero, then you're
00:12:40.460 --> 00:12:43.060
predicting that the label is 1, and if
00:12:43.060 --> 00:12:45.225
this is less than zero, then you're
00:12:45.225 --> 00:12:48.136
predicting that the label is -, 1, and
00:12:48.136 --> 00:12:49.990
if it's equal to 0, then you're right
00:12:49.990 --> 00:12:51.750
on the boundary of that decision.
00:12:52.590 --> 00:12:53.940
There's a help.
00:12:55.640 --> 00:12:56.310
Yeah.
00:13:03.470 --> 00:13:04.010
If.
00:13:04.090 --> 00:13:04.740
And.
00:13:05.910 --> 00:13:08.550
So if the so the decision boundary
00:13:08.550 --> 00:13:10.320
actually it kind of it's not shown
00:13:10.320 --> 00:13:11.390
here, but it also kind of as a
00:13:11.390 --> 00:13:11.910
direction.
00:13:12.590 --> 00:13:14.958
So if things are on one side of the
00:13:14.958 --> 00:13:16.490
boundary then they would be X's, and if
00:13:16.490 --> 00:13:17.600
they're on the other side of the
00:13:17.600 --> 00:13:18.740
boundary then they'd be OS.
00:13:20.890 --> 00:13:22.920
And the boundary is fit to this data,
00:13:22.920 --> 00:13:25.775
so it's solved for in a way that this
00:13:25.775 --> 00:13:26.620
is true.
00:13:29.100 --> 00:13:30.080
Question.
00:13:30.080 --> 00:13:30.830
So how?
00:13:31.830 --> 00:13:35.420
Will perform when two data set are
00:13:35.420 --> 00:13:37.020
merged with each other, like when
00:13:37.020 --> 00:13:40.300
they're not separated, separable,
00:13:40.300 --> 00:13:41.310
mostly separable.
00:13:41.560 --> 00:13:43.020
They have a lot of emerging.
00:13:43.020 --> 00:13:44.902
Yeah, I'll get to that.
00:13:44.902 --> 00:13:45.220
Yeah.
00:13:45.220 --> 00:13:46.500
For now, I'm just dealing with this
00:13:46.500 --> 00:13:48.410
separable case where they can be
00:13:48.410 --> 00:13:49.906
perfectly classified.
00:13:49.906 --> 00:13:53.510
So the linear logistic regression
00:13:53.510 --> 00:13:55.379
behaves differently because it wants,
00:13:55.380 --> 00:13:57.240
these are a lot of data points and they
00:13:57.240 --> 00:14:00.760
will all have some loss even if they're
00:14:00.760 --> 00:14:02.314
like further away than other data
00:14:02.314 --> 00:14:03.372
points from the boundary.
00:14:03.372 --> 00:14:05.230
And so it wants them all to be really
00:14:05.230 --> 00:14:06.770
far from the boundary so that they're
00:14:06.770 --> 00:14:08.390
not incurring a lot of loss in total.
00:14:09.260 --> 00:14:11.633
So the linear logistic regression will
00:14:11.633 --> 00:14:13.880
push the line push the decision
00:14:13.880 --> 00:14:16.139
boundary away from this cluster of X's,
00:14:16.140 --> 00:14:17.970
even if it means that it has to be
00:14:17.970 --> 00:14:19.810
closer to one of the other ex's.
00:14:20.810 --> 00:14:22.650
And in some sense, this is a reasonable
00:14:22.650 --> 00:14:25.260
thing to do, because it makes your
00:14:25.260 --> 00:14:27.210
improves your overall average
00:14:27.210 --> 00:14:29.010
confidence in the correct label.
00:14:29.740 --> 00:14:32.143
Your average correct log confidence to
00:14:32.143 --> 00:14:33.310
be precise.
00:14:33.310 --> 00:14:37.100
But in another sense it's not so good
00:14:37.100 --> 00:14:38.640
because if you're if at the end of the
00:14:38.640 --> 00:14:39.930
day you're trying to minimize your
00:14:39.930 --> 00:14:42.230
classification error, they're very well
00:14:42.230 --> 00:14:44.380
could be other ex's that are in the
00:14:44.380 --> 00:14:46.570
test data that are around this point,
00:14:46.570 --> 00:14:47.940
and some of them might end up on the
00:14:47.940 --> 00:14:49.040
wrong side of the boundary.
00:14:56.200 --> 00:14:59.150
So this is the basic idea of the SVM,
00:14:59.150 --> 00:15:01.870
and the reason that SVMS are so popular
00:15:01.870 --> 00:15:04.380
is because they have really good
00:15:04.380 --> 00:15:05.590
marginalization.
00:15:05.590 --> 00:15:07.550
I mean really good generalization
00:15:07.550 --> 00:15:09.130
guarantees.
00:15:10.130 --> 00:15:14.360
So there's like 2 main Principles, 2
00:15:14.360 --> 00:15:16.720
main reasons that they generalize, and
00:15:16.720 --> 00:15:18.380
again generalize means that they will
00:15:18.380 --> 00:15:20.470
perform similarly to the test data
00:15:20.470 --> 00:15:21.700
compared to the training data.
00:15:24.090 --> 00:15:26.030
One is that maximizing the margin.
00:15:26.030 --> 00:15:28.320
So if all the examples are far from the
00:15:28.320 --> 00:15:30.570
margin, then you can be confident that
00:15:30.570 --> 00:15:31.770
other samples from the same
00:15:31.770 --> 00:15:33.896
distribution are probably also going to
00:15:33.896 --> 00:15:35.600
be correct on the correct side of the
00:15:35.600 --> 00:15:36.010
boundary.
00:15:38.430 --> 00:15:41.410
The second thing is that it doesn't
00:15:41.410 --> 00:15:43.380
depend on a lot of training samples.
00:15:44.630 --> 00:15:48.810
So even if most of these X's and O's
00:15:48.810 --> 00:15:50.640
disappeared, as long as these three
00:15:50.640 --> 00:15:52.150
examples were here, you would end up
00:15:52.150 --> 00:15:53.270
fitting the same boundary.
00:15:54.170 --> 00:15:56.630
And so for example one way that you can
00:15:56.630 --> 00:16:00.110
measure the that you can get an
00:16:00.110 --> 00:16:02.830
estimate of your test error is to do
00:16:02.830 --> 00:16:04.120
leave one out cross validation.
00:16:04.120 --> 00:16:06.310
Which is when you remove one data point
00:16:06.310 --> 00:16:08.570
from the training set and then train a
00:16:08.570 --> 00:16:10.715
model and then test it on that left out
00:16:10.715 --> 00:16:12.370
point and then you keep on changing
00:16:12.370 --> 00:16:13.350
which point is left out.
00:16:13.960 --> 00:16:15.290
If you do leave one out cross
00:16:15.290 --> 00:16:17.370
validation on this, then if you leave
00:16:17.370 --> 00:16:19.286
out any of these points that are not on
00:16:19.286 --> 00:16:20.690
the margin, that you're going to get
00:16:20.690 --> 00:16:23.710
them correct, because the boundary will
00:16:23.710 --> 00:16:25.440
be defined by only these three points
00:16:25.440 --> 00:16:26.050
anyway.
00:16:26.050 --> 00:16:27.779
In other words, leaving out any of
00:16:27.779 --> 00:16:29.130
these points not on the margin won't
00:16:29.130 --> 00:16:31.840
change the boundary, and so if they're
00:16:31.840 --> 00:16:33.140
correct in training, they'll also be
00:16:33.140 --> 00:16:34.040
corrected in testing.
00:16:35.290 --> 00:16:36.780
So that leads to this.
00:16:36.850 --> 00:16:38.905
On this there's a.
00:16:38.905 --> 00:16:42.170
There's a proof here of the expected
00:16:42.170 --> 00:16:43.000
test error.
00:16:43.690 --> 00:16:45.425
A bound on the expected test error.
00:16:45.425 --> 00:16:47.120
So the expected test error will be no
00:16:47.120 --> 00:16:49.430
more than the percent of training
00:16:49.430 --> 00:16:51.360
samples that are support vectors.
00:16:51.360 --> 00:16:53.440
So in this case it would be 3 divided
00:16:53.440 --> 00:16:55.110
by the total number of training points.
00:16:56.250 --> 00:17:00.253
Or if it's or, it could be also smaller
00:17:00.253 --> 00:17:00.486
than.
00:17:00.486 --> 00:17:02.140
It will be smaller than the smallest of
00:17:02.140 --> 00:17:02.760
these.
00:17:03.910 --> 00:17:08.460
The D squared is like the smallest, the
00:17:08.460 --> 00:17:11.040
diameter of the smallest ball that
00:17:11.040 --> 00:17:11.470
contains.
00:17:11.470 --> 00:17:13.161
It's a square of the diameter of the
00:17:13.161 --> 00:17:14.370
smallest ball that contains all these
00:17:14.370 --> 00:17:14.730
points.
00:17:15.420 --> 00:17:16.620
Compared to the margin.
00:17:16.620 --> 00:17:18.853
So in other words, if the data, if the
00:17:18.853 --> 00:17:20.695
margin is like pretty big compared to
00:17:20.695 --> 00:17:22.340
the general variance of the data
00:17:22.340 --> 00:17:24.580
points, then you're going to have a
00:17:24.580 --> 00:17:27.950
small test error and that proves a lot
00:17:27.950 --> 00:17:28.950
more complicated.
00:17:28.950 --> 00:17:30.930
So it's at the link though, yeah?
00:17:33.500 --> 00:17:36.120
We find that the support vector through
00:17:36.120 --> 00:17:38.430
operation, so I will get to the
00:17:38.430 --> 00:17:40.280
optimization too, yeah.
00:17:41.500 --> 00:17:42.160
Some.
00:17:42.160 --> 00:17:44.290
There's actually many ways to solve it,
00:17:44.290 --> 00:17:46.960
and in the third part I'll talk about.
00:17:47.960 --> 00:17:51.920
What is a stochastic gradient descent?
00:17:51.920 --> 00:17:56.200
Which is the most the fastest way and
00:17:56.200 --> 00:17:58.080
probably the preferred way right now,
00:17:58.080 --> 00:17:58.380
yeah?
00:18:14.040 --> 00:18:17.385
So you could say, I think that you
00:18:17.385 --> 00:18:18.880
could pose, I think you could
00:18:18.880 --> 00:18:19.810
equivalently.
00:18:20.470 --> 00:18:23.580
Pose the problem as you want to.
00:18:23.790 --> 00:18:24.410
00:18:25.410 --> 00:18:26.182
Maximum.
00:18:26.182 --> 00:18:31.216
So this distance here is like the is
00:18:31.216 --> 00:18:35.140
the West transpose X + b * Y.
00:18:35.140 --> 00:18:38.950
So in other words, if WTX is very far
00:18:38.950 --> 00:18:40.550
from the boundary then you have a high
00:18:40.550 --> 00:18:43.414
margin that's like the distance in this
00:18:43.414 --> 00:18:44.560
like plotted space.
00:18:45.920 --> 00:18:48.440
And if you just like arbitrarily
00:18:48.440 --> 00:18:50.380
increase W, then that distance is going
00:18:50.380 --> 00:18:52.030
to increase because you're multiplying
00:18:52.030 --> 00:18:54.050
X by a larger number for each of your
00:18:54.050 --> 00:18:54.460
weights.
00:18:55.160 --> 00:18:57.523
And so you need to kind of normal, you
00:18:57.523 --> 00:19:00.000
need to in some way normalize for the
00:19:00.000 --> 00:19:00.705
weight length.
00:19:00.705 --> 00:19:03.159
And one way to do that is to say you
00:19:03.160 --> 00:19:05.293
could say that I'm going to fix my
00:19:05.293 --> 00:19:07.740
weights to be unit length that they
00:19:07.740 --> 00:19:09.730
have to their weights can't just get
00:19:09.730 --> 00:19:10.824
like arbitrarily bigger.
00:19:10.824 --> 00:19:13.390
And I'm going to try to make the margin
00:19:13.390 --> 00:19:14.820
as big as possible given that.
00:19:15.790 --> 00:19:18.812
But I probably just first for.
00:19:18.812 --> 00:19:20.250
It's probably just an easier
00:19:20.250 --> 00:19:21.250
optimization problem.
00:19:21.250 --> 00:19:23.100
I'm not sure exactly why, but it's
00:19:23.100 --> 00:19:25.380
usually posed as you want to minimize
00:19:25.380 --> 00:19:26.550
the length of the weights.
00:19:27.250 --> 00:19:29.180
While maintaining that the margin is 1.
00:19:29.910 --> 00:19:32.079
And I think that it may be that this
00:19:32.080 --> 00:19:34.690
lends itself better to so.
00:19:34.690 --> 00:19:36.260
I haven't talked about it yet, but to
00:19:36.260 --> 00:19:38.120
when you have when the data is not
00:19:38.120 --> 00:19:40.295
linearly separable, then it's very easy
00:19:40.295 --> 00:19:42.677
to modify this objective to account for
00:19:42.677 --> 00:19:44.410
the data that can't be correctly
00:19:44.410 --> 00:19:44.980
classified.
00:19:47.520 --> 00:19:50.140
Did that follow that at all?
00:19:52.740 --> 00:19:53.160
OK.
00:19:56.510 --> 00:19:58.540
So.
00:20:00.950 --> 00:20:02.700
Alright, so in the separable case,
00:20:02.700 --> 00:20:04.360
meaning that you can perfectly classify
00:20:04.360 --> 00:20:05.700
your data with a linear model.
00:20:06.580 --> 00:20:08.630
The prediction is simply the sign of
00:20:08.630 --> 00:20:12.077
your linear model W transpose X + B so
00:20:12.077 --> 00:20:15.780
and the labels here are one and -, 1.
00:20:15.780 --> 00:20:17.295
You can see in like different cases,
00:20:17.295 --> 00:20:19.054
sometimes people say binary problem,
00:20:19.054 --> 00:20:21.045
the labels are zero or one and
00:20:21.045 --> 00:20:23.022
sometimes they'll say it's -, 1 or one.
00:20:23.022 --> 00:20:25.460
And it's mainly just chosen for the
00:20:25.460 --> 00:20:26.712
simplicity of the math.
00:20:26.712 --> 00:20:29.040
In this case it kind of makes it the
00:20:29.040 --> 00:20:29.350
make.
00:20:29.350 --> 00:20:31.080
It makes the math a lot simpler so I
00:20:31.080 --> 00:20:33.794
don't have to say like F y = 0 then
00:20:33.794 --> 00:20:36.030
this, if y = 1 then this other thing I
00:20:36.030 --> 00:20:36.410
can just.
00:20:36.490 --> 00:20:37.630
Y into the equation.
00:20:39.400 --> 00:20:42.540
The optimization is I'm going to solve
00:20:42.540 --> 00:20:45.960
for the West the weights that minimize
00:20:45.960 --> 00:20:48.930
that the smallest weights that satisfy
00:20:48.930 --> 00:20:49.850
this constraint.
00:20:50.680 --> 00:20:53.650
That the margin is one for all
00:20:53.650 --> 00:20:56.840
examples, so the model times the model
00:20:56.840 --> 00:20:59.826
prediction times the label is at least
00:20:59.826 --> 00:21:01.600
one for every training sample.
00:21:06.580 --> 00:21:09.440
If the data is not linearly separable,
00:21:09.440 --> 00:21:12.490
then I can just extend a little bit.
00:21:13.190 --> 00:21:14.520
And I can say.
00:21:15.780 --> 00:21:17.000
I don't know what that sound is.
00:21:17.000 --> 00:21:19.350
It's really weird, OK?
00:21:20.690 --> 00:21:23.210
And if the data is not linearly
00:21:23.210 --> 00:21:24.230
separable.
00:21:25.130 --> 00:21:26.900
Then I can say that I'm going to just
00:21:26.900 --> 00:21:30.240
pay a penalty of C times, like how much
00:21:30.240 --> 00:21:32.467
that data violates my margin.
00:21:32.467 --> 00:21:35.405
So the if it has a margin of less than
00:21:35.405 --> 00:21:39.533
one, then I pay C * 1 minus its margin.
00:21:39.533 --> 00:21:42.222
So for example if it's right on the
00:21:42.222 --> 00:21:44.280
boundary, then W transpose X + b is
00:21:44.280 --> 00:21:47.665
equal to 0 and so I pay a penalty of C
00:21:47.665 --> 00:21:49.380
* 1 if it's negative.
00:21:49.380 --> 00:21:50.638
If it's on the wrong side of the
00:21:50.638 --> 00:21:51.820
boundary, then I'd pay an even higher
00:21:51.820 --> 00:21:53.456
penalty, and if it's on the right side
00:21:53.456 --> 00:21:54.500
of the boundary, but.
00:21:54.560 --> 00:21:56.800
But the margin is less than one, then I
00:21:56.800 --> 00:21:57.810
pay a smaller penalty.
00:22:00.520 --> 00:22:03.040
This is called the hinge loss, and I'll
00:22:03.040 --> 00:22:04.050
show it here.
00:22:04.050 --> 00:22:06.210
So in the hinge loss, if you're
00:22:06.210 --> 00:22:08.130
confidently correct, there's zero
00:22:08.130 --> 00:22:10.110
penalty if you have a margin of greater
00:22:10.110 --> 00:22:12.080
than one in the case of an SVM.
00:22:12.750 --> 00:22:14.820
But if you're not confidently correct
00:22:14.820 --> 00:22:17.085
if they're unconfident or incorrect,
00:22:17.085 --> 00:22:18.980
which means which is when you're on
00:22:18.980 --> 00:22:20.640
this side of the decision boundary.
00:22:21.300 --> 00:22:24.460
Then you pay a penalty and the penalty
00:22:24.460 --> 00:22:26.070
just increases.
00:22:27.410 --> 00:22:30.220
Proportionally to how far you are from
00:22:30.220 --> 00:22:31.600
the margin of 1.
00:22:33.010 --> 00:22:35.640
And say if you have, if you're just
00:22:35.640 --> 00:22:37.350
unconfident way correct, you pay a
00:22:37.350 --> 00:22:38.803
little penalty, if you're incorrect,
00:22:38.803 --> 00:22:41.209
you pay a bigger penalty, and if you're
00:22:41.210 --> 00:22:42.760
confidently incorrect, then you pay an
00:22:42.760 --> 00:22:43.720
even bigger penalty.
00:22:45.420 --> 00:22:48.050
And this is important because.
00:22:48.780 --> 00:22:51.170
With this kind of loss, the confidently
00:22:51.170 --> 00:22:54.450
correct examples don't make any they
00:22:54.450 --> 00:22:56.090
don't change the decision.
00:22:56.090 --> 00:22:58.350
So anything that incurs a loss means
00:22:58.350 --> 00:23:00.000
that it's part of your thing that
00:23:00.000 --> 00:23:01.420
you're minimizing and your objective
00:23:01.420 --> 00:23:02.190
function.
00:23:02.190 --> 00:23:04.070
But if it doesn't incur a loss, then
00:23:04.070 --> 00:23:07.180
it's not changing your objective
00:23:07.180 --> 00:23:09.710
evaluation, so it's not causing any
00:23:09.710 --> 00:23:10.760
change to your decision.
00:23:15.700 --> 00:23:18.486
So I also need to note that there's
00:23:18.486 --> 00:23:20.373
like different ways of expressing the
00:23:20.373 --> 00:23:20.839
same thing.
00:23:20.839 --> 00:23:22.939
So here I express it in terms of this
00:23:22.940 --> 00:23:23.840
hinge loss.
00:23:23.840 --> 00:23:26.399
But you can also express it in terms of
00:23:26.400 --> 00:23:28.490
what people call slack variables.
00:23:28.490 --> 00:23:30.443
It's the exact same thing.
00:23:30.443 --> 00:23:32.850
It's just that here this slack variable
00:23:32.850 --> 00:23:35.220
is equal to 1 minus the margin.
00:23:35.220 --> 00:23:37.270
This is like if I bring.
00:23:39.220 --> 00:23:39.796
A.
00:23:39.796 --> 00:23:42.610
Bring this over here and then bring
00:23:42.610 --> 00:23:43.335
that over here.
00:23:43.335 --> 00:23:45.030
Then this slack variable when you
00:23:45.030 --> 00:23:47.030
minimize it will be equal to 1 minus
00:23:47.030 --> 00:23:47.730
this margin.
00:23:49.480 --> 00:23:51.330
So Slack variable is 1 minus the margin
00:23:51.330 --> 00:23:52.740
and you pay the same penalty.
00:23:52.740 --> 00:23:55.020
But if you're ever like reading about
00:23:55.020 --> 00:23:57.230
SVMS and somebody says like slack
00:23:57.230 --> 00:23:58.820
variable, then I just want you to know
00:23:58.820 --> 00:23:59.350
what that means.
00:24:00.260 --> 00:24:01.620
This means.
00:24:01.620 --> 00:24:03.760
So for this example here, we would be
00:24:03.760 --> 00:24:05.740
paying some penalty, some slack
00:24:05.740 --> 00:24:08.010
penalty, or some hinge loss penalty
00:24:08.010 --> 00:24:08.780
equivalently.
00:24:10.520 --> 00:24:12.840
Here's an example of an SVM decision
00:24:12.840 --> 00:24:15.510
boundary classifying between these red
00:24:15.510 --> 00:24:17.390
Oreos and Blue X's.
00:24:17.390 --> 00:24:19.270
This is from Andrews Esterman slides
00:24:19.270 --> 00:24:20.530
from Oxford.
00:24:22.710 --> 00:24:25.266
And here there's a soft margin, so
00:24:25.266 --> 00:24:27.210
there's some penalty.
00:24:27.210 --> 00:24:29.740
If you were to set this PC to Infinity,
00:24:29.740 --> 00:24:32.280
it means that you are still requiring
00:24:32.280 --> 00:24:34.820
that every example has a.
00:24:35.960 --> 00:24:37.930
Is has a margin of 1.
00:24:38.610 --> 00:24:40.310
Which that can be a problem if you have
00:24:40.310 --> 00:24:41.930
this case, because then you won't be
00:24:41.930 --> 00:24:43.360
able to optimize it because it's
00:24:43.360 --> 00:24:44.000
impossible.
00:24:45.030 --> 00:24:48.150
So if you set a small CC is 10, then
00:24:48.150 --> 00:24:49.790
you pay a small penalty when things
00:24:49.790 --> 00:24:50.495
violate the margin.
00:24:50.495 --> 00:24:52.360
And in this case it finds the decision
00:24:52.360 --> 00:24:54.180
boundary where it incorrectly
00:24:54.180 --> 00:24:57.270
classifies this one example and you
00:24:57.270 --> 00:25:00.473
have these four examples are within the
00:25:00.473 --> 00:25:00.829
margin.
00:25:00.830 --> 00:25:01.310
We're on it.
00:25:05.750 --> 00:25:06.300
OK.
00:25:06.300 --> 00:25:08.500
Any questions about that so far?
00:25:09.590 --> 00:25:09.980
OK.
00:25:11.890 --> 00:25:16.150
So I'm going to talk about the
00:25:16.150 --> 00:25:18.270
objective functions a little bit more,
00:25:18.270 --> 00:25:20.730
and to do that I'll introduce this
00:25:20.730 --> 00:25:22.180
thing called the Representer theorem.
00:25:22.940 --> 00:25:25.500
So the Representer theorem basically
00:25:25.500 --> 00:25:29.100
says that if you have some model, some
00:25:29.100 --> 00:25:31.240
linear model, that's W transpose X.
00:25:32.240 --> 00:25:37.240
Then the optimal West in many cases can
00:25:37.240 --> 00:25:43.100
be expressed as a of some weight for
00:25:43.100 --> 00:25:46.210
each example and the example features.
00:25:46.970 --> 00:25:49.240
And the label of the features or the
00:25:49.240 --> 00:25:50.450
label of the data point?
00:25:52.080 --> 00:25:55.300
So the optimal weight vector is just a
00:25:55.300 --> 00:25:58.160
weighted average of the input training
00:25:58.160 --> 00:25:59.270
example features.
00:26:02.260 --> 00:26:03.940
And there's certain like caveats and
00:26:03.940 --> 00:26:06.760
conditions, but this is true for L2
00:26:06.760 --> 00:26:10.550
logistic regression or SVM for example.
00:26:13.120 --> 00:26:17.500
And for SVMS these alphas are zeros for
00:26:17.500 --> 00:26:20.066
all the non support vectors because the
00:26:20.066 --> 00:26:22.080
support vectors influence the decision.
00:26:23.420 --> 00:26:24.800
So it's actually depends on a very
00:26:24.800 --> 00:26:26.390
small number of training examples.
00:26:28.710 --> 00:26:30.690
So I'm not going to go deep into the
00:26:30.690 --> 00:26:33.000
math and I don't expect anybody to be
00:26:33.000 --> 00:26:34.880
able to derive the dual or anything
00:26:34.880 --> 00:26:38.127
like that, but I just want to express
00:26:38.127 --> 00:26:39.940
express these objectives and different
00:26:39.940 --> 00:26:41.240
ways of looking at the problem.
00:26:42.100 --> 00:26:44.433
So in terms of prediction already I
00:26:44.433 --> 00:26:46.030
already gave you this formulation
00:26:46.030 --> 00:26:47.833
that's called the primal where the
00:26:47.833 --> 00:26:49.550
where you're optimizing in terms of the
00:26:49.550 --> 00:26:50.310
feature weights.
00:26:51.570 --> 00:26:53.550
And then you can also represent it in
00:26:53.550 --> 00:26:56.020
terms of you can represent, whoops, the
00:26:56.020 --> 00:26:56.700
dual.
00:26:57.700 --> 00:26:58.280
Where to go?
00:26:59.380 --> 00:27:01.400
Alright, you can also represent it in
00:27:01.400 --> 00:27:03.595
what's called a dual, where instead of
00:27:03.595 --> 00:27:05.330
optimizing over feature weights, you're
00:27:05.330 --> 00:27:06.920
optimizing over the weights of each
00:27:06.920 --> 00:27:07.520
example.
00:27:08.160 --> 00:27:10.470
Where again sum of those weights of the
00:27:10.470 --> 00:27:12.720
examples gives you your weight vector.
00:27:13.560 --> 00:27:16.256
And remember that this weights are the
00:27:16.256 --> 00:27:19.560
sum of alpha YX and when I plug that in
00:27:19.560 --> 00:27:22.410
here then I see in the dual that my
00:27:22.410 --> 00:27:25.914
prediction is the sum of alpha Y and
00:27:25.914 --> 00:27:28.395
the dot product of each training
00:27:28.395 --> 00:27:30.910
example with the example that I'm
00:27:30.910 --> 00:27:31.680
predicting for.
00:27:33.230 --> 00:27:33.980
So this.
00:27:33.980 --> 00:27:36.540
So here there's like a, it's a.
00:27:36.540 --> 00:27:39.255
It's an average of the similarities of
00:27:39.255 --> 00:27:43.550
the training examples with the features
00:27:43.550 --> 00:27:45.330
that I'm making a prediction for.
00:27:46.110 --> 00:27:47.730
Where the similarity is defined by A
00:27:47.730 --> 00:27:49.020
dot product in this case.
00:27:50.820 --> 00:27:53.831
Dot product is the sum of the elements
00:27:53.831 --> 00:27:56.571
squared or the I mean squared but the
00:27:56.571 --> 00:27:58.820
sum of the product of the elements.
00:28:01.790 --> 00:28:05.558
And this is just plugging it into the
00:28:05.558 --> 00:28:06.950
into the.
00:28:06.950 --> 00:28:08.750
If I plug everything in and then write
00:28:08.750 --> 00:28:10.350
the objective of the dual it comes out
00:28:10.350 --> 00:28:11.030
to this.
00:28:13.950 --> 00:28:17.410
For an SVM, alpha sparse, which means
00:28:17.410 --> 00:28:19.080
most of the values are zero.
00:28:19.080 --> 00:28:22.115
So the SVM only depends on these few
00:28:22.115 --> 00:28:25.920
examples, and so it's only nonzero for
00:28:25.920 --> 00:28:27.640
the support vectors, the examples that
00:28:27.640 --> 00:28:28.560
are within the margin.
00:28:35.550 --> 00:28:37.460
So the reason that the dual will be
00:28:37.460 --> 00:28:40.280
helpful is that it.
00:28:41.900 --> 00:28:45.020
Is that it allows us to deal with a
00:28:45.020 --> 00:28:45.930
nonlinear case.
00:28:45.930 --> 00:28:48.422
So in the top example, we might say a
00:28:48.422 --> 00:28:50.180
linear classifier is OK, it only gets
00:28:50.180 --> 00:28:51.550
one example wrong.
00:28:51.550 --> 00:28:53.179
I can live with that.
00:28:53.180 --> 00:28:55.437
But in the bottom case, a linear
00:28:55.437 --> 00:28:57.860
example seems like a really bad choice,
00:28:57.860 --> 00:28:58.210
right?
00:28:58.210 --> 00:29:00.995
Like it's obviously nonlinear and a
00:29:00.995 --> 00:29:02.010
linear classifier is going to get
00:29:02.010 --> 00:29:02.780
really high error.
00:29:03.530 --> 00:29:06.790
So what is some way that I could try
00:29:06.790 --> 00:29:08.630
to, let's say I still want to stick
00:29:08.630 --> 00:29:10.220
with a linear classifier, what's
00:29:10.220 --> 00:29:12.750
something that I can do to this do in
00:29:12.750 --> 00:29:16.375
this case to improve the ability of the
00:29:16.375 --> 00:29:16.950
linear classifier?
00:29:19.410 --> 00:29:19.860
Yeah.
00:29:22.680 --> 00:29:24.680
So I can like I can change the
00:29:24.680 --> 00:29:26.880
coordinate system or change the
00:29:26.880 --> 00:29:28.740
features in some way so that they
00:29:28.740 --> 00:29:30.140
become linearly separable.
00:29:30.930 --> 00:29:32.160
And the new feature space.
00:29:32.230 --> 00:29:34.440
Can we reject it in different
00:29:34.440 --> 00:29:34.890
dimensions?
00:29:37.530 --> 00:29:38.160
Right, yeah.
00:29:38.160 --> 00:29:40.230
And we can also project it into a
00:29:40.230 --> 00:29:41.810
higher dimensional space, for example,
00:29:41.810 --> 00:29:43.300
where it is linearly separable.
00:29:44.200 --> 00:29:45.666
Exactly those are the two.
00:29:45.666 --> 00:29:47.950
I think there's either 2 valid answers
00:29:47.950 --> 00:29:48.750
that I can think of.
00:29:49.900 --> 00:29:52.720
So for example, if we were to use polar
00:29:52.720 --> 00:29:56.040
coordinates, then we could represent
00:29:56.040 --> 00:29:59.273
instead of the like position on the X&Y
00:29:59.273 --> 00:30:01.190
axis or X1 and X2 axis.
00:30:01.910 --> 00:30:03.770
We could represent the distance and
00:30:03.770 --> 00:30:05.550
angle of each point from the center.
00:30:06.220 --> 00:30:08.980
And then here's that new coordinate
00:30:08.980 --> 00:30:09.350
space.
00:30:09.350 --> 00:30:11.300
And then this is a really easy like
00:30:11.300 --> 00:30:12.120
linear decision.
00:30:12.860 --> 00:30:14.440
So that's one way to solve it.
00:30:16.250 --> 00:30:18.520
Another way is that we can map the data
00:30:18.520 --> 00:30:21.520
into another higher dimensional space S
00:30:21.520 --> 00:30:23.550
if I instead represent instead of
00:30:23.550 --> 00:30:25.920
representing X1 and X2 directly.
00:30:25.920 --> 00:30:30.209
If I represent X1 squared and X2
00:30:30.209 --> 00:30:33.620
squared and the X1 times X2.
00:30:34.450 --> 00:30:35.960
Sqrt 2.
00:30:36.180 --> 00:30:38.580
Come it's helpful in the in some math
00:30:38.580 --> 00:30:39.240
later.
00:30:39.240 --> 00:30:41.830
If I represent these three coordinates
00:30:41.830 --> 00:30:44.680
instead, then it gets mapped as is
00:30:44.680 --> 00:30:47.545
shown in this 3D plot, and now there's
00:30:47.545 --> 00:30:51.020
a linear like a plane boundary that can
00:30:51.020 --> 00:30:54.040
separate the circles from the
00:30:54.040 --> 00:30:54.630
triangles.
00:30:55.490 --> 00:30:57.110
So this also works right?
00:30:57.110 --> 00:30:57.920
Two ways to do it.
00:30:57.920 --> 00:31:00.270
I can change the features or map into a
00:31:00.270 --> 00:31:01.380
higher dimensional space.
00:31:04.820 --> 00:31:07.180
So if I wanted to so I can write this
00:31:07.180 --> 00:31:09.740
as I have some kind of transformation
00:31:09.740 --> 00:31:12.190
on my input features and then given
00:31:12.190 --> 00:31:13.730
that transformation I then have a
00:31:13.730 --> 00:31:16.510
linear model and I can solve that using
00:31:16.510 --> 00:31:17.860
an SVM if I want.
00:31:24.020 --> 00:31:27.569
So if I'm representing this in the
00:31:27.570 --> 00:31:30.130
directly in the primal, then I can say
00:31:30.130 --> 00:31:33.090
that I just map my original features to
00:31:33.090 --> 00:31:34.970
my new features through this fee.
00:31:34.970 --> 00:31:37.120
Just some feature function.
00:31:37.980 --> 00:31:40.220
And then I solve for my weights in the
00:31:40.220 --> 00:31:41.030
new space.
00:31:42.030 --> 00:31:43.540
Sometimes though, in order to make the
00:31:43.540 --> 00:31:45.225
data linearly separable you might have
00:31:45.225 --> 00:31:47.050
to map into a very high dimensional
00:31:47.050 --> 00:31:47.480
space.
00:31:47.480 --> 00:31:50.390
So here like doing this trick where I
00:31:50.390 --> 00:31:53.370
look at the squares and then the
00:31:53.370 --> 00:31:55.390
product of the individual variables
00:31:55.390 --> 00:31:57.510
only went from 2 to 3 dimensions.
00:31:57.510 --> 00:31:59.162
But if I had started with 1000
00:31:59.162 --> 00:32:01.510
dimensions and I was like looking at
00:32:01.510 --> 00:32:03.666
all products of pairs of variables,
00:32:03.666 --> 00:32:05.292
this would become very high
00:32:05.292 --> 00:32:05.680
dimensional.
00:32:07.400 --> 00:32:08.880
So I might want to avoid that.
00:32:10.320 --> 00:32:12.050
So we can use the dual and I'm not
00:32:12.050 --> 00:32:13.690
going to step through the equations,
00:32:13.690 --> 00:32:16.199
but it's just showing that in the dual,
00:32:16.200 --> 00:32:18.750
since we're before you had a decision
00:32:18.750 --> 00:32:20.850
in terms of a dot product of original
00:32:20.850 --> 00:32:22.960
features, now it's a dot product of the
00:32:22.960 --> 00:32:24.060
transform features.
00:32:24.680 --> 00:32:26.280
So it's just the transformed features
00:32:26.280 --> 00:32:28.180
transpose times the other transform
00:32:28.180 --> 00:32:28.650
features.
00:32:32.240 --> 00:32:35.300
And sometimes we don't even need to
00:32:35.300 --> 00:32:37.300
compute the transformed features.
00:32:37.300 --> 00:32:38.970
All we really need at the end of the
00:32:38.970 --> 00:32:41.022
day is this kernel function.
00:32:41.022 --> 00:32:43.410
The kernel is a similarity function.
00:32:43.410 --> 00:32:45.192
It's a certain kind of similarity
00:32:45.192 --> 00:32:48.860
function that defines how similar to
00:32:48.860 --> 00:32:49.790
feature vectors are.
00:32:50.510 --> 00:32:52.710
So I could compute it explicitly.
00:32:53.920 --> 00:32:56.120
By transforming the features and taking
00:32:56.120 --> 00:32:57.740
their dot product and then I could
00:32:57.740 --> 00:32:59.560
store this kernel value for all my
00:32:59.560 --> 00:33:01.655
pairs of features in the training set,
00:33:01.655 --> 00:33:02.900
for example, and then do my
00:33:02.900 --> 00:33:03.680
optimization.
00:33:04.330 --> 00:33:05.980
I don't necessarily need to compute it
00:33:05.980 --> 00:33:08.142
every time, and sometimes I don't need
00:33:08.142 --> 00:33:09.740
to compute it as at all.
00:33:11.500 --> 00:33:12.930
An example where I don't need to
00:33:12.930 --> 00:33:15.150
compute it is in this case where I was
00:33:15.150 --> 00:33:17.230
looking at the square of the individual
00:33:17.230 --> 00:33:17.970
variables.
00:33:18.610 --> 00:33:20.750
And the product of pairs of variables.
00:33:22.140 --> 00:33:25.190
You can show that if you like, do this
00:33:25.190 --> 00:33:27.920
multiplication of these two different
00:33:27.920 --> 00:33:29.830
feature vectors X&Z.
00:33:31.090 --> 00:33:32.823
Then and you expand it.
00:33:32.823 --> 00:33:34.970
Then you can see that it actually ends
00:33:34.970 --> 00:33:39.575
up being that the product of this Phi
00:33:39.575 --> 00:33:42.410
of X times Phi of Z.
00:33:43.260 --> 00:33:46.422
Is equal to the square of the dot
00:33:46.422 --> 00:33:46.780
product.
00:33:46.780 --> 00:33:49.473
So you can get the same benefit just by
00:33:49.473 --> 00:33:50.837
squaring the dot product.
00:33:50.837 --> 00:33:53.150
And you can compute the similarity just
00:33:53.150 --> 00:33:55.440
by squaring the dot product instead of
00:33:55.440 --> 00:33:56.650
needing the map into the higher
00:33:56.650 --> 00:33:58.660
dimensional space and then taking the
00:33:58.660 --> 00:33:59.230
dot product.
00:34:00.400 --> 00:34:02.120
So if you had like a very high
00:34:02.120 --> 00:34:03.540
dimensional feature, this would save a
00:34:03.540 --> 00:34:04.230
lot of time.
00:34:04.230 --> 00:34:07.340
You wouldn't need to compute a million
00:34:07.340 --> 00:34:10.910
dimensional upper upper D feature.
00:34:13.930 --> 00:34:15.680
And yeah.
00:34:16.550 --> 00:34:18.310
So one thing to note though, is that
00:34:18.310 --> 00:34:19.840
because you're learning in terms of the
00:34:19.840 --> 00:34:22.950
distance of pairs of examples, the
00:34:22.950 --> 00:34:24.760
optimization tends to be pretty slow
00:34:24.760 --> 00:34:26.389
for kernel methods, at least in the
00:34:26.390 --> 00:34:27.730
traditional kernel methods.
00:34:28.440 --> 00:34:30.520
There's the algorithm that Austria is a
00:34:30.520 --> 00:34:32.710
lot faster for kernels, although I'm
00:34:32.710 --> 00:34:35.050
not going to go into depth for its
00:34:35.050 --> 00:34:35.900
kernelized version.
00:34:35.900 --> 00:34:36.140
Yep.
00:34:39.220 --> 00:34:40.700
Gives us a vector.
00:34:42.920 --> 00:34:45.130
X transpose times Z.
00:34:46.120 --> 00:34:49.250
Z This one that gives us a scalar
00:34:49.250 --> 00:34:51.760
because and Z are the same length,
00:34:51.760 --> 00:34:53.000
they're just two different feature
00:34:53.000 --> 00:34:53.570
vectors.
00:34:54.580 --> 00:34:57.028
And so they're both like say north by
00:34:57.028 --> 00:34:57.314
one.
00:34:57.314 --> 00:34:59.430
So then I have a one by North Times
00:34:59.430 --> 00:35:02.490
north by one gives me a 1 by 1.
00:35:04.390 --> 00:35:05.942
Yeah, so it's a dot product.
00:35:05.942 --> 00:35:08.740
So that dot product of two vectors
00:35:08.740 --> 00:35:10.490
gives you just a single value.
00:35:14.290 --> 00:35:16.340
So there's various kinds of kernels
00:35:16.340 --> 00:35:17.260
that people use.
00:35:17.260 --> 00:35:18.400
Polynomial.
00:35:19.430 --> 00:35:23.005
The one we talked about Gaussian, which
00:35:23.005 --> 00:35:25.610
is where you say that the similarity is
00:35:25.610 --> 00:35:28.630
based on how the squared distance
00:35:28.630 --> 00:35:30.060
between two feature vectors.
00:35:31.670 --> 00:35:32.360
And.
00:35:33.050 --> 00:35:34.730
And they can all just be used in the
00:35:34.730 --> 00:35:37.440
same way by computing the kernel value.
00:35:37.440 --> 00:35:39.100
In some cases you might compute
00:35:39.100 --> 00:35:40.700
explicitly, like for the Gaussian
00:35:40.700 --> 00:35:42.726
kernel and other places, and other
00:35:42.726 --> 00:35:44.550
cases there's a shortcut for the
00:35:44.550 --> 00:35:45.170
polynomial.
00:35:46.800 --> 00:35:49.010
But you just plug in your kernel
00:35:49.010 --> 00:35:50.190
function and then you can do this
00:35:50.190 --> 00:35:51.040
optimization.
00:35:52.850 --> 00:35:54.760
So I'm going to talk about optimization
00:35:54.760 --> 00:35:56.800
a little bit later, so I just want to
00:35:56.800 --> 00:35:58.430
show a couple of examples of how the
00:35:58.430 --> 00:36:00.410
decision boundary can be affected by
00:36:00.410 --> 00:36:02.090
some of the SVM parameters.
00:36:02.790 --> 00:36:05.910
So one of the parameters is CC is like.
00:36:05.910 --> 00:36:07.660
How important is it to make sure that
00:36:07.660 --> 00:36:10.625
every example is like outside the
00:36:10.625 --> 00:36:11.779
margin or on the margin?
00:36:12.530 --> 00:36:14.950
If it's Infinity, then you're forcing
00:36:14.950 --> 00:36:16.020
a, correct?
00:36:16.020 --> 00:36:18.630
You're forcing that everything has a
00:36:18.630 --> 00:36:20.030
margin of at least one.
00:36:20.810 --> 00:36:22.750
And so I wouldn't even be able to solve
00:36:22.750 --> 00:36:24.610
it if I were doing a linear classifier.
00:36:24.610 --> 00:36:27.556
But in this case it's a RBF classifier
00:36:27.556 --> 00:36:30.060
RBF kernel, which means that the
00:36:30.060 --> 00:36:31.110
distance is defined.
00:36:31.110 --> 00:36:32.920
The distance between examples is
00:36:32.920 --> 00:36:35.510
defined as like this squared distance
00:36:35.510 --> 00:36:37.160
divided by some Sigma.
00:36:38.040 --> 00:36:40.390
Sigma squared, so in this case I can
00:36:40.390 --> 00:36:41.990
linearly separate it with the RBF
00:36:41.990 --> 00:36:43.490
kernel and I get this function.
00:36:44.140 --> 00:36:48.490
If I reduce C then I start to get I get
00:36:48.490 --> 00:36:51.300
some an additional sample that is
00:36:51.300 --> 00:36:53.880
within the margin over here, but on
00:36:53.880 --> 00:36:55.885
average examples are further from the
00:36:55.885 --> 00:36:57.260
margin because I've relaxed my
00:36:57.260 --> 00:36:57.970
constraints.
00:36:57.970 --> 00:36:59.840
So sometimes you can get a better
00:36:59.840 --> 00:37:02.820
classifier by you don't always want to
00:37:02.820 --> 00:37:05.140
have C equal to Infinity or force that
00:37:05.140 --> 00:37:06.970
everything is outside the margin, even
00:37:06.970 --> 00:37:07.860
if it's possible.
00:37:09.610 --> 00:37:10.715
Often you have to optimize.
00:37:10.715 --> 00:37:12.700
You have to do like some kind of cross
00:37:12.700 --> 00:37:14.710
validation to choose C and that's one
00:37:14.710 --> 00:37:16.330
of the things that I always hated about
00:37:16.330 --> 00:37:18.571
SVMS because they can take a while to
00:37:18.571 --> 00:37:19.770
optimize and you have to do that
00:37:19.770 --> 00:37:20.130
search.
00:37:22.990 --> 00:37:27.090
So the if you relax, even more so now
00:37:27.090 --> 00:37:28.215
there's like a very weak penalty.
00:37:28.215 --> 00:37:29.860
So now you have lots of things within
00:37:29.860 --> 00:37:30.390
the margin.
00:37:32.280 --> 00:37:34.499
Then the other parameter, your kernel
00:37:34.500 --> 00:37:37.570
sometimes has parameters, so the RBF
00:37:37.570 --> 00:37:40.630
kernel is how sharp your distance
00:37:40.630 --> 00:37:41.690
function is.
00:37:41.690 --> 00:37:43.190
So if Sigma is.
00:37:43.470 --> 00:37:47.625
A Sigma is 1 then whatever, it's one.
00:37:47.625 --> 00:37:50.240
If Sigma Sigma goes closer to zero
00:37:50.240 --> 00:37:53.440
though, your RBF kernel becomes more a
00:37:53.440 --> 00:37:55.165
nearest neighbor classifier, because if
00:37:55.165 --> 00:37:56.739
Sigma is really close to 0.
00:37:57.700 --> 00:37:59.730
Then it means that an example that
00:37:59.730 --> 00:38:01.760
you're really close to.
00:38:01.760 --> 00:38:03.857
Only if you're super close to an
00:38:03.857 --> 00:38:06.459
example will it have a will it have a
00:38:06.460 --> 00:38:08.970
high similarity, and examples that are
00:38:08.970 --> 00:38:11.035
further away will have much lower
00:38:11.035 --> 00:38:11.540
similarity.
00:38:12.360 --> 00:38:14.080
So you can see that with Sigma equals
00:38:14.080 --> 00:38:16.010
one you just fit like these circular
00:38:16.010 --> 00:38:17.090
decision functions.
00:38:17.820 --> 00:38:19.770
As Sigma gets smaller, it starts to
00:38:19.770 --> 00:38:21.680
become like a little bit more wobbly.
00:38:22.440 --> 00:38:24.050
This is the this is the decision
00:38:24.050 --> 00:38:25.960
boundary, this solid line, in case
00:38:25.960 --> 00:38:27.630
that's not clear, with the green on one
00:38:27.630 --> 00:38:29.310
side and the yellow on the other side.
00:38:30.140 --> 00:38:32.459
And then as it gets smaller, then it
00:38:32.460 --> 00:38:33.800
starts to become like a nearest
00:38:33.800 --> 00:38:34.670
neighbor classifier.
00:38:34.670 --> 00:38:36.370
So almost everything is a support
00:38:36.370 --> 00:38:38.140
vector except for the very easiest
00:38:38.140 --> 00:38:40.429
points on the interior here and the
00:38:40.430 --> 00:38:41.110
decision boundary.
00:38:41.110 --> 00:38:43.050
You can start to become really
00:38:43.050 --> 00:38:45.935
arbitrarily complicated, just like just
00:38:45.935 --> 00:38:47.329
like a nearest neighbor.
00:38:48.570 --> 00:38:49.150
Question.
00:38:50.520 --> 00:38:51.895
What?
00:38:51.895 --> 00:38:54.120
So yeah, good question.
00:38:54.120 --> 00:38:55.320
So Sigma is in.
00:38:55.320 --> 00:38:57.750
It's from this equation here where I
00:38:57.750 --> 00:39:00.720
say that the similarity of two examples
00:39:00.720 --> 00:39:04.350
is their distance, their L2 distance
00:39:04.350 --> 00:39:06.140
squared divided by two Sigma.
00:39:06.980 --> 00:39:07.473
Squared.
00:39:07.473 --> 00:39:09.930
So if Sigma is really high, then it
00:39:09.930 --> 00:39:11.500
means that my similarity falls off
00:39:11.500 --> 00:39:14.490
slowly as two examples get further away
00:39:14.490 --> 00:39:15.620
in feature space.
00:39:15.620 --> 00:39:18.050
And if it's really small then the
00:39:18.050 --> 00:39:20.210
similarity drops off really quickly.
00:39:20.210 --> 00:39:22.120
So if it's like close to 0.
00:39:22.970 --> 00:39:25.380
Then the closest example will just be
00:39:25.380 --> 00:39:27.390
way, way way closer than any of the
00:39:27.390 --> 00:39:28.190
other examples.
00:39:29.690 --> 00:39:31.070
According to the similarity measure.
00:39:32.440 --> 00:39:32.980
Yeah.
00:39:33.240 --> 00:39:35.970
The previous example we are discussing
00:39:35.970 --> 00:39:37.700
projecting features to higher
00:39:37.700 --> 00:39:38.580
dimensions, right?
00:39:38.580 --> 00:39:41.730
Yeah, so how can we be sure this is the
00:39:41.730 --> 00:39:43.650
minimum dimension we required to
00:39:43.650 --> 00:39:44.380
classify that?
00:39:45.130 --> 00:39:46.810
Particular features are example space
00:39:46.810 --> 00:39:47.240
we have.
00:39:49.810 --> 00:39:50.980
Sorry, can you ask it again?
00:39:50.980 --> 00:39:52.100
I'm not sure if I got it.
00:39:52.590 --> 00:39:55.120
Understand something so we know that we
00:39:55.120 --> 00:39:56.250
need to project it in different
00:39:56.250 --> 00:39:58.610
dimensions to classify that properly.
00:39:58.610 --> 00:40:01.210
In the previous example like so we said
00:40:01.210 --> 00:40:02.100
we discussed right?
00:40:02.100 --> 00:40:04.486
So how can we very sure what is the
00:40:04.486 --> 00:40:05.850
minimum our minimum dimension?
00:40:05.850 --> 00:40:08.659
So the question is how do you know what
00:40:08.660 --> 00:40:10.700
kernel you should use or how high you
00:40:10.700 --> 00:40:12.400
should project the data right?
00:40:12.980 --> 00:40:15.750
Yeah, that that's a problem that you
00:40:15.750 --> 00:40:17.523
don't really know, so you have to try.
00:40:17.523 --> 00:40:19.350
You can try different things and then
00:40:19.350 --> 00:40:21.200
you use your validation set to choose
00:40:21.200 --> 00:40:21.950
the best model.
00:40:22.930 --> 00:40:26.350
But that's a downside of SVMS that
00:40:26.350 --> 00:40:29.960
since the optimization for big data set
00:40:29.960 --> 00:40:32.700
can be pretty slow if you're using a
00:40:32.700 --> 00:40:33.120
kernel.
00:40:33.790 --> 00:40:36.000
And so it can be very time consuming to
00:40:36.000 --> 00:40:37.410
try to search through all the different
00:40:37.410 --> 00:40:38.620
parameters and different types of
00:40:38.620 --> 00:40:39.700
kernels that you could use.
00:40:41.420 --> 00:40:44.310
There's another trick which you could
00:40:44.310 --> 00:40:46.230
do, which is like you take a random
00:40:46.230 --> 00:40:47.150
forest.
00:40:48.650 --> 00:40:51.300
And you take the leaf node that each
00:40:51.300 --> 00:40:53.632
data point falls into as a binary
00:40:53.632 --> 00:40:55.690
variable, so it'll be a sparse binary
00:40:55.690 --> 00:40:56.140
variable.
00:40:56.920 --> 00:40:58.230
And then you can apply your linear
00:40:58.230 --> 00:40:59.690
classifier to it.
00:40:59.690 --> 00:41:01.480
So then you're like mapping it into
00:41:01.480 --> 00:41:03.650
this high dimensional space that kind
00:41:03.650 --> 00:41:05.540
of takes into account the feature
00:41:05.540 --> 00:41:08.800
structure and where the data should be
00:41:08.800 --> 00:41:10.190
like pretty linearly separable.
00:41:16.350 --> 00:41:19.396
So in summary of the kernels for
00:41:19.396 --> 00:41:21.560
kernels you can learn the classifiers
00:41:21.560 --> 00:41:23.120
in high dimensional feature spaces
00:41:23.120 --> 00:41:24.705
without actually having to map them
00:41:24.705 --> 00:41:25.090
there.
00:41:25.090 --> 00:41:26.380
We did for the polynomial.
00:41:26.380 --> 00:41:28.898
The data can be linearly separable in
00:41:28.898 --> 00:41:30.229
the high dimensional space.
00:41:30.230 --> 00:41:31.796
Even if it weren't highly separable,
00:41:31.796 --> 00:41:34.029
wasn't wasn't there weren't actually
00:41:34.029 --> 00:41:36.150
separable in the original feature
00:41:36.150 --> 00:41:36.520
space.
00:41:37.530 --> 00:41:40.830
And you can use the kernel for an SVM,
00:41:40.830 --> 00:41:42.760
but the concept of kernels it's also
00:41:42.760 --> 00:41:44.620
used in other learning algorithms, so
00:41:44.620 --> 00:41:46.200
it's just like a general concept worth
00:41:46.200 --> 00:41:46.710
knowing.
00:41:48.530 --> 00:41:51.890
All right, so it's time for a stretch
00:41:51.890 --> 00:41:52.750
break.
00:41:53.910 --> 00:41:56.160
And you can think about this question
00:41:56.160 --> 00:41:58.130
if you were to remove a support vector
00:41:58.130 --> 00:41:59.600
from the training set with the decision
00:41:59.600 --> 00:42:00.560
boundary change.
00:42:01.200 --> 00:42:03.799
And then after 2 minutes I'll give the
00:42:03.800 --> 00:42:06.150
answer to that and then I'll give an
00:42:06.150 --> 00:42:08.360
application example and talk about the
00:42:08.360 --> 00:42:09.380
Pegasus algorithm.
00:44:27.710 --> 00:44:30.520
So what's the answer to this?
00:44:30.520 --> 00:44:32.510
If I were to remove one of these
00:44:32.510 --> 00:44:35.240
examples, here is my decision boundary.
00:44:35.240 --> 00:44:36.540
You're going to change or not?
00:44:38.300 --> 00:44:40.580
Yeah, it will change right?
00:44:40.580 --> 00:44:42.120
If I moved any of the other ones, it
00:44:42.120 --> 00:44:42.760
wouldn't change.
00:44:42.760 --> 00:44:43.979
But if I remove one of the support
00:44:43.980 --> 00:44:45.655
vectors it's going to change because my
00:44:45.655 --> 00:44:46.315
support is changing.
00:44:46.315 --> 00:44:49.144
So if I remove this for example, then I
00:44:49.144 --> 00:44:51.328
think the line would like tilt this way
00:44:51.328 --> 00:44:53.944
so that it would depend on that X and
00:44:53.944 --> 00:44:54.651
this X.
00:44:54.651 --> 00:44:58.186
And if I remove this O then I think it
00:44:58.186 --> 00:45:00.240
would shift down this way so that it
00:45:00.240 --> 00:45:02.020
depends on this O and these X's.
00:45:02.660 --> 00:45:04.970
Birds find some boundary where three of
00:45:04.970 --> 00:45:06.920
those points are equidistant, 2 on one
00:45:06.920 --> 00:45:07.840
side and 1 on the other.
00:45:12.630 --> 00:45:14.120
Alright, so I'm going to give you an
00:45:14.120 --> 00:45:15.920
example of how it's used, and you may
00:45:15.920 --> 00:45:17.862
notice that almost all the examples are
00:45:17.862 --> 00:45:19.570
computer vision, and that's because I
00:45:19.570 --> 00:45:21.431
know a lot of computer vision and so
00:45:21.431 --> 00:45:22.700
that's always what occurs to me.
00:45:24.630 --> 00:45:29.090
But this is an object detection case,
00:45:29.090 --> 00:45:29.760
so.
00:45:30.620 --> 00:45:33.770
The method here it's like called
00:45:33.770 --> 00:45:35.790
sliding window object detection which
00:45:35.790 --> 00:45:37.370
you can visualize it as like you have
00:45:37.370 --> 00:45:38.853
some image and you take a little window
00:45:38.853 --> 00:45:41.230
and you slide it across the image and
00:45:41.230 --> 00:45:43.250
you extract a patch at each position.
00:45:44.180 --> 00:45:45.990
And then you rescale the image and do
00:45:45.990 --> 00:45:46.550
it again.
00:45:46.550 --> 00:45:48.467
So you end up with like a whole.
00:45:48.467 --> 00:45:50.290
You turn the image into a whole bunch
00:45:50.290 --> 00:45:53.290
of different patches of the same size.
00:45:54.400 --> 00:45:56.830
After rescaling them, but that
00:45:56.830 --> 00:45:59.690
correspond to different different
00:45:59.690 --> 00:46:01.650
overlapping patches at different
00:46:01.650 --> 00:46:03.170
positions and scales in the original
00:46:03.170 --> 00:46:03.550
image.
00:46:04.270 --> 00:46:06.360
And then for each of those patches you
00:46:06.360 --> 00:46:08.840
have to classify it as being the object
00:46:08.840 --> 00:46:10.470
of interest or not, in this case of
00:46:10.470 --> 00:46:11.120
pedestrian.
00:46:12.070 --> 00:46:14.830
Where pedestrian just means person.
00:46:14.830 --> 00:46:16.970
These aren't actually necessarily
00:46:16.970 --> 00:46:18.480
pedestrians like this guy's not on the
00:46:18.480 --> 00:46:19.000
road, but.
00:46:19.960 --> 00:46:20.846
This person.
00:46:20.846 --> 00:46:24.290
So these are all examples of patches
00:46:24.290 --> 00:46:26.126
that you would want to classify as a
00:46:26.126 --> 00:46:26.464
person.
00:46:26.464 --> 00:46:28.490
So you can see it's kind of difficult
00:46:28.490 --> 00:46:30.190
because there could be lots of
00:46:30.190 --> 00:46:31.880
different backgrounds or other people
00:46:31.880 --> 00:46:34.030
in the way and you have to distinguish
00:46:34.030 --> 00:46:36.580
it from like a fire hydrant that's like
00:46:36.580 --> 00:46:37.953
pretty far away and looks kind of
00:46:37.953 --> 00:46:39.420
person like or a lamp post.
00:46:42.390 --> 00:46:45.400
This method is to like extract
00:46:45.400 --> 00:46:46.330
features.
00:46:46.330 --> 00:46:48.060
Basically you normalize the colors,
00:46:48.060 --> 00:46:49.730
compute gradients, compute the gradient
00:46:49.730 --> 00:46:50.340
orientation.
00:46:50.340 --> 00:46:51.550
I'll show you an illustration in the
00:46:51.550 --> 00:46:53.760
next slide and then you apply a linear
00:46:53.760 --> 00:46:54.290
SVM.
00:46:55.040 --> 00:46:56.450
And so for each of these windows you
00:46:56.450 --> 00:46:57.902
want to say it's a person or not a
00:46:57.902 --> 00:46:58.098
person.
00:46:58.098 --> 00:46:59.840
So you train on some training set of
00:46:59.840 --> 00:47:01.400
images where you have some people that
00:47:01.400 --> 00:47:02.100
are annotated.
00:47:02.770 --> 00:47:04.650
And then you test on some held out set.
00:47:06.300 --> 00:47:09.515
So this is the feature representation.
00:47:09.515 --> 00:47:11.920
It's basically like where are the edges
00:47:11.920 --> 00:47:14.170
and the image and the patch and how
00:47:14.170 --> 00:47:15.470
strong are they and what are their
00:47:15.470 --> 00:47:16.185
orientations.
00:47:16.185 --> 00:47:18.460
It's called a hog or histogram of
00:47:18.460 --> 00:47:20.460
gradients representation.
00:47:21.200 --> 00:47:23.930
And this paper is cited over 40,000
00:47:23.930 --> 00:47:24.610
times.
00:47:24.610 --> 00:47:26.670
It's mostly for the hog features, but
00:47:26.670 --> 00:47:28.790
it was also the most effective person
00:47:28.790 --> 00:47:29.840
detector for a while.
00:47:34.610 --> 00:47:38.876
So it it's very effective.
00:47:38.876 --> 00:47:42.730
So these plots are the X axis is the
00:47:42.730 --> 00:47:44.432
number of false positives per window.
00:47:44.432 --> 00:47:47.180
So it's a chance that you misclassify
00:47:47.180 --> 00:47:49.040
one of these windows as a person when
00:47:49.040 --> 00:47:50.117
it's not really a person.
00:47:50.117 --> 00:47:52.460
It's like a fire hydrant or random
00:47:52.460 --> 00:47:53.520
leaves or something else.
00:47:54.660 --> 00:47:58.600
X axis, Y axis is the miss rate, which
00:47:58.600 --> 00:48:01.480
is the number of true people that you
00:48:01.480 --> 00:48:02.440
fail to detect.
00:48:03.080 --> 00:48:05.160
So the fact that it's way down here
00:48:05.160 --> 00:48:07.560
basically means that it never makes any
00:48:07.560 --> 00:48:09.630
mistakes on this data set, so it can
00:48:09.630 --> 00:48:13.110
classify it gets 99.8% of the fines,
00:48:13.110 --> 00:48:16.730
99.8% of the people, and almost never
00:48:16.730 --> 00:48:17.860
has false positives.
00:48:18.900 --> 00:48:20.400
That was on this MIT database.
00:48:21.040 --> 00:48:23.154
Then there's another data set which was
00:48:23.154 --> 00:48:25.140
like more, which was harder.
00:48:25.140 --> 00:48:27.490
Those were the examples I showed of St.
00:48:27.490 --> 00:48:29.170
scenes and more crowded scenes.
00:48:29.860 --> 00:48:32.870
And they're the previous approaches had
00:48:32.870 --> 00:48:35.230
like pretty high false positive rates.
00:48:35.230 --> 00:48:38.340
So as a rule of thumb I would say
00:48:38.340 --> 00:48:43.090
there's typically about 10,000 windows
00:48:43.090 --> 00:48:43.780
per image.
00:48:44.480 --> 00:48:46.427
So if you have like a false positive
00:48:46.427 --> 00:48:48.755
rate of 10 to the -, 4, that means that
00:48:48.755 --> 00:48:50.555
you make one mistake on every single
00:48:50.555 --> 00:48:50.920
image.
00:48:50.920 --> 00:48:51.650
On average.
00:48:51.650 --> 00:48:53.400
You like think that there's one person
00:48:53.400 --> 00:48:55.080
where there isn't anybody on average
00:48:55.080 --> 00:48:55.985
once per image.
00:48:55.985 --> 00:48:57.410
So that's kind of a that's an
00:48:57.410 --> 00:48:58.490
unacceptable rate.
00:48:59.950 --> 00:49:02.723
But this method is able to get like 10
00:49:02.723 --> 00:49:06.380
to the -, 6 which is a pretty good rate
00:49:06.380 --> 00:49:09.230
and still find like 70% of the people.
00:49:10.030 --> 00:49:11.400
So these like.
00:49:12.320 --> 00:49:14.665
These curves that are clustered here
00:49:14.665 --> 00:49:17.020
are all different SVMS.
00:49:17.020 --> 00:49:20.970
Linear SVMS, they also do.
00:49:21.040 --> 00:49:21.800
00:49:22.760 --> 00:49:23.060
Weight.
00:49:23.060 --> 00:49:23.930
Linear.
00:49:23.930 --> 00:49:25.860
Yeah, so the black one here is a
00:49:25.860 --> 00:49:28.110
kernelized SVM, which performs very
00:49:28.110 --> 00:49:30.130
similarly, but takes a lot longer to
00:49:30.130 --> 00:49:32.340
train and do inference, so it wouldn't
00:49:32.340 --> 00:49:32.890
be referred.
00:49:33.880 --> 00:49:35.790
And then the other previous approaches
00:49:35.790 --> 00:49:36.870
are doing worse.
00:49:36.870 --> 00:49:38.500
They have like higher false positives
00:49:38.500 --> 00:49:39.960
rates for the same detection rate.
00:49:42.860 --> 00:49:44.470
So that was just that was just one
00:49:44.470 --> 00:49:46.832
example, but as I said like SVMS where
00:49:46.832 --> 00:49:49.080
the dominant I think the most commonly
00:49:49.080 --> 00:49:50.903
used, I wouldn't say dominant, but most
00:49:50.903 --> 00:49:53.510
commonly used classifier for several
00:49:53.510 --> 00:49:53.930
years.
00:49:56.330 --> 00:49:58.440
So SVMS are good broadly applicable
00:49:58.440 --> 00:49:58.782
classifier.
00:49:58.782 --> 00:50:00.780
They have a strong foundation in
00:50:00.780 --> 00:50:01.970
statistical learning theory.
00:50:01.970 --> 00:50:04.000
They work even if you have a lot of
00:50:04.000 --> 00:50:05.480
weak features.
00:50:05.480 --> 00:50:08.400
You do have to tune the parameters like
00:50:08.400 --> 00:50:10.470
C and that can be time consuming.
00:50:11.160 --> 00:50:13.390
And if you're using nonlinear SVM, then
00:50:13.390 --> 00:50:14.817
you have to decide what kernel function
00:50:14.817 --> 00:50:16.560
you're going to use, which may involve
00:50:16.560 --> 00:50:19.010
even more tuning in it, and it means
00:50:19.010 --> 00:50:20.150
that it's going to be a slow
00:50:20.150 --> 00:50:21.940
optimization and slower inference.
00:50:22.860 --> 00:50:24.680
The main negatives of SVM, the
00:50:24.680 --> 00:50:25.550
downsides.
00:50:25.550 --> 00:50:27.160
It doesn't have feature learning as
00:50:27.160 --> 00:50:29.580
part of the framework, where trees for
00:50:29.580 --> 00:50:30.750
example, you're kind of learning
00:50:30.750 --> 00:50:32.620
features and for neural Nets you are as
00:50:32.620 --> 00:50:32.930
well.
00:50:33.770 --> 00:50:38.430
And it also can took could be very slow
00:50:38.430 --> 00:50:39.010
to train.
00:50:40.290 --> 00:50:42.930
Until Pegasus, which is the next thing
00:50:42.930 --> 00:50:44.510
that I'm talking about, South, this was
00:50:44.510 --> 00:50:46.660
like a much faster and simpler way to
00:50:46.660 --> 00:50:47.790
train these algorithms.
00:50:49.220 --> 00:50:50.755
So I'm not going to talk about the bad
00:50:50.755 --> 00:50:53.270
ways or they're slow ways to optimize
00:50:53.270 --> 00:50:53.380
it.
00:50:54.360 --> 00:50:56.750
So this is so the next thing I'm going
00:50:56.750 --> 00:50:57.710
to talk about.
00:50:57.980 --> 00:51:01.350
Is called Pegasus which is how you can
00:51:01.350 --> 00:51:04.100
optimize the SVM and it stands for
00:51:04.100 --> 00:51:06.510
primal estimated subgradient solver for
00:51:06.510 --> 00:51:07.360
SVM, so.
00:51:09.020 --> 00:51:11.095
Primal because you're solving it in the
00:51:11.095 --> 00:51:12.660
primal formulation where you're
00:51:12.660 --> 00:51:14.540
minimizing the weights and the margin.
00:51:15.460 --> 00:51:16.840
Estimated because that's where you're.
00:51:17.900 --> 00:51:20.090
The subgradient is because you're going
00:51:20.090 --> 00:51:21.860
to you're going to make decisions based
00:51:21.860 --> 00:51:24.970
on a subsample of the training data.
00:51:24.970 --> 00:51:27.030
So you're trying to take a step in the
00:51:27.030 --> 00:51:29.000
right direction based on a few training
00:51:29.000 --> 00:51:31.710
examples to solver for SVM.
00:51:33.540 --> 00:51:36.790
I found out yesterday when I was look
00:51:36.790 --> 00:51:39.460
searching for the paper that Pegasus is
00:51:39.460 --> 00:51:42.260
also like an assisted suicide system in
00:51:42.260 --> 00:51:42.880
Switzerland.
00:51:42.880 --> 00:51:45.420
So it's kind of an unfortunate name,
00:51:45.420 --> 00:51:46.820
unfortunate acronym.
00:51:48.920 --> 00:51:49.520
And.
00:51:50.550 --> 00:51:54.150
So the so this is the SVM problem that
00:51:54.150 --> 00:51:56.160
we want to solve, minimize the weights
00:51:56.160 --> 00:52:00.000
and while also minimizing the hinge
00:52:00.000 --> 00:52:01.260
loss on all the samples.
00:52:02.510 --> 00:52:04.200
But we can reframe this.
00:52:04.200 --> 00:52:06.780
We can reframe it in terms of one
00:52:06.780 --> 00:52:07.110
example.
00:52:07.110 --> 00:52:09.297
So we could say, well, let's say we
00:52:09.297 --> 00:52:10.870
want to minimize the weights and we
00:52:10.870 --> 00:52:12.706
want to minimize the loss for one
00:52:12.706 --> 00:52:13.079
example.
00:52:14.410 --> 00:52:17.200
Then we can ask like how would I change
00:52:17.200 --> 00:52:19.630
the weights if that were my objective?
00:52:19.630 --> 00:52:21.897
And if you want to know how you can
00:52:21.897 --> 00:52:23.913
improve something, improve some
00:52:23.913 --> 00:52:25.230
objective with respect to some
00:52:25.230 --> 00:52:25.780
variable.
00:52:26.670 --> 00:52:27.890
Then what you do is you take the
00:52:27.890 --> 00:52:30.260
partial derivative of the objective
00:52:30.260 --> 00:52:33.330
with respect to the variable, and if
00:52:33.330 --> 00:52:35.285
you want the objective to go down, this
00:52:35.285 --> 00:52:36.440
is like a loss function.
00:52:36.440 --> 00:52:38.090
So we wanted to go down.
00:52:38.090 --> 00:52:40.763
So I want to find the derivative with
00:52:40.763 --> 00:52:42.670
respect to my variable, in this case
00:52:42.670 --> 00:52:45.680
the weights, and I want to take a small
00:52:45.680 --> 00:52:47.450
step in the negative direction of that
00:52:47.450 --> 00:52:49.262
gradient of that derivative.
00:52:49.262 --> 00:52:51.750
So that will make my objective just a
00:52:51.750 --> 00:52:52.400
little bit better.
00:52:52.400 --> 00:52:53.990
It'll make my loss a little bit lower.
00:52:56.470 --> 00:52:58.690
And if I compute the gradient of this
00:52:58.690 --> 00:53:01.440
objective with respect to West.
00:53:02.110 --> 00:53:06.610
So the gradient of West squared is just
00:53:06.610 --> 00:53:10.502
is just two WI mean and also the
00:53:10.502 --> 00:53:11.210
gradient of.
00:53:11.210 --> 00:53:13.360
Again vector math like.
00:53:13.360 --> 00:53:15.750
You might not be familiar with doing
00:53:15.750 --> 00:53:17.440
like gradients of vectors and stuff,
00:53:17.440 --> 00:53:19.350
but it often works out kind of
00:53:19.350 --> 00:53:20.800
analogous to the scalars.
00:53:20.800 --> 00:53:23.385
So the gradient of W transpose W is
00:53:23.385 --> 00:53:24.130
also W.
00:53:26.290 --> 00:53:29.515
This loss function is this margin which
00:53:29.515 --> 00:53:30.850
is just Y of.
00:53:30.850 --> 00:53:32.650
This is like a dot product W transpose
00:53:32.650 --> 00:53:33.000
X.
00:53:34.380 --> 00:53:36.690
So the gradient of this with respect to
00:53:36.690 --> 00:53:39.770
West is.
00:53:39.830 --> 00:53:41.590
Negative YX, right?
00:53:42.320 --> 00:53:45.880
And so my gradient if I've got this Max
00:53:45.880 --> 00:53:46.570
here as well.
00:53:46.570 --> 00:53:49.260
So that means that if I'm already like
00:53:49.260 --> 00:53:50.890
confidently correct, then I have no
00:53:50.890 --> 00:53:52.780
loss so my gradient is 0.
00:53:53.620 --> 00:53:55.800
If I'm not confidently correct, if I'm
00:53:55.800 --> 00:53:58.380
within the margin of 1 then I have this
00:53:58.380 --> 00:54:01.630
loss and the size of this.
00:54:03.400 --> 00:54:06.490
The size of the size of the gradient.
00:54:07.180 --> 00:54:11.210
Is just one, has a magnitude of 1 and
00:54:11.210 --> 00:54:13.750
the direction because my hinge loss has
00:54:13.750 --> 00:54:14.320
this.
00:54:15.400 --> 00:54:17.315
So the size do the hinge loss is just
00:54:17.315 --> 00:54:18.900
one because the hinge loss just has a
00:54:18.900 --> 00:54:20.250
gradient of 1, it's just a straight
00:54:20.250 --> 00:54:20.550
line.
00:54:21.620 --> 00:54:24.950
And then the of this is YX, right?
00:54:24.950 --> 00:54:28.825
The gradient of YW transpose X is YX
00:54:28.825 --> 00:54:31.797
and so I get this gradient here, which
00:54:31.797 --> 00:54:35.696
is it's a 0 if my margin is good enough
00:54:35.696 --> 00:54:36.963
and it's a one.
00:54:36.963 --> 00:54:40.300
This term is A1 if I'm under the
00:54:40.300 --> 00:54:40.630
margin.
00:54:41.520 --> 00:54:44.500
Times Y which is one or - 1 depending
00:54:44.500 --> 00:54:46.419
on the label, times X which is the
00:54:46.420 --> 00:54:47.060
feature vector.
00:54:47.900 --> 00:54:48.830
So in other words.
00:54:49.930 --> 00:54:52.720
If I'm not happy with my score right
00:54:52.720 --> 00:54:56.070
now and let's say let's say W transpose
00:54:56.070 --> 00:54:58.690
X is oh .5 and y = 1.
00:54:59.660 --> 00:55:02.116
And let's say that X is positive, then
00:55:02.116 --> 00:55:06.612
I want to increase WA bit and if I
00:55:06.612 --> 00:55:09.710
increase WA bit then I'm going to.
00:55:10.070 --> 00:55:13.230
Increase my score or increase like the
00:55:13.230 --> 00:55:16.060
output of my linear model, which will
00:55:16.060 --> 00:55:18.380
then better satisfy the margin.
00:55:21.030 --> 00:55:23.160
And then I'm going to take.
00:55:23.160 --> 00:55:25.380
So this is just the gradient here
00:55:25.380 --> 00:55:27.760
Lambda times W Plus this thing that I
00:55:27.760 --> 00:55:28.740
just talked about.
00:55:30.920 --> 00:55:32.630
So we're going to use this to do what's
00:55:32.630 --> 00:55:34.300
called gradient descent.
00:55:35.500 --> 00:55:37.820
SGD stands for stochastic gradient
00:55:37.820 --> 00:55:38.310
descent.
00:55:39.280 --> 00:55:41.050
And I'll explain what stochastic, why
00:55:41.050 --> 00:55:43.420
it's stochastic, and a little bit.
00:55:43.420 --> 00:55:45.690
But this is like a nice illustration of
00:55:45.690 --> 00:55:47.990
gradient descent, basically.
00:55:48.700 --> 00:55:50.213
You visualize.
00:55:50.213 --> 00:55:52.600
You can mentally visualize it as you've
00:55:52.600 --> 00:55:53.270
got some.
00:55:54.370 --> 00:55:56.200
You've got some surface of your loss
00:55:56.200 --> 00:55:58.070
function, so depending on what your
00:55:58.070 --> 00:55:59.630
model is, you would have different
00:55:59.630 --> 00:56:00.220
losses.
00:56:00.950 --> 00:56:02.500
And so here it's just like if your
00:56:02.500 --> 00:56:04.600
model just has two parameters, then you
00:56:04.600 --> 00:56:07.400
can visualize this as like a 3D surface
00:56:07.400 --> 00:56:09.070
where the height is your loss.
00:56:09.730 --> 00:56:13.420
And the position XY position on this is
00:56:13.420 --> 00:56:14.950
the parameters.
00:56:16.390 --> 00:56:17.730
And gradient descent, you're just
00:56:17.730 --> 00:56:19.269
trying to roll down the hill.
00:56:19.270 --> 00:56:20.590
That's why I had a ball rolling down
00:56:20.590 --> 00:56:21.950
the hill on the first slide.
00:56:22.510 --> 00:56:25.710
And you try to every position you
00:56:25.710 --> 00:56:26.990
calculate gradient.
00:56:26.990 --> 00:56:29.070
That's the direction of the slope and
00:56:29.070 --> 00:56:29.830
its speed.
00:56:30.430 --> 00:56:32.240
And then you take a little step in the
00:56:32.240 --> 00:56:34.020
direction of that gradient downward.
00:56:35.560 --> 00:56:38.370
And there's a common terms that you'll
00:56:38.370 --> 00:56:40.532
hear in this kind of optimization are
00:56:40.532 --> 00:56:43.300
like global optimum and local optimum.
00:56:43.300 --> 00:56:45.956
So a global optimum is the lowest point
00:56:45.956 --> 00:56:48.780
in the whole like surface of solutions.
00:56:49.890 --> 00:56:51.660
That's where you want to go in.
00:56:51.660 --> 00:56:54.606
A local optimum means that if you have
00:56:54.606 --> 00:56:56.960
that solution then you can't improve it
00:56:56.960 --> 00:56:58.840
by taking a small step anywhere.
00:56:58.840 --> 00:57:00.460
So you have to go up the hill before
00:57:00.460 --> 00:57:01.320
you can go down the hill.
00:57:02.030 --> 00:57:04.613
So this is a global optimum here and
00:57:04.613 --> 00:57:06.329
this is a local optimum.
00:57:06.330 --> 00:57:09.720
Now SVMS, SVMS are just like a big
00:57:09.720 --> 00:57:10.430
bowl.
00:57:10.430 --> 00:57:11.650
They are convex.
00:57:11.650 --> 00:57:13.810
It's a convex problem where they're the
00:57:13.810 --> 00:57:15.820
only local optimum is global optimum.
00:57:16.960 --> 00:57:18.620
And so with the suitable optimization
00:57:18.620 --> 00:57:20.090
algorithm you should always be able to
00:57:20.090 --> 00:57:21.540
find the best solution.
00:57:22.320 --> 00:57:25.260
But neural networks, which we'll get to
00:57:25.260 --> 00:57:28.460
later, are like really bumpy, and so
00:57:28.460 --> 00:57:29.870
the optimization is much harder.
00:57:33.810 --> 00:57:36.080
So finally, this is the Pegasus
00:57:36.080 --> 00:57:38.380
algorithm for stochastic gradient
00:57:38.380 --> 00:57:38.920
descent.
00:57:39.910 --> 00:57:40.490
And.
00:57:41.120 --> 00:57:43.309
Fortunately, it's kind of it's kind of
00:57:43.310 --> 00:57:46.490
short, it's a simple algorithm, but it
00:57:46.490 --> 00:57:47.790
takes a little bit of explanation.
00:57:48.710 --> 00:57:50.200
Just laughing because my daughter has
00:57:50.200 --> 00:57:52.720
this book, fortunately, unfortunately,
00:57:52.720 --> 00:57:53.360
where?
00:57:54.040 --> 00:57:57.710
Fortunately, unfortunately, the he gets
00:57:57.710 --> 00:57:58.100
an airplane.
00:57:58.100 --> 00:58:00.041
The engine exploded, fortunately at a
00:58:00.041 --> 00:58:00.353
parachute.
00:58:00.353 --> 00:58:02.552
Unfortunately there is a hole in the
00:58:02.552 --> 00:58:02.933
parachute.
00:58:02.933 --> 00:58:05.110
Fortunately there is a haystack below
00:58:05.110 --> 00:58:05.380
him.
00:58:05.380 --> 00:58:07.500
Unfortunately there is a pitchfork in
00:58:07.500 --> 00:58:08.080
haystack.
00:58:08.080 --> 00:58:09.490
Just goes on like that for the whole
00:58:09.490 --> 00:58:10.010
book.
00:58:10.990 --> 00:58:12.700
It's really funny, so fortunately this
00:58:12.700 --> 00:58:13.420
is short.
00:58:13.420 --> 00:58:15.490
Unfortunately, it still may be hard to
00:58:15.490 --> 00:58:16.190
understand.
00:58:16.990 --> 00:58:18.760
And so the.
00:58:18.760 --> 00:58:21.250
So we have a training set here.
00:58:21.250 --> 00:58:23.280
These are the input training examples.
00:58:23.940 --> 00:58:25.950
I've got some regularization weight and
00:58:25.950 --> 00:58:27.380
I have some number of iterations that
00:58:27.380 --> 00:58:28.030
I'm going to do.
00:58:28.850 --> 00:58:30.370
And I initialize the weights to be
00:58:30.370 --> 00:58:31.120
zeros.
00:58:31.120 --> 00:58:32.630
These are the weights in my model.
00:58:33.290 --> 00:58:35.220
And then I step through each iteration.
00:58:36.070 --> 00:58:38.270
And I choose some sample.
00:58:39.280 --> 00:58:41.140
Uniformly at random, so I just choose
00:58:41.140 --> 00:58:43.170
one single training sample from my data
00:58:43.170 --> 00:58:43.480
set.
00:58:44.310 --> 00:58:48.440
And then I set my learning rate which
00:58:48.440 --> 00:58:49.100
is.
00:58:49.180 --> 00:58:49.790
00:58:52.030 --> 00:58:54.220
Or I should say, I guess that's it.
00:58:54.220 --> 00:58:55.720
So I choose some samples from my data
00:58:55.720 --> 00:58:56.220
set.
00:58:56.220 --> 00:58:57.840
Then I set my learning rate which is
00:58:57.840 --> 00:59:00.520
one over Lambda T so basically my step
00:59:00.520 --> 00:59:02.945
size is going to get smaller the more
00:59:02.945 --> 00:59:04.200
samples that I process.
00:59:06.200 --> 00:59:10.200
And if my margin is less than one, that
00:59:10.200 --> 00:59:12.330
means that I'm not happy with my score
00:59:12.330 --> 00:59:13.330
for that example.
00:59:14.120 --> 00:59:16.990
So I increment my weights by 1 minus
00:59:16.990 --> 00:59:20.828
ETA Lambda W so this is the.
00:59:20.828 --> 00:59:22.833
This part is just saying that I want my
00:59:22.833 --> 00:59:24.160
weights to get smaller in general
00:59:24.160 --> 00:59:25.760
because I'm trying to minimize the
00:59:25.760 --> 00:59:27.760
squared weights and that's based on the
00:59:27.760 --> 00:59:29.570
derivative of W transpose W.
00:59:30.480 --> 00:59:32.370
And then this part is saying I also
00:59:32.370 --> 00:59:34.180
want to improve my score for this
00:59:34.180 --> 00:59:36.110
example, so I add.
00:59:37.400 --> 00:59:44.440
I add ETA YX so if X is positive then
00:59:44.440 --> 00:59:46.712
I'm going to increase and Y is
00:59:46.712 --> 00:59:48.340
positive, then I'm going to increase
00:59:48.340 --> 00:59:50.790
the weight so that it becomes so that X
00:59:50.790 --> 00:59:51.920
becomes more positive.
00:59:52.550 --> 00:59:54.970
Is positive and Y is negative, then I'm
00:59:54.970 --> 00:59:57.438
going to decrease the weight so that so
00:59:57.438 --> 00:59:59.634
that X becomes less positive, more
00:59:59.634 --> 01:00:00.940
negative and more correct.
01:00:02.430 --> 01:00:04.410
And then if I'm happy with my score of
01:00:04.410 --> 01:00:06.830
the example, it's outside the margin YW
01:00:06.830 --> 01:00:07.750
transpose X.
01:00:08.950 --> 01:00:12.040
Is greater or equal to 1, then I only
01:00:12.040 --> 01:00:13.750
care about this regularization term, so
01:00:13.750 --> 01:00:15.010
I'm just going to make the weight a
01:00:15.010 --> 01:00:17.100
little bit smaller because I'm trying
01:00:17.100 --> 01:00:18.590
to again minimize the square of the
01:00:18.590 --> 01:00:18.850
weights.
01:00:20.220 --> 01:00:21.500
So I just that's it.
01:00:21.500 --> 01:00:23.145
I just stepped through all the
01:00:23.145 --> 01:00:23.420
examples.
01:00:23.420 --> 01:00:25.615
It's like a pretty short optimization.
01:00:25.615 --> 01:00:27.750
And what I'm doing is I'm just like
01:00:27.750 --> 01:00:30.530
incrementally trying to improve my
01:00:30.530 --> 01:00:32.479
solution for each example that I
01:00:32.480 --> 01:00:33.490
encounter.
01:00:33.490 --> 01:00:37.459
And what's not intuitive maybe is that
01:00:37.460 --> 01:00:38.810
theoretically you can show that this
01:00:38.810 --> 01:00:42.970
eventually improves gives you the best
01:00:42.970 --> 01:00:44.860
possible weights for all your examples.
01:00:47.930 --> 01:00:49.640
There's a there's another version of
01:00:49.640 --> 01:00:52.180
this where you use what's called a mini
01:00:52.180 --> 01:00:52.770
batch.
01:00:53.580 --> 01:00:55.290
We're just instead of sampling.
01:00:55.290 --> 01:00:57.165
Instead of taking one sample at a time,
01:00:57.165 --> 01:00:59.165
one training sample at a time, you take
01:00:59.165 --> 01:01:01.280
a whole set at a time of random set of
01:01:01.280 --> 01:01:01.930
examples.
01:01:03.000 --> 01:01:06.970
And then you take instead of instead of
01:01:06.970 --> 01:01:09.660
this term involving like the margin
01:01:09.660 --> 01:01:13.570
loss of one example involves the
01:01:13.570 --> 01:01:16.564
average of those losses for all the
01:01:16.564 --> 01:01:17.999
examples that violate the margin.
01:01:18.000 --> 01:01:23.340
So you're taking the average of YXI
01:01:23.340 --> 01:01:24.750
where these are the examples in your
01:01:24.750 --> 01:01:26.530
mini batch that violate the margin.
01:01:27.200 --> 01:01:29.270
And multiplying by ETA and adding it to
01:01:29.270 --> 01:01:29.640
West.
01:01:30.740 --> 01:01:32.470
So if your batch size is 1, it's the
01:01:32.470 --> 01:01:34.900
exact same algorithm as before, but by
01:01:34.900 --> 01:01:36.600
averaging your gradient over multiple
01:01:36.600 --> 01:01:38.220
examples you get a more stable
01:01:38.220 --> 01:01:39.230
optimization.
01:01:39.230 --> 01:01:41.250
And it can also be faster if you're
01:01:41.250 --> 01:01:44.800
able to parallelize your algorithm like
01:01:44.800 --> 01:01:47.470
you can with multiple GPUs, I mean CPUs
01:01:47.470 --> 01:01:48.120
or GPU.
01:01:52.450 --> 01:01:53.580
Any questions about that?
01:01:55.250 --> 01:01:55.480
Yeah.
01:01:56.770 --> 01:01:57.310
When it comes to.
01:01:58.820 --> 01:02:01.350
Divide the regular regularization
01:02:01.350 --> 01:02:02.740
constant by the mini batch.
01:02:04.020 --> 01:02:05.420
An.
01:02:05.770 --> 01:02:07.330
Just into when you're updating the
01:02:07.330 --> 01:02:07.680
weights.
01:02:10.790 --> 01:02:12.510
The average of that badge is not just
01:02:12.510 --> 01:02:15.110
like stochastic versus 1, right?
01:02:15.110 --> 01:02:17.145
So are you saying should you be taking
01:02:17.145 --> 01:02:19.781
like a bigger, are you saying should
01:02:19.781 --> 01:02:21.789
you change like how much weight you
01:02:21.790 --> 01:02:25.350
assign to this guy where you're trying
01:02:25.350 --> 01:02:26.350
to reduce the weight?
01:02:28.150 --> 01:02:30.930
Divided by the batch size by bad.
01:02:32.390 --> 01:02:32.960
This update.
01:02:34.290 --> 01:02:36.460
After 10 and then so you divide it by
01:02:36.460 --> 01:02:36.970
10.
01:02:36.970 --> 01:02:37.370
OK.
01:02:38.230 --> 01:02:39.950
You could do that.
01:02:39.950 --> 01:02:41.090
I mean this also.
01:02:41.090 --> 01:02:42.813
You don't have to have a 1 / K here,
01:02:42.813 --> 01:02:44.540
this could be just the sum.
01:02:44.540 --> 01:02:47.270
So here they averaged out the
01:02:47.270 --> 01:02:48.240
gradients.
01:02:48.300 --> 01:02:48.930
And.
01:02:49.910 --> 01:02:53.605
And also like sometimes, depending on
01:02:53.605 --> 01:02:56.210
your batch size, your ideal learning
01:02:56.210 --> 01:02:58.040
rate and other regularizations can
01:02:58.040 --> 01:02:58.970
sometimes change.
01:03:03.220 --> 01:03:07.570
So we saw SGD stochastic gradient
01:03:07.570 --> 01:03:10.420
descent for the hinge loss with, which
01:03:10.420 --> 01:03:11.740
is what the SVM uses.
01:03:13.340 --> 01:03:15.110
It's nice for the hinge loss because
01:03:15.110 --> 01:03:17.155
there's no gradient for incorrect or
01:03:17.155 --> 01:03:19.020
for confidently correct examples, so
01:03:19.020 --> 01:03:21.280
you only have to optimize over the ones
01:03:21.280 --> 01:03:22.310
that are within the margin.
01:03:24.320 --> 01:03:27.270
But you can also compute the gradients
01:03:27.270 --> 01:03:29.265
for all these other kinds of losses,
01:03:29.265 --> 01:03:30.830
like whoops, like the logistic
01:03:30.830 --> 01:03:32.810
regression loss or sigmoid loss.
01:03:35.540 --> 01:03:37.620
Another logistic loss, another kind of
01:03:37.620 --> 01:03:39.260
margin loss.
01:03:39.260 --> 01:03:40.730
These are not things that you should
01:03:40.730 --> 01:03:41.400
ever memorize.
01:03:41.400 --> 01:03:42.570
Or you can memorize them.
01:03:42.570 --> 01:03:44.470
I won't hold it against you, but.
01:03:45.510 --> 01:03:46.850
But you can always look them up, so
01:03:46.850 --> 01:03:47.820
they're not things you need to
01:03:47.820 --> 01:03:48.160
memorize.
01:03:50.430 --> 01:03:53.380
I will never ask you like what is the?
01:03:53.380 --> 01:03:55.270
I won't ask you like what's the
01:03:55.270 --> 01:03:56.600
gradient of some function.
01:03:58.090 --> 01:03:58.750
And.
01:03:59.660 --> 01:04:02.980
So this is just comparing like the
01:04:02.980 --> 01:04:05.930
optimization speed of the of this
01:04:05.930 --> 01:04:08.160
approach, Pegasus versus other
01:04:08.160 --> 01:04:08.900
optimizers.
01:04:10.000 --> 01:04:14.040
So for example, here's Pegasus.
01:04:14.040 --> 01:04:17.680
It goes like this is time on the X axis
01:04:17.680 --> 01:04:18.493
in seconds.
01:04:18.493 --> 01:04:20.920
So basically you want to get low
01:04:20.920 --> 01:04:22.300
because this is the objective that
01:04:22.300 --> 01:04:23.670
you're trying to minimize.
01:04:23.670 --> 01:04:25.900
So basically Pegasus shoots down to
01:04:25.900 --> 01:04:28.210
zero and like milliseconds and these
01:04:28.210 --> 01:04:29.980
other things are like still chugging
01:04:29.980 --> 01:04:31.940
away like many seconds later.
01:04:33.020 --> 01:04:33.730
And.
01:04:34.530 --> 01:04:37.500
And so consistently if you compare
01:04:37.500 --> 01:04:40.500
Pegasus to SVM perf, which is like
01:04:40.500 --> 01:04:41.920
stands for performance.
01:04:41.920 --> 01:04:45.050
It was a highly optimized SVM library.
01:04:45.940 --> 01:04:49.230
Or LA SVM, which I forget what that
01:04:49.230 --> 01:04:50.030
stands for right now.
01:04:50.740 --> 01:04:53.140
But two different SVM optimizers.
01:04:53.140 --> 01:04:56.056
Pegasus is just way faster you reach
01:04:56.056 --> 01:04:59.710
the you reach the ideal solution really
01:04:59.710 --> 01:05:00.690
really fast.
01:05:02.020 --> 01:05:04.290
The other one that performs just as
01:05:04.290 --> 01:05:06.280
well, if not better.
01:05:06.280 --> 01:05:09.180
Sdca is also a stochastic gradient
01:05:09.180 --> 01:05:13.470
descent method that just also chooses
01:05:13.470 --> 01:05:15.160
the learning rate dynamically instead
01:05:15.160 --> 01:05:16.738
of following a single schedule.
01:05:16.738 --> 01:05:19.080
The learning rate is the step size.
01:05:19.080 --> 01:05:20.460
It's like how much you move in the
01:05:20.460 --> 01:05:21.290
gradient direction.
01:05:24.240 --> 01:05:26.340
And then in terms of the error,
01:05:26.340 --> 01:05:28.440
training time and error, so it's so
01:05:28.440 --> 01:05:30.590
Pegasus is taking like under a second
01:05:30.590 --> 01:05:32.710
for all these different problems where
01:05:32.710 --> 01:05:34.390
some other libraries could take even
01:05:34.390 --> 01:05:35.380
hundreds of seconds.
01:05:36.290 --> 01:05:39.620
And it achieves just as good, if not
01:05:39.620 --> 01:05:42.120
better, error than most of them.
01:05:43.000 --> 01:05:44.800
And in part that's just like even
01:05:44.800 --> 01:05:46.090
though it's a global objective
01:05:46.090 --> 01:05:47.520
function, you have to like choose your
01:05:47.520 --> 01:05:50.120
regularization parameters and other
01:05:50.120 --> 01:05:50.720
parameters.
01:05:51.460 --> 01:05:53.490
And you have to.
01:05:53.860 --> 01:05:56.930
It may be hard to tell when you
01:05:56.930 --> 01:05:58.560
converge exactly, so you can get small
01:05:58.560 --> 01:06:00.180
differences between different
01:06:00.180 --> 01:06:00.800
algorithms.
01:06:04.300 --> 01:06:05.630
And then they also did.
01:06:05.630 --> 01:06:07.590
There's a kernelized version which
01:06:07.590 --> 01:06:07.873
won't.
01:06:07.873 --> 01:06:09.560
I won't go into, but it's the same
01:06:09.560 --> 01:06:10.350
principle.
01:06:10.770 --> 01:06:15.190
And so they're able to get.
01:06:15.300 --> 01:06:15.940
01:06:18.170 --> 01:06:20.520
They're able to use the kernelized
01:06:20.520 --> 01:06:21.960
version to get really good performance.
01:06:21.960 --> 01:06:24.470
So on MNIST for example, which was your
01:06:24.470 --> 01:06:29.010
homework one, they get 6% accuracy, 6%
01:06:29.010 --> 01:06:32.595
error rate using a kernelized SVM with
01:06:32.595 --> 01:06:33.460
a Gaussian kernel.
01:06:34.070 --> 01:06:35.330
So it's essentially just like a
01:06:35.330 --> 01:06:37.070
slightly smarter nearest neighbor
01:06:37.070 --> 01:06:37.850
algorithm.
01:06:40.840 --> 01:06:42.910
And the thing that's notable?
01:06:42.910 --> 01:06:44.210
Actually this takes.
01:06:48.920 --> 01:06:49.513
Kind of interesting.
01:06:49.513 --> 01:06:51.129
So it's not so fast.
01:06:51.130 --> 01:06:51.350
Sorry.
01:06:51.350 --> 01:06:52.590
It's just looking at the times.
01:06:52.590 --> 01:06:54.330
Yeah, so it's not so fast in the
01:06:54.330 --> 01:06:55.390
kernelized version, I guess.
01:06:55.390 --> 01:06:56.080
But it still works.
01:06:56.080 --> 01:06:57.650
I didn't look into that in depth, so
01:06:57.650 --> 01:06:57.965
I'm not.
01:06:57.965 --> 01:06:58.770
I can't explain it.
01:07:01.980 --> 01:07:02.290
Alright.
01:07:02.290 --> 01:07:04.050
And then finally like one other thing
01:07:04.050 --> 01:07:05.820
that they look at is the mini batch
01:07:05.820 --> 01:07:06.270
size.
01:07:06.270 --> 01:07:08.810
So if you as you like sample chunks of
01:07:08.810 --> 01:07:10.420
data and do the optimization with
01:07:10.420 --> 01:07:11.659
respect to each chunk of data.
01:07:12.730 --> 01:07:13.680
If you.
01:07:13.770 --> 01:07:14.430
01:07:15.530 --> 01:07:18.520
This is looking at the.
01:07:19.780 --> 01:07:22.780
At how close do you get to the ideal
01:07:22.780 --> 01:07:23.450
solution?
01:07:24.540 --> 01:07:26.830
And this is the mini batch size.
01:07:26.830 --> 01:07:28.860
So for a pretty big range of mini batch
01:07:28.860 --> 01:07:31.295
sizes you can get like very close to
01:07:31.295 --> 01:07:32.330
the ideal solution.
01:07:33.720 --> 01:07:36.570
So this is making an approximation
01:07:36.570 --> 01:07:39.660
because your every step you're choosing
01:07:39.660 --> 01:07:41.500
your step based on a subset of the
01:07:41.500 --> 01:07:41.860
data.
01:07:42.860 --> 01:07:47.530
But for like a big range of conditions,
01:07:47.530 --> 01:07:49.960
it gives you an ideal solution.
01:07:50.700 --> 01:07:53.459
And these are these are after different
01:07:53.460 --> 01:07:55.635
step length after different numbers of
01:07:55.635 --> 01:07:56.000
iterations.
01:07:56.000 --> 01:07:58.533
So if you do 4K iterations, you're at
01:07:58.533 --> 01:08:00.542
the Black line, 16 K iterations you're
01:08:00.542 --> 01:08:03.083
at the blue, and 64K iterations you're
01:08:03.083 --> 01:08:04.050
at the red.
01:08:05.030 --> 01:08:06.680
And yeah.
01:08:11.670 --> 01:08:13.200
And then they also did an experiment
01:08:13.200 --> 01:08:15.300
showing, like in their original paper,
01:08:15.300 --> 01:08:17.120
you would randomly sample with
01:08:17.120 --> 01:08:18.410
replacement the data.
01:08:18.410 --> 01:08:20.420
But if you randomly sample, if you just
01:08:20.420 --> 01:08:22.100
shuffle your data, essentially for
01:08:22.100 --> 01:08:23.750
what's called a epoch, which is like
01:08:23.750 --> 01:08:25.780
one cycle through the data, then you do
01:08:25.780 --> 01:08:26.250
better.
01:08:26.250 --> 01:08:28.680
So that's All in all optimization
01:08:28.680 --> 01:08:30.367
algorithms that I see now, you
01:08:30.367 --> 01:08:32.386
essentially shuffle the data, iterate
01:08:32.386 --> 01:08:35.440
through all the data and then reshuffle
01:08:35.440 --> 01:08:37.360
it and iterate again and each of those
01:08:37.360 --> 01:08:37.920
iterations.
01:08:37.970 --> 01:08:39.490
To the data is called at epoch.
01:08:41.110 --> 01:08:41.750
Epic.
01:08:41.750 --> 01:08:42.760
I never know how to pronounce it.
01:08:44.260 --> 01:08:46.440
And then they also just showed like
01:08:46.440 --> 01:08:48.363
their learning rate schedule seems to
01:08:48.363 --> 01:08:50.150
like provide much more stable results
01:08:50.150 --> 01:08:51.750
compared to a previous approach that
01:08:51.750 --> 01:08:53.540
would use a fixed learning rate for all
01:08:53.540 --> 01:08:55.200
the for all the iterations.
01:08:58.610 --> 01:09:02.230
So, takeaways and surprising facts
01:09:02.230 --> 01:09:03.190
about Pegasus.
01:09:04.460 --> 01:09:08.480
So it's using this SGD, which could be
01:09:08.480 --> 01:09:11.730
an acronym for sub gradient descent or
01:09:11.730 --> 01:09:13.560
stochastic gradient descent, and it
01:09:13.560 --> 01:09:14.780
applies both ways here.
01:09:15.580 --> 01:09:16.585
It's very simple.
01:09:16.585 --> 01:09:18.160
It's an effective optimization
01:09:18.160 --> 01:09:18.675
algorithm.
01:09:18.675 --> 01:09:20.830
It's probably the most widely used
01:09:20.830 --> 01:09:22.640
optimization algorithm in machine
01:09:22.640 --> 01:09:22.990
learning.
01:09:24.330 --> 01:09:26.230
There's very many variants of it, so
01:09:26.230 --> 01:09:28.590
I'll talk about some like atom in a
01:09:28.590 --> 01:09:30.990
couple classes, but the idea is that
01:09:30.990 --> 01:09:32.540
you just step towards a better solution
01:09:32.540 --> 01:09:34.380
of your parameters based on a small
01:09:34.380 --> 01:09:35.830
sample of the training data
01:09:35.830 --> 01:09:36.550
iteratively.
01:09:37.490 --> 01:09:39.370
It's not very sensitive that mini batch
01:09:39.370 --> 01:09:39.940
size.
01:09:40.990 --> 01:09:43.140
With larger batches you get like more
01:09:43.140 --> 01:09:44.720
stable estimates to the gradient and it
01:09:44.720 --> 01:09:46.560
can be a lot faster if you're doing GPU
01:09:46.560 --> 01:09:47.430
processing.
01:09:47.430 --> 01:09:50.860
So in machine learning and like large
01:09:50.860 --> 01:09:53.470
scale machine learning, deep learning.
01:09:54.150 --> 01:09:56.790
You tend to prefer large batches up to
01:09:56.790 --> 01:09:58.520
what you're GPU memory can hold.
01:09:59.680 --> 01:10:01.620
The same learning schedule is effective
01:10:01.620 --> 01:10:04.120
across many problems, so they're like
01:10:04.120 --> 01:10:05.865
decreasing the learning rate gradually
01:10:05.865 --> 01:10:08.610
is just like generally a good way to
01:10:08.610 --> 01:10:08.900
go.
01:10:08.900 --> 01:10:10.780
It doesn't require a lot of tuning.
01:10:12.550 --> 01:10:15.070
And the thing, so I don't know if it's
01:10:15.070 --> 01:10:17.350
in this paper, but this I forgot to
01:10:17.350 --> 01:10:18.880
mention, this work was done at TTI
01:10:18.880 --> 01:10:21.345
Chicago, so just very new here.
01:10:21.345 --> 01:10:23.890
So one of the first talks they give was
01:10:23.890 --> 01:10:25.540
for our group at UIUC.
01:10:25.540 --> 01:10:27.190
So I remember I remember them talking
01:10:27.190 --> 01:10:27.490
about it.
01:10:28.360 --> 01:10:29.810
And one of the things that's kind of a
01:10:29.810 --> 01:10:30.820
surprising result.
01:10:31.650 --> 01:10:35.390
Is that with this algorithm it's faster
01:10:35.390 --> 01:10:37.880
to train using a larger training set,
01:10:37.880 --> 01:10:40.180
so that's not super intuitive, right?
01:10:41.370 --> 01:10:42.710
In order to get the same test
01:10:42.710 --> 01:10:43.430
performance.
01:10:43.430 --> 01:10:46.990
And the reason is like if you think
01:10:46.990 --> 01:10:49.780
about like a little bit of data, if you
01:10:49.780 --> 01:10:51.970
have a little bit of data, then you
01:10:51.970 --> 01:10:53.540
have to like keep on iterating over
01:10:53.540 --> 01:10:55.450
that same little bit of data and each
01:10:55.450 --> 01:10:57.010
time you iterate over it, you're just
01:10:57.010 --> 01:10:58.330
like learning a little bit new.
01:10:58.330 --> 01:10:59.660
It's like trying to keep on like
01:10:59.660 --> 01:11:00.999
squeezing the same water out of a
01:11:01.000 --> 01:11:01.510
sponge.
01:11:02.560 --> 01:11:04.557
But if you have a lot of data and
01:11:04.557 --> 01:11:06.270
you're cycling through this big thing
01:11:06.270 --> 01:11:08.030
of data, you keep on seeing new things
01:11:08.030 --> 01:11:10.125
as you as you go through the data.
01:11:10.125 --> 01:11:12.290
And so you're learning more, like
01:11:12.290 --> 01:11:14.050
learning more per time.
01:11:14.690 --> 01:11:17.719
So if you have a million examples then,
01:11:17.719 --> 01:11:20.520
and you do like a million steps with
01:11:20.520 --> 01:11:22.220
one example each, then you learn a lot
01:11:22.220 --> 01:11:22.930
new.
01:11:22.930 --> 01:11:25.257
But if you have 10 examples and you do
01:11:25.257 --> 01:11:26.829
a million steps, million steps, then
01:11:26.830 --> 01:11:28.955
you've just seen there's 10 examples
01:11:28.955 --> 01:11:29.830
10,000 times.
01:11:30.660 --> 01:11:32.410
Or something 100,000 times.
01:11:32.410 --> 01:11:36.630
So if you get a larger training set,
01:11:36.630 --> 01:11:38.440
you actually get faster.
01:11:38.440 --> 01:11:40.230
It's faster to get the same test
01:11:40.230 --> 01:11:41.840
performance.
01:11:41.840 --> 01:11:44.020
And where that comes into play is that
01:11:44.020 --> 01:11:45.978
sometimes I'll have somebody say like,
01:11:45.978 --> 01:11:48.355
I don't like, I don't want to, I don't
01:11:48.355 --> 01:11:49.939
want to get more training examples
01:11:49.940 --> 01:11:51.700
because my optimization will take too
01:11:51.700 --> 01:11:52.390
long.
01:11:52.390 --> 01:11:54.650
But actually your optimization will be
01:11:54.650 --> 01:11:56.116
faster if you have more training
01:11:56.116 --> 01:11:57.500
examples, if you're using this kind of
01:11:57.500 --> 01:11:59.090
approach, if what you're trying to do
01:11:59.090 --> 01:12:01.490
is maximize your performance.
01:12:01.550 --> 01:12:02.780
Which is pretty much what you're always
01:12:02.780 --> 01:12:03.160
trying to do.
01:12:04.090 --> 01:12:06.810
So larger training set means faster
01:12:06.810 --> 01:12:07.920
runtime for training.
01:12:10.280 --> 01:12:14.330
So that's all about SVMS and SGDS.
01:12:14.330 --> 01:12:16.640
I know that's a lot to take in, but
01:12:16.640 --> 01:12:18.270
thank you for being patient and
01:12:18.270 --> 01:12:18.590
listening.
01:12:19.300 --> 01:12:21.480
And next week I'm going to start
01:12:21.480 --> 01:12:22.440
talking about neural networks.
01:12:22.440 --> 01:12:23.990
So I'll talk about multilayer
01:12:23.990 --> 01:12:26.450
perceptrons and then some concepts and
01:12:26.450 --> 01:12:28.120
deep networks.
01:12:28.120 --> 01:12:28.800
Thank you.
01:12:28.800 --> 01:12:30.040
Have a good weekend.
|