From 0bc2e082942c07b8839f802a2f8461c4b2472388 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 14 Oct 2024 13:54:09 +0200 Subject: [PATCH] last update, perhaps --- .../_build/.doctrees/environment.pickle | Bin 538942 -> 538942 bytes .../_build/.doctrees/week39.doctree | Bin 1121128 -> 1120432 bytes .../_build/.doctrees/week40.doctree | Bin 633872 -> 629427 bytes .../_build/.doctrees/week42.doctree | Bin 758371 -> 758758 bytes .../_build/html/_images/chapter1_17_0.png | Bin 13604 -> 13791 bytes .../_build/html/_images/chapter1_19_1.png | Bin 18825 -> 18607 bytes .../_build/html/_images/chapter1_33_0.png | Bin 25813 -> 27069 bytes .../_build/html/_images/chapter1_9_0.png | Bin 20002 -> 20243 bytes .../_build/html/_images/chapter3_51_0.png | Bin 21966 -> 22405 bytes .../_build/html/_images/chapter6_1_1.png | Bin 50630 -> 47012 bytes .../_build/html/_images/chapter6_1_2.png | Bin 36012 -> 35519 bytes .../_build/html/_images/chapter8_65_1.png | Bin 41116 -> 35221 bytes 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For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. \n", + "2. [Video of lecture](https://youtu.be/7B2F35gNj2Y)\n", "\n", - "3. Neural Networks demystified at \n", + "3. [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct14.pdf)\n", "\n", - "4. Building Neural Networks from scratch at \n", + "4. For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. \n", "\n", - "5. Video on Neural Networks at \n", + "5. Neural Networks demystified at \n", "\n", - "6. Video on the back propagation algorithm at \n", + "6. Building Neural Networks from scratch at \n", + "\n", + "7. Video on Neural Networks at \n", + "\n", + "8. Video on the back propagation algorithm at \n", "\n", "I also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at ." ] }, { "cell_type": "markdown", - "id": "8c43f62c", + "id": "493dbcac", "metadata": { "editable": true }, @@ -73,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "bb52c881", + "id": "0a83a2c3", "metadata": { "editable": true }, @@ -92,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "e53a998a", + "id": "93735ab4", "metadata": { "editable": true }, @@ -108,7 +110,7 @@ }, { "cell_type": "markdown", - "id": "2be1dbc1", + "id": "9f8617c7", "metadata": { "editable": true }, @@ -119,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "d81e5954", + "id": "e35c3c4a", "metadata": { "editable": true }, @@ -133,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "bf67ca94", + "id": "35d77455", "metadata": { "editable": true }, @@ -148,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "ea5ccdfd", + "id": "aed7f415", "metadata": { "editable": true }, @@ -160,7 +162,7 @@ }, { "cell_type": "markdown", - 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"id": "927894b5", + "id": "aeea971d", "metadata": { "editable": true }, @@ -1080,7 +1082,7 @@ }, { "cell_type": "markdown", - "id": "75624550", + "id": "bbaf4d20", "metadata": { "editable": true }, @@ -1092,7 +1094,7 @@ }, { "cell_type": "markdown", - "id": "9252c078", + "id": "4469f285", "metadata": { "editable": true }, @@ -1102,7 +1104,7 @@ }, { "cell_type": "markdown", - "id": "10c7da6e", + "id": "9735e5df", "metadata": { "editable": true }, @@ -1114,7 +1116,7 @@ }, { "cell_type": "markdown", - "id": "0fe640a9", + "id": "54d79226", "metadata": { "editable": true }, @@ -1126,7 +1128,7 @@ }, { "cell_type": "markdown", - "id": "01ff9a38", + "id": "4365ba97", "metadata": { "editable": true }, @@ -1138,7 +1140,7 @@ }, { "cell_type": "markdown", - "id": "37fda9de", + "id": "bcd2d94c", "metadata": { "editable": true }, @@ -1148,7 +1150,7 @@ }, { "cell_type": "markdown", - "id": "861af2b9", + "id": "5584b15e", "metadata": { "editable": true }, @@ -1160,7 +1162,7 @@ }, { "cell_type": "markdown", - "id": "f9cea8b7", + "id": "f0c53017", "metadata": { "editable": true }, @@ -1170,7 +1172,7 @@ }, { "cell_type": "markdown", - "id": "12e3298b", + "id": "2ebbdf34", "metadata": { "editable": true }, @@ -1183,7 +1185,7 @@ }, { "cell_type": "markdown", - "id": "a104df98", + "id": "b94ec668", "metadata": { "editable": true }, @@ -1195,7 +1197,7 @@ }, { "cell_type": "markdown", - "id": "9bc2f036", + "id": "8bcb00fc", "metadata": { "editable": true }, @@ -1205,7 +1207,7 @@ }, { "cell_type": "markdown", - "id": "568ced5c", + "id": "f8725166", "metadata": { "editable": true }, @@ -1217,7 +1219,7 @@ }, { "cell_type": "markdown", - "id": "906d2bd9", + "id": "bcb99786", "metadata": { "editable": true }, @@ -1227,7 +1229,7 @@ }, { "cell_type": "markdown", - "id": "79992e6f", + "id": "975fb151", "metadata": { "editable": true }, @@ -1239,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "0745b6ba", + "id": "e17fa81d", "metadata": { "editable": true }, @@ -1249,7 +1251,7 @@ }, { "cell_type": "markdown", - "id": "4fb2781f", + "id": "12912a16", "metadata": { "editable": true }, @@ -1261,7 +1263,7 @@ }, { "cell_type": "markdown", - "id": "57576b6e", + "id": "0c14e44b", "metadata": { "editable": true }, @@ -1271,7 +1273,7 @@ }, { "cell_type": "markdown", - "id": "d1f38053", + "id": "6e854ca6", "metadata": { "editable": true }, @@ -1288,7 +1290,7 @@ }, { "cell_type": "markdown", - "id": "6f6f31e8", + "id": "d10945a7", "metadata": { "editable": true }, @@ -1300,7 +1302,7 @@ }, { "cell_type": "markdown", - "id": "f206ae2b", + "id": "44212558", "metadata": { "editable": true }, @@ -1312,7 +1314,7 @@ }, { "cell_type": "markdown", - "id": "5e7af877", + "id": "3fd80944", "metadata": { "editable": true }, @@ -1328,7 +1330,7 @@ }, { "cell_type": "markdown", - "id": "96c13dab", + "id": "42598707", "metadata": { "editable": true }, @@ -1345,7 +1347,7 @@ }, { "cell_type": "markdown", - "id": "a6781c7d", + "id": "21638e6e", "metadata": { "editable": true }, @@ -1357,7 +1359,7 @@ }, { "cell_type": "markdown", - "id": "4db58da4", + "id": "2843d78a", "metadata": { "editable": true }, @@ -1370,7 +1372,7 @@ }, { "cell_type": "markdown", - "id": "b4458c55", + "id": "152a46dd", "metadata": { "editable": true }, @@ -1382,7 +1384,7 @@ }, { "cell_type": "markdown", - "id": "b32e0714", + "id": "f888a137", "metadata": { "editable": true }, @@ -1398,7 +1400,7 @@ }, { "cell_type": "markdown", - "id": "fffb7785", + "id": "adc3a5e4", "metadata": { "editable": true }, @@ -1410,7 +1412,7 @@ }, { "cell_type": "markdown", - "id": "08bff16c", + "id": "8c598490", "metadata": { "editable": true }, @@ -1426,7 +1428,7 @@ }, { "cell_type": "markdown", - "id": "fb907bb3", + "id": "0ae86831", "metadata": { "editable": true }, @@ -1438,7 +1440,7 @@ }, { "cell_type": "markdown", - "id": "f97ed7ef", + "id": "077d65f3", "metadata": { "editable": true }, @@ -1450,7 +1452,7 @@ }, { "cell_type": "markdown", - "id": "136c2230", + "id": "816b7643", "metadata": { "editable": true }, @@ -1460,7 +1462,7 @@ }, { "cell_type": "markdown", - "id": "4b2344b6", + "id": "c748d997", "metadata": { "editable": true }, @@ -1472,7 +1474,7 @@ }, { "cell_type": "markdown", - "id": "52a4e7a7", + "id": "6186a477", "metadata": { "editable": true }, @@ -1482,7 +1484,7 @@ }, { "cell_type": "markdown", - "id": "163ee2e5", + "id": "5159e465", "metadata": { "editable": true }, @@ -1494,7 +1496,7 @@ }, { "cell_type": "markdown", - "id": "5aa607a5", + "id": "1717c046", "metadata": { "editable": true }, @@ -1508,7 +1510,7 @@ }, { "cell_type": "markdown", - "id": "da13c77b", + "id": "43b02473", "metadata": { "editable": true }, @@ -1520,7 +1522,7 @@ }, { "cell_type": "markdown", - "id": "7bf944d0", + "id": "5034b9a1", "metadata": { "editable": true }, @@ -1530,7 +1532,7 @@ }, { "cell_type": "markdown", - "id": "ea130e95", + "id": "cd13d020", "metadata": { "editable": true }, @@ -1542,7 +1544,7 @@ }, { "cell_type": "markdown", - "id": "2fba64b7", + "id": "592375f7", "metadata": { "editable": true }, @@ -1552,7 +1554,7 @@ }, { "cell_type": "markdown", - "id": "5904a528", + "id": "2bbcf893", "metadata": { "editable": true }, @@ -1564,7 +1566,7 @@ }, { "cell_type": "markdown", - "id": "86f8199b", + "id": "58fc5cdc", "metadata": { "editable": true }, @@ -1576,7 +1578,7 @@ }, { "cell_type": "markdown", - "id": "e4370f9f", + "id": "9ec4e6ef", "metadata": { "editable": true }, @@ -1588,7 +1590,7 @@ }, { "cell_type": "markdown", - "id": "f60e1730", + "id": "fcdfd63d", "metadata": { "editable": true }, @@ -1598,7 +1600,7 @@ }, { "cell_type": "markdown", - "id": "e282d002", + "id": "5199bd46", "metadata": { "editable": true }, @@ -1610,7 +1612,7 @@ }, { "cell_type": "markdown", - "id": "5de6d59f", + "id": "b255a6df", "metadata": { "editable": true }, @@ -1620,7 +1622,7 @@ }, { "cell_type": "markdown", - "id": "97c35e7d", + "id": "a6617bc8", "metadata": { "editable": true }, @@ -1632,7 +1634,7 @@ }, { "cell_type": "markdown", - "id": "c4754c54", + "id": "1ae198f0", "metadata": { "editable": true }, @@ -1650,7 +1652,7 @@ }, { "cell_type": "markdown", - "id": "0b03f12b", + "id": "f0333d5b", "metadata": { "editable": true }, @@ -1668,7 +1670,7 @@ }, { "cell_type": "markdown", - "id": "ad079735", + "id": "05f19c67", "metadata": { "editable": true }, @@ -1680,7 +1682,7 @@ }, { "cell_type": "markdown", - "id": "0bf757b6", + "id": "f35623e3", "metadata": { "editable": true }, @@ -1690,7 +1692,7 @@ }, { "cell_type": "markdown", - "id": "b6246783", + "id": "422aabb5", "metadata": { "editable": true }, @@ -1702,7 +1704,7 @@ }, { "cell_type": "markdown", - "id": "b7575d50", + "id": "5f2f2143", "metadata": { "editable": true }, @@ -1714,7 +1716,7 @@ }, { "cell_type": "markdown", - "id": "e6c93f95", + "id": "e0ec6446", "metadata": { "editable": true }, @@ -1726,7 +1728,7 @@ }, { "cell_type": "markdown", - "id": "b52b78ac", + "id": "ab3e824e", "metadata": { "editable": true }, @@ -1736,7 +1738,7 @@ }, { "cell_type": "markdown", - "id": "a5fdfb9c", + "id": "b63e0260", "metadata": { "editable": true }, @@ -1748,7 +1750,7 @@ }, { "cell_type": "markdown", - "id": "8bd7f846", + "id": "8f85d588", "metadata": { "editable": true }, @@ -1758,7 +1760,7 @@ }, { "cell_type": "markdown", - "id": "550334c8", + "id": "c53b387b", "metadata": { "editable": true }, @@ -1770,7 +1772,7 @@ }, { "cell_type": "markdown", - "id": "ac298c05", + "id": "8bcd2836", "metadata": { "editable": true }, @@ -1788,7 +1790,7 @@ }, { "cell_type": "markdown", - "id": "6609cbf5", + "id": "e37eab2a", "metadata": { "editable": true }, @@ -1798,7 +1800,7 @@ }, { "cell_type": "markdown", - "id": "64741262", + "id": "e7371843", "metadata": { "editable": true }, @@ -1816,7 +1818,7 @@ }, { "cell_type": "markdown", - "id": "76c4610d", + "id": "c6b5e4ee", "metadata": { "editable": true }, @@ -1826,7 +1828,7 @@ }, { "cell_type": "markdown", - "id": "efd0ba93", + "id": "d49e9c2c", "metadata": { "editable": true }, @@ -1844,7 +1846,7 @@ }, { "cell_type": "markdown", - "id": "98e88435", + "id": "bc813237", "metadata": { "editable": true }, @@ -1856,7 +1858,7 @@ }, { "cell_type": "markdown", - "id": "08bdbdff", + "id": "0adc23ee", "metadata": { "editable": true }, @@ -1868,7 +1870,7 @@ }, { "cell_type": "markdown", - "id": "e234c8a6", + "id": "9f18ba82", "metadata": { "editable": true }, @@ -1878,7 +1880,7 @@ }, { "cell_type": "markdown", - "id": "6a4118be", + "id": "b9c3658c", "metadata": { "editable": true }, @@ -1890,7 +1892,7 @@ }, { "cell_type": "markdown", - "id": "d211df4b", + "id": "3792e41e", "metadata": { "editable": true }, @@ -1902,7 +1904,7 @@ }, { "cell_type": "markdown", - "id": "bf0c2817", + "id": "d052d0c1", "metadata": { "editable": true }, @@ -1912,7 +1914,7 @@ }, { "cell_type": "markdown", - "id": "2de9333a", + "id": "cb86b10e", "metadata": { "editable": true }, @@ -1924,7 +1926,7 @@ }, { "cell_type": "markdown", - "id": "00d82ece", + "id": "4053ba69", "metadata": { "editable": true }, @@ -1934,7 +1936,7 @@ }, { "cell_type": "markdown", - "id": "19f78643", + "id": "444986c0", "metadata": { "editable": true }, @@ -1946,7 +1948,7 @@ }, { "cell_type": "markdown", - "id": "1424f687", + "id": "19dc83fd", "metadata": { "editable": true }, @@ -1958,7 +1960,7 @@ }, { "cell_type": "markdown", - "id": "9d7c23b2", + "id": "a10386fd", "metadata": { "editable": true }, @@ -1984,7 +1986,7 @@ }, { "cell_type": "markdown", - "id": "1decfbef", + "id": "08b046a2", "metadata": { "editable": true }, @@ -2007,7 +2009,7 @@ }, { "cell_type": "markdown", - "id": "7f237d52", + "id": "abd09f94", "metadata": { "editable": true }, @@ -2019,7 +2021,7 @@ }, { "cell_type": "markdown", - "id": "d37fa1b5", + "id": "d2b58a05", "metadata": { "editable": true }, @@ -2031,7 +2033,7 @@ }, { "cell_type": "markdown", - "id": "213b757d", + "id": "9eb706a2", "metadata": { "editable": true }, @@ -2041,7 +2043,7 @@ }, { "cell_type": "markdown", - "id": "3137751d", + "id": "4dd2c404", "metadata": { "editable": true }, @@ -2053,7 +2055,7 @@ }, { "cell_type": "markdown", - "id": "da1cf61b", + "id": "10ec2807", "metadata": { "editable": true }, @@ -2067,7 +2069,7 @@ }, { "cell_type": "markdown", - "id": "3b57ef97", + "id": "dc831415", "metadata": { "editable": true }, @@ -2079,7 +2081,7 @@ }, { "cell_type": "markdown", - "id": "bb0c4d59", + "id": "b6841649", "metadata": { "editable": true }, @@ -2091,7 +2093,7 @@ }, { "cell_type": "markdown", - "id": "a9483fc9", + "id": "355caec2", "metadata": { "editable": true }, @@ -2101,7 +2103,7 @@ }, { "cell_type": "markdown", - "id": "49c7c2f3", + "id": "472bf1c5", "metadata": { "editable": true }, @@ -2113,7 +2115,7 @@ }, { "cell_type": "markdown", - "id": "734ef014", + "id": "d0a449d0", "metadata": { "editable": true }, @@ -2125,7 +2127,7 @@ }, { "cell_type": "markdown", - "id": "ac393c38", + "id": "128ab1d1", "metadata": { "editable": true }, @@ -2135,7 +2137,7 @@ }, { "cell_type": "markdown", - "id": "c38ed8eb", + "id": "bfae04eb", "metadata": { "editable": true }, @@ -2147,7 +2149,7 @@ }, { "cell_type": "markdown", - "id": "d097bbf6", + "id": "4530efa3", "metadata": { "editable": true }, @@ -2159,7 +2161,7 @@ }, { "cell_type": "markdown", - "id": "56e28349", + "id": "4da3f467", "metadata": { "editable": true }, @@ -2182,7 +2184,7 @@ }, { "cell_type": "markdown", - "id": "0f764f08", + "id": "a3f2750e", "metadata": { "editable": true }, @@ -2201,7 +2203,7 @@ }, { "cell_type": "markdown", - "id": "697fbd9c", + "id": "a8996317", "metadata": { "editable": true }, @@ -2213,7 +2215,7 @@ }, { "cell_type": "markdown", - "id": "e9f79c9b", + "id": "bb6de399", "metadata": { "editable": true }, @@ -2223,7 +2225,7 @@ }, { "cell_type": "markdown", - "id": "8285a58e", + "id": "c6aa6e93", "metadata": { "editable": true }, @@ -2235,7 +2237,7 @@ }, { "cell_type": "markdown", - "id": "eba13151", + "id": "e10b7b94", "metadata": { "editable": true }, @@ -2252,7 +2254,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "d5693cd0", + "id": "7255188c", "metadata": { "collapsed": false, "editable": true @@ -2336,7 +2338,7 @@ }, { "cell_type": "markdown", - "id": "b0e28a6b", + "id": "cbe6427f", "metadata": { "editable": true }, @@ -2358,7 +2360,7 @@ }, { "cell_type": "markdown", - "id": "436fb27b", + "id": "7aa9a09d", "metadata": { "editable": true }, @@ -2376,7 +2378,7 @@ }, { "cell_type": "markdown", - "id": "9319de67", + "id": "a6833949", "metadata": { "editable": true }, @@ -2399,7 +2401,7 @@ }, { "cell_type": "markdown", - "id": "e90fbb0a", + "id": "6c4a6dfd", "metadata": { "editable": true }, @@ -2418,7 +2420,7 @@ }, { "cell_type": "markdown", - "id": "7a18427f", + "id": "7ebf35c4", "metadata": { "editable": true }, @@ -2446,7 +2448,7 @@ }, { "cell_type": "markdown", - "id": "ac352aa1", + "id": "81e57dfd", "metadata": { "editable": true }, @@ -2466,7 +2468,7 @@ }, { "cell_type": "markdown", - "id": "67a8bef0", + "id": "c0ad35af", "metadata": { "editable": true }, @@ -2487,7 +2489,7 @@ }, { "cell_type": "markdown", - "id": "76de2016", + "id": "9abee1c8", "metadata": { "editable": true }, @@ -2501,7 +2503,7 @@ }, { "cell_type": "markdown", - "id": "e798fa5d", + "id": "1d1ff061", "metadata": { "editable": true }, @@ -2513,7 +2515,7 @@ }, { "cell_type": "markdown", - "id": "6f33abb9", + "id": "a8b62fc1", "metadata": { "editable": true }, @@ -2535,7 +2537,7 @@ }, { "cell_type": "markdown", - "id": "ad76ab9d", + "id": "a8858730", "metadata": { "editable": true }, @@ -2557,7 +2559,7 @@ }, { "cell_type": "markdown", - "id": "4d0588bb", + "id": "be1e67e4", "metadata": { "editable": true }, @@ -2586,7 +2588,7 @@ }, { "cell_type": "markdown", - "id": "cb5679f8", + "id": "361acaff", "metadata": { "editable": true }, @@ -2612,7 +2614,7 @@ }, { "cell_type": "markdown", - "id": "fa928c9b", + "id": "636cc811", "metadata": { "editable": true }, @@ -2638,7 +2640,7 @@ }, { "cell_type": "markdown", - "id": "4a9462ce", + "id": "a9c3c37b", "metadata": { "editable": true }, @@ -2658,7 +2660,7 @@ }, { "cell_type": "markdown", - "id": "e523997a", + "id": "99899732", "metadata": { "editable": true }, @@ -2678,7 +2680,7 @@ }, { "cell_type": "markdown", - "id": "19bba5e1", + "id": "d76992f2", "metadata": { "editable": true }, @@ -2704,7 +2706,7 @@ }, { "cell_type": "markdown", - "id": "ff6b5f13", + "id": "f3f3714c", "metadata": { "editable": true }, @@ -2733,7 +2735,7 @@ }, { "cell_type": "markdown", - "id": "df905d9f", + "id": "f9eea049", "metadata": { "editable": true }, @@ -2751,7 +2753,7 @@ }, { "cell_type": "markdown", - "id": "233e93d6", + "id": "ffe27b44", "metadata": { "editable": true }, @@ -2767,7 +2769,7 @@ }, { "cell_type": "markdown", - "id": "0034168c", + "id": "8a4d6517", "metadata": { "editable": true }, @@ -2779,7 +2781,7 @@ }, { "cell_type": "markdown", - "id": "cd701f7d", + "id": "322a5ffb", "metadata": { "editable": true }, @@ -2793,7 +2795,7 @@ }, { "cell_type": "markdown", - "id": "84048fb0", + "id": "d7c906f4", "metadata": { "editable": true }, @@ -2821,7 +2823,7 @@ }, { "cell_type": "markdown", - "id": "61ea03ed", + "id": "d9b1c4a9", "metadata": { "editable": true }, @@ -2833,7 +2835,7 @@ }, { "cell_type": "markdown", - "id": "abd0c817", + "id": "62795b6a", "metadata": { "editable": true }, @@ -2843,7 +2845,7 @@ }, { "cell_type": "markdown", - "id": "2e0379cc", + "id": "a302cc7a", "metadata": { "editable": true }, @@ -2855,7 +2857,7 @@ }, { "cell_type": "markdown", - "id": "59290cf7", + "id": "892da0f2", "metadata": { "editable": true }, @@ -2866,7 +2868,7 @@ }, { "cell_type": "markdown", - "id": "6bc256eb", + "id": "0702951f", "metadata": { "editable": true }, @@ -2878,7 +2880,7 @@ }, { "cell_type": "markdown", - "id": "831eee08", + "id": "cb0f2050", "metadata": { "editable": true }, @@ -2891,7 +2893,7 @@ }, { "cell_type": "markdown", - "id": "ba46bcc0", + "id": "9d6da3d7", "metadata": { "editable": true }, @@ -2916,7 +2918,7 @@ }, { "cell_type": "markdown", - "id": "a475ed33", + "id": "eb7f6521", "metadata": { "editable": true }, @@ -2929,7 +2931,7 @@ }, { "cell_type": "markdown", - "id": "378895ce", + "id": "2d3caa00", "metadata": { "editable": true }, @@ -2941,7 +2943,7 @@ }, { "cell_type": "markdown", - "id": "268989a6", + "id": "b84b3da0", "metadata": { "editable": true }, @@ -2953,7 +2955,7 @@ }, { "cell_type": "markdown", - "id": "1ac5dfe0", + "id": "5be6ff37", "metadata": { "editable": true }, @@ -2963,7 +2965,7 @@ }, { "cell_type": "markdown", - "id": "4d3d6bb7", + "id": "491b47ff", "metadata": { "editable": true }, @@ -2975,7 +2977,7 @@ }, { "cell_type": "markdown", - "id": "bc4e4a53", + "id": "c752e63e", "metadata": { "editable": true }, @@ -2987,7 +2989,7 @@ }, { "cell_type": "markdown", - "id": "4cc000a1", + "id": "efe0f0c7", "metadata": { "editable": true }, @@ -2999,7 +3001,7 @@ }, { "cell_type": "markdown", - "id": "7f5ea691", + "id": "13ee778d", "metadata": { "editable": true }, @@ -3011,7 +3013,7 @@ }, { "cell_type": "markdown", - "id": "a7f4fc8e", + "id": "6884cc1e", "metadata": { "editable": true }, @@ -3021,7 +3023,7 @@ }, { "cell_type": "markdown", - "id": "acdecb39", + "id": "9d7a0a4e", "metadata": { "editable": true }, @@ -3033,7 +3035,7 @@ }, { "cell_type": "markdown", - "id": "b0c3e8c7", + "id": "35bfbdc8", "metadata": { "editable": true }, @@ -3043,7 +3045,7 @@ }, { "cell_type": "markdown", - "id": "6e130deb", + "id": "631ba4a6", "metadata": { "editable": true }, @@ -3055,7 +3057,7 @@ }, { "cell_type": "markdown", - "id": "23e83ce8", + "id": "a37f8d69", "metadata": { "editable": true }, @@ -3068,7 +3070,7 @@ }, { "cell_type": "markdown", - "id": "67c77893", + "id": "fcedfc85", "metadata": { "editable": true }, @@ -3080,7 +3082,7 @@ }, { "cell_type": "markdown", - "id": "36ede636", + "id": "4c1ab15e", "metadata": { "editable": true }, @@ -3090,7 +3092,7 @@ }, { "cell_type": "markdown", - "id": "4008b26c", + "id": "6518cab5", "metadata": { "editable": true }, @@ -3102,7 +3104,7 @@ }, { "cell_type": "markdown", - "id": "258ae1d4", + "id": "ccfcb38c", "metadata": { "editable": true }, @@ -3113,7 +3115,7 @@ }, { "cell_type": "markdown", - "id": "b9c648be", + "id": "4174ce25", "metadata": { "editable": true }, @@ -3125,7 +3127,7 @@ }, { "cell_type": "markdown", - "id": "615f1fce", + "id": "4602e3f2", "metadata": { "editable": true }, @@ -3135,7 +3137,7 @@ }, { "cell_type": "markdown", - "id": "620b34cb", + "id": "4b71d88d", "metadata": { "editable": true }, @@ -3147,7 +3149,7 @@ }, { "cell_type": "markdown", - "id": "7d09dc1a", + "id": "d5f0d911", "metadata": { "editable": true }, @@ -3157,7 +3159,7 @@ }, { "cell_type": "markdown", - "id": "8589b8ee", + "id": "d0c13a16", "metadata": { "editable": true }, @@ -3168,7 +3170,7 @@ }, { "cell_type": "markdown", - "id": "63160327", + "id": "7af1d556", "metadata": { "editable": true }, @@ -3181,7 +3183,7 @@ }, { "cell_type": "markdown", - "id": "98614055", + "id": "a42c51d7", "metadata": { "editable": true }, @@ -3191,7 +3193,7 @@ }, { "cell_type": "markdown", - "id": "37435d7c", + "id": "6365d360", "metadata": { "editable": true }, @@ -3203,7 +3205,7 @@ }, { "cell_type": "markdown", - "id": "64a4d79f", + "id": "557a932f", "metadata": { "editable": true }, @@ -3213,7 +3215,7 @@ }, { "cell_type": "markdown", - "id": "7e3891af", + "id": "055db867", "metadata": { "editable": true }, @@ -3225,7 +3227,7 @@ }, { "cell_type": "markdown", - "id": "7205e781", + "id": "ccd85582", "metadata": { "editable": true }, @@ -3235,7 +3237,7 @@ }, { "cell_type": "markdown", - "id": "a5645092", + "id": "d2d1077a", "metadata": { "editable": true }, @@ -3259,7 +3261,7 @@ }, { "cell_type": "markdown", - "id": "09e9b12d", + "id": "3dff91e3", "metadata": { "editable": true }, @@ -3309,7 +3311,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "84fd1480", + "id": "0527bd02", "metadata": { "collapsed": false, "editable": true @@ -3362,7 +3364,7 @@ }, { "cell_type": "markdown", - "id": "e671e2a8", + "id": "b5238d8c", "metadata": { "editable": true }, @@ -3383,7 +3385,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "5d1c4624", + "id": "bba6be21", "metadata": { "collapsed": false, "editable": true @@ -3421,7 +3423,7 @@ }, { "cell_type": "markdown", - "id": "b397d4ee", + "id": "4a77d660", "metadata": { "editable": true }, @@ -3465,7 +3467,7 @@ }, { "cell_type": "markdown", - "id": "1fce534f", + "id": "0d276258", "metadata": { "editable": true }, @@ -3505,7 +3507,7 @@ }, { "cell_type": "markdown", - "id": "9c50a158", + "id": "f25c6fa1", "metadata": { "editable": true }, @@ -3526,7 +3528,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "4daeba9a", + "id": "3a4a6bba", "metadata": { "collapsed": false, "editable": true @@ -3552,7 +3554,7 @@ }, { "cell_type": "markdown", - "id": "f26a835c", + "id": "de886e02", "metadata": { "editable": true }, @@ -3580,7 +3582,7 @@ }, { "cell_type": "markdown", - "id": "09f1187b", + "id": "baeb1290", "metadata": { "editable": true }, @@ -3617,7 +3619,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "35d71f0e", + "id": "b575ea05", "metadata": { "collapsed": false, "editable": true @@ -3663,7 +3665,7 @@ }, { "cell_type": "markdown", - "id": "e318575f", + "id": "a981e9cf", "metadata": { "editable": true }, @@ -3694,7 +3696,7 @@ }, { "cell_type": "markdown", - "id": "788f45d2", + "id": "fcb5b7b4", "metadata": { "editable": true }, @@ -3732,7 +3734,7 @@ }, { "cell_type": "markdown", - "id": "599a7b8f", + "id": "135edd42", "metadata": { "editable": true }, @@ -3766,7 +3768,7 @@ }, { "cell_type": "markdown", - "id": "cc694849", + "id": "6a94b210", "metadata": { "editable": true }, @@ -3807,7 +3809,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "e3607c66", + "id": "10a0f4b1", "metadata": { "collapsed": false, "editable": true @@ -3886,7 +3888,7 @@ }, { "cell_type": "markdown", - "id": "0a70cdcf", + "id": "37e26c5f", "metadata": { "editable": true }, @@ -3907,7 +3909,7 @@ }, { "cell_type": "markdown", - "id": "b0600212", + "id": "3721f1b2", "metadata": { "editable": true }, @@ -3921,7 +3923,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "4e0c1326", + "id": "a2225589", "metadata": { "collapsed": false, "editable": true @@ -4031,7 +4033,7 @@ }, { "cell_type": "markdown", - "id": "125f8eca", + "id": "9d915d49", "metadata": { "editable": true }, @@ -4050,7 +4052,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "cef0e788", + "id": "62e979c5", "metadata": { "collapsed": false, "editable": true @@ -4077,7 +4079,7 @@ }, { "cell_type": "markdown", - "id": "b12d6f86", + "id": "32464471", "metadata": { "editable": true }, @@ -4091,7 +4093,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "974faa4e", + "id": "c3277c8f", "metadata": { "collapsed": false, "editable": true @@ -4122,7 +4124,7 @@ }, { "cell_type": "markdown", - "id": "3bb19122", + "id": "bb09a45b", "metadata": { "editable": true }, @@ -4133,7 +4135,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "baeb1ea3", + "id": "6e2566d5", "metadata": { "collapsed": false, "editable": true @@ -4177,7 +4179,7 @@ }, { "cell_type": "markdown", - "id": "d75dad8e", + "id": "eec163df", "metadata": { "editable": true }, @@ -4200,7 +4202,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "d55fae28", + "id": "b33a66c0", "metadata": { "collapsed": false, "editable": true @@ -4227,7 +4229,7 @@ }, { "cell_type": "markdown", - "id": "276badf8", + "id": "e1d6d9d7", "metadata": { "editable": true }, @@ -4238,7 +4240,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "7d6c714e", + "id": "4bc9d5c7", "metadata": { "collapsed": false, "editable": true @@ -4283,7 +4285,7 @@ }, { "cell_type": "markdown", - "id": "0d45b429", + "id": "c1c46aeb", "metadata": { "editable": true }, @@ -4301,7 +4303,7 @@ }, { "cell_type": "markdown", - "id": "67aec670", + "id": "b5205123", "metadata": { "editable": true }, @@ -4336,7 +4338,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "0ab38a83", + "id": "b9c0dfe3", "metadata": { "collapsed": false, "editable": true @@ -4348,7 +4350,7 @@ }, { "cell_type": "markdown", - "id": "8f53f5b9", + "id": "f562b8e8", "metadata": { "editable": true }, @@ -4360,7 +4362,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "5190c7ff", + "id": "d4526899", "metadata": { "collapsed": false, "editable": true @@ -4373,7 +4375,7 @@ }, { "cell_type": "markdown", - "id": "676ff9d7", + "id": "59617395", "metadata": { "editable": true }, @@ -4384,7 +4386,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "479149f7", + "id": "9c975a67", "metadata": { "collapsed": false, "editable": true @@ -4397,7 +4399,7 @@ }, { "cell_type": "markdown", - "id": "62d1b789", + "id": "975357be", "metadata": { "editable": true }, @@ -4412,7 +4414,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "4a11035a", + "id": "e558fb1f", "metadata": { "collapsed": false, "editable": true @@ -4424,7 +4426,7 @@ }, { "cell_type": "markdown", - "id": "abf44b70", + "id": "a6f5c4d4", "metadata": { "editable": true }, @@ -4436,7 +4438,7 @@ }, { "cell_type": "markdown", - "id": "3f163559", + "id": "6bb12225", "metadata": { "editable": true }, @@ -4449,7 +4451,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "f7418c1e", + "id": "41fd6ccf", "metadata": { "collapsed": false, "editable": true @@ -4504,7 +4506,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "49ec0156", + "id": "c70145e7", "metadata": { "collapsed": false, "editable": true @@ -4533,7 +4535,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "302ad127", + "id": "f0413064", "metadata": { "collapsed": false, "editable": true @@ -4563,7 +4565,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "436a3e0a", + "id": "a4ba8bc0", "metadata": { "collapsed": false, "editable": true @@ -4590,7 +4592,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "a26e83e0", + "id": "e38856a7", "metadata": { "collapsed": false, "editable": true @@ -4632,7 +4634,7 @@ }, { "cell_type": "markdown", - "id": "8d69b494", + "id": "143ff6b2", "metadata": { "editable": true }, @@ -4643,7 +4645,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "cad16bbe", + "id": "8830114d", "metadata": { "collapsed": false, "editable": true @@ -4820,7 +4822,7 @@ }, { "cell_type": "markdown", - "id": "61624838", + "id": "3a018087", "metadata": { "editable": true }, @@ -4839,7 +4841,7 @@ }, { "cell_type": "markdown", - "id": "f825f2da", + "id": "e513a294", "metadata": { "editable": true }, @@ -4861,7 +4863,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "409b4250", + "id": "5a213611", "metadata": { "collapsed": false, "editable": true @@ -5002,7 +5004,7 @@ }, { "cell_type": "markdown", - "id": "c6830d86", + "id": "961917fd", "metadata": { "editable": true }, @@ -5018,7 +5020,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "041fc0bf", + "id": "e3745f70", "metadata": { "collapsed": false, "editable": true @@ -5031,7 +5033,7 @@ }, { "cell_type": "markdown", - "id": "0e18ef84", + "id": "22aca85a", "metadata": { "editable": true }, @@ -5043,7 +5045,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "8be0e7fd", + "id": "3c9a6d4a", "metadata": { "collapsed": false, "editable": true @@ -5065,7 +5067,7 @@ }, { "cell_type": "markdown", - "id": "2d2bc7a5", + "id": "6cea2036", "metadata": { "editable": true }, @@ -5081,7 +5083,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "f5cb107e", + "id": "76f5c3ab", "metadata": { "collapsed": false, "editable": true @@ -5119,7 +5121,7 @@ }, { "cell_type": "markdown", - "id": "beb4f622", + "id": "8cbf4208", "metadata": { "editable": true }, @@ -5132,7 +5134,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "1b508839", + "id": "d6e082d7", "metadata": { "collapsed": false, "editable": true @@ -5153,7 +5155,7 @@ }, { "cell_type": "markdown", - "id": "afb2e0af", + "id": "110f03f8", "metadata": { "editable": true }, @@ -5169,7 +5171,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "b96d6c89", + "id": "3c6712b7", "metadata": { "collapsed": false, "editable": true @@ -5227,7 +5229,7 @@ }, { "cell_type": "markdown", - "id": "0be588f8", + "id": "498d3949", "metadata": { "editable": true }, @@ -5242,7 +5244,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "49b8531e", + "id": "33947583", "metadata": { "collapsed": false, "editable": true @@ -5263,7 +5265,7 @@ }, { "cell_type": "markdown", - "id": "874d306a", + "id": "731fc79c", "metadata": { "editable": true }, @@ -5287,7 +5289,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "c25e9955", + "id": "f27ea6ab", "metadata": { "collapsed": false, "editable": true @@ -5759,7 +5761,7 @@ }, { "cell_type": "markdown", - "id": "86746540", + "id": "bf5cdac7", "metadata": { "editable": true }, @@ -5771,7 +5773,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "6f232f0a", + "id": "c57cb644", "metadata": { "collapsed": false, "editable": true @@ -5815,7 +5817,7 @@ }, { "cell_type": "markdown", - "id": "7227f21d", + "id": "c5864d33", "metadata": { "editable": true }, @@ -5831,7 +5833,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "944ba89b", + "id": "474c34e0", "metadata": { "collapsed": false, "editable": true @@ -5846,7 +5848,7 @@ }, { "cell_type": "markdown", - "id": "3cef110a", + "id": "74f3bc91", "metadata": { "editable": true }, @@ -5857,7 +5859,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "8eb39d5e", + "id": "a47d9dc5", "metadata": { "collapsed": false, "editable": true @@ -5872,7 +5874,7 @@ }, { "cell_type": "markdown", - "id": "4c0ab092", + "id": "bb2d666b", "metadata": { "editable": true }, @@ -5888,7 +5890,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "af77a32b", + "id": "f05cdd60", "metadata": { "collapsed": false, "editable": true @@ -5902,7 +5904,7 @@ }, { "cell_type": "markdown", - "id": "dfdb722f", + "id": "0034d61c", "metadata": { "editable": true }, @@ -5917,7 +5919,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "d740dbad", + "id": "67ecf987", "metadata": { "collapsed": false, "editable": true @@ -5943,7 +5945,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "bfb7c28b", + "id": "729ba5dd", "metadata": { "collapsed": false, "editable": true @@ -5958,7 +5960,7 @@ }, { "cell_type": "markdown", - "id": "17a8dc93", + "id": "719a054a", "metadata": { "editable": true }, @@ -5969,7 +5971,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "7efc0180", + "id": "7e58cd70", "metadata": { "collapsed": false, "editable": true @@ -5984,7 +5986,7 @@ }, { "cell_type": "markdown", - "id": "8255133c", + "id": "8225cc6d", "metadata": { "editable": true }, @@ -5995,7 +5997,7 @@ { "cell_type": "code", "execution_count": 39, - "id": "4fa47196", + "id": "a134deda", "metadata": { "collapsed": false, "editable": true @@ -6015,7 +6017,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "b7b5ed9f", + "id": "92aeced5", "metadata": { "collapsed": false, "editable": true @@ -6030,7 +6032,7 @@ }, { "cell_type": "markdown", - "id": "bc0fc41e", + "id": "3f0373a0", "metadata": { "editable": true }, @@ -6045,7 +6047,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "4ccf32f6", + "id": "02888bf9", "metadata": { "collapsed": false, "editable": true @@ -6082,7 +6084,7 @@ }, { "cell_type": "markdown", - "id": "382301fa", + "id": "e7714a6d", "metadata": { "editable": true }, @@ -6095,7 +6097,7 @@ { "cell_type": "code", "execution_count": 42, - "id": "0feb8f2a", + "id": "08a9206b", "metadata": { "collapsed": false, "editable": true @@ -6118,7 +6120,7 @@ }, { "cell_type": "markdown", - "id": "3f48285b", + "id": "82cfd04b", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index dbdf858f3..a8fdd3eb0 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -1066,13 +1066,13 @@ example of the functionality of Scikit-Learn.

    The intercept alpha: 
- [2.07549007]
+ [2.06336262]
 Coefficient beta : 
- [[5.14029264]]
-Mean squared error: 0.23
-Variance score: 0.89
+ [[4.80453713]]
+Mean squared error: 0.19
+Variance score: 0.90
 Mean squared log error: 0.01
-Mean absolute error: 0.38
+Mean absolute error: 0.35
    _images/chapter1_19_1.png @@ -1172,7 +1172,7 @@ a linear \(x\)-dependence we s
    _images/chapter1_33_0.png -
    0.004999999999999987
+
    0.004999999999999996
    diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index dd0d76d05..9ce806edc 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -1383,7 +1383,7 @@ the Hadamard product, meaning element-wise multiplication.

    Old accuracy on training data: 0.1440501043841336
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1717,7 +1717,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1726,7 +1726,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1735,7 +1735,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1744,7 +1744,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1753,7 +1753,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1762,7 +1762,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1771,34 +1771,191 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57122/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
-
    ---------------------------------------------------------------------------
-KeyboardInterrupt                         Traceback (most recent call last)
-Cell In[8], line 11
-      8 for j, lmbd in enumerate(lmbd_vals):
-      9     dnn = NeuralNetwork(X_train, Y_train_onehot, eta=eta, lmbd=lmbd, epochs=epochs, batch_size=batch_size,
-     10                         n_hidden_neurons=n_hidden_neurons, n_categories=n_categories)
----> 11     dnn.train()
-     13     DNN_numpy[i][j] = dnn
-     15     test_predict = dnn.predict(X_test)
-
-Cell In[6], line 98, in NeuralNetwork.train(self)
-     95 self.X_data = self.X_data_full[chosen_datapoints]
-     96 self.Y_data = self.Y_data_full[chosen_datapoints]
----> 98 self.feed_forward()
-     99 self.backpropagation()
-
-Cell In[6], line 38, in NeuralNetwork.feed_forward(self)
-     36 def feed_forward(self):
-     37     # feed-forward for training
----> 38     self.z_h = np.matmul(self.X_data, self.hidden_weights) + self.hidden_bias
-     39     self.a_h = sigmoid(self.z_h)
-     41     self.z_o = np.matmul(self.a_h, self.output_weights) + self.output_bias
-
-KeyboardInterrupt: 
+
    Learning rate  =  0.1
+Lambda =  10.0
+Accuracy score on test set:  0.09166666666666666
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  1.0
+Lambda =  0.001
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  1.0
+Lambda =  0.01
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  1.0
+Lambda =  0.1
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
    
+
    
+
    Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.10555555555555556
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  1.0
+Lambda =  10.0
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.07777777777777778
 
    
 
    
 
    
@@ -1844,6 +2001,22 @@ Accuracy score on test set:  0.08888888888888889
    +
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
    +
    +_images/chapter10_59_1.png +_images/chapter10_59_2.png +
    @@ -1879,6 +2052,327 @@ performance overall.

    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  1e-05
+Accuracy score on test set:  0.18333333333333332
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.0001
+Accuracy score on test set:  0.18611111111111112
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.001
+Accuracy score on test set:  0.13055555555555556
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.01
+Accuracy score on test set:  0.24444444444444444
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.1
+Accuracy score on test set:  0.23333333333333334
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  1.0
+Accuracy score on test set:  0.12777777777777777
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  10.0
+Accuracy score on test set:  0.1527777777777778
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  1e-05
+Accuracy score on test set:  0.9111111111111111
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.0001
+Accuracy score on test set:  0.8888888888888888
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.001
+Accuracy score on test set:  0.8722222222222222
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.01
+Accuracy score on test set:  0.8305555555555556
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.1
+Accuracy score on test set:  0.8888888888888888
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  1.0
+Accuracy score on test set:  0.8805555555555555
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  10.0
+Accuracy score on test set:  0.8944444444444445
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  1e-05
+Accuracy score on test set:  0.975
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.0001
+Accuracy score on test set:  0.9777777777777777
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.001
+Accuracy score on test set:  0.9805555555555555
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.01
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.1
+Accuracy score on test set:  0.9805555555555555
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  1.0
+Accuracy score on test set:  0.9777777777777777
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  10.0
+Accuracy score on test set:  0.9444444444444444
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.01
+Lambda =  1e-05
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.0001
+Accuracy score on test set:  0.9888888888888889
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.001
+Accuracy score on test set:  0.9888888888888889
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.01
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.1
+Accuracy score on test set:  0.9888888888888889
+
+Learning rate  =  0.01
+Lambda =  1.0
+Accuracy score on test set:  0.9722222222222222
+
    +
    +
    Learning rate  =  0.01
+Lambda =  10.0
+Accuracy score on test set:  0.9527777777777777
+
+Learning rate  =  0.1
+Lambda =  1e-05
+Accuracy score on test set:  0.9027777777777778
+
    +
    +
    Learning rate  =  0.1
+Lambda =  0.0001
+Accuracy score on test set:  0.8583333333333333
+
+Learning rate  =  0.1
+Lambda =  0.001
+Accuracy score on test set:  0.8722222222222222
+
    +
    +
    Learning rate  =  0.1
+Lambda =  0.01
+Accuracy score on test set:  0.9055555555555556
+
+Learning rate  =  0.1
+Lambda =  0.1
+Accuracy score on test set:  0.8805555555555555
+
    +
    +
    Learning rate  =  0.1
+Lambda =  1.0
+Accuracy score on test set:  0.8722222222222222
+
+Learning rate  =  0.1
+Lambda =  10.0
+Accuracy score on test set:  0.8666666666666667
+
    +
    +
    Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on test set:  0.08611111111111111
+
+Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  1.0
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
    +
    +
    Learning rate  =  1.0
+Lambda =  0.01
+Accuracy score on test set:  0.17777777777777778
+
+Learning rate  =  1.0
+Lambda =  0.1
+Accuracy score on test set:  0.08333333333333333
+
+Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.08888888888888889
+
    +
    +
    Learning rate  =  1.0
+Lambda =  10.0
+Accuracy score on test set:  0.09444444444444444
+
+Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.17222222222222222
+
+Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.11666666666666667
+
    +
    +
    Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.1388888888888889
+
+Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.11388888888888889
+
    +
    +
    Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.09444444444444444
+
    +
    +
    @@ -1922,6 +2416,10 @@ performance overall.

    +
    +_images/chapter10_63_0.png +_images/chapter10_63_1.png +
    @@ -1960,6 +2458,14 @@ and/or if you use anaconda, just write (or install from the gra
    +
    +
      Cell In[12], line 1
+    conda create -n tf tensorflow
+          ^
+SyntaxError: invalid syntax
+
    +
    +

    To install the current release of GPU TensorFlow

    diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index 832b3261a..d5e22c913 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -2709,63 +2709,50 @@ Using TensorFlow results in a much better execution time. Try it!

    19 x = tuple(args[i] for i in argnum) ---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:64, in jacobian(fun, x) - 62 jacobian_shape = ans_vspace.shape + vspace(x).shape - 63 grads = map(vjp, ans_vspace.standard_basis()) ----> 64 return np.reshape(np.stack(grads), jacobian_shape) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60, in jacobian(fun, x) + 50 @unary_to_nary + 51 def jacobian(fun, x): + 52 """ + 53 Returns a function which computes the Jacobian of `fun` with respect to + 54 positional argument number `argnum`, which must be a scalar or array. Unlike + (...) + 58 (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...). + 59 """ +---> 60 vjp, ans = _make_vjp(fun, x) + 61 ans_vspace = vspace(ans) + 62 jacobian_shape = ans_vspace.shape + vspace(x).shape -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88, in stack(arrays, axis) - 83 def stack(arrays, axis=0): - 84 # this code is basically copied from numpy/core/shape_base.py's stack - 85 # we need it here because we want to re-implement stack in terms of the - 86 # primitives defined in this file ----> 88 arrays = [array(arr) for arr in arrays] - 89 if not arrays: - 90 raise ValueError('need at least one array to stack') +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x) + 8 def make_vjp(fun, x): + 9 start_node = VJPNode.new_root() +---> 10 end_value, end_node = trace(start_node, fun, x) + 11 if end_node is None: + 12 def vjp(g): return vspace(x).zeros() -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88, in <listcomp>(.0) - 83 def stack(arrays, axis=0): - 84 # this code is basically copied from numpy/core/shape_base.py's stack - 85 # we need it here because we want to re-implement stack in terms of the - 86 # primitives defined in this file ----> 88 arrays = [array(arr) for arr in arrays] - 89 if not arrays: - 90 raise ValueError('need at least one array to stack') +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x) + 8 with trace_stack.new_trace() as t: + 9 start_box = new_box(x, t, start_node) +---> 10 end_box = fun(start_box) + 11 if isbox(end_box) and end_box._trace == start_box._trace: + 12 return end_box._value, end_box._node -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14, in make_vjp.<locals>.vjp(g) ----> 14 def vjp(g): return backward_pass(g, end_node) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x) + 13 else: + 14 subargs = subvals(args, zip(argnum, x)) +---> 15 return fun(*subargs, **kwargs) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21, in backward_pass(g, end_node) - 19 for node in toposort(end_node): - 20 outgrad = outgrads.pop(node) ----> 21 ingrads = node.vjp(outgrad[0]) - 22 for parent, ingrad in zip(node.parents, ingrads): - 23 outgrads[parent] = add_outgrads(outgrads.get(parent), ingrad) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x) + 13 else: + 14 subargs = subvals(args, zip(argnum, x)) +---> 15 return fun(*subargs, **kwargs) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67, in defvjp.<locals>.vjp_argnums.<locals>.<lambda>(g) - 64 raise NotImplementedError( - 65 "VJP of {} wrt argnum 0 not defined".format(fun.__name__)) - 66 vjp = vjpfun(ans, *args, **kwargs) ----> 67 return lambda g: (vjp(g),) - 68 elif L == 2: - 69 argnum_0, argnum_1 = argnums +Cell In[9], line 61, in g_trial(point, P) + 59 def g_trial(point,P): + 60 x,t = point +---> 61 return (1-t)*u(x) + x*(1-x)*t*deep_neural_network(P,point) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:423, in matmul_vjp_1.<locals>.<lambda>(g) - 421 A_ndim = anp.ndim(A) - 422 B_meta = anp.metadata(B) ---> 423 return lambda g: matmul_adjoint_1(A, g, A_ndim, B_meta) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:413, in matmul_adjoint_1(A, G, A_ndim, B_meta) - 411 if B_is_vec: - 412 result = anp.squeeze(result, anp.ndim(G) - 1) ---> 413 return unbroadcast(result, B_meta) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:653, in unbroadcast(x, target_meta, broadcast_idx) - 651 for axis, size in enumerate(target_shape): - 652 if size == 1: ---> 653 x = anp.sum(x, axis=axis, keepdims=True) - 654 if anp.iscomplexobj(x) and not target_iscomplex: - 655 x = anp.real(x) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:34, in ArrayBox.__rsub__(self, other) +---> 34 def __rsub__(self, other): return anp.subtract(other, self) File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45, in primitive.<locals>.f_wrapped(*args, **kwargs) 43 argnums = tuple(argnum for argnum, _ in boxed_args) @@ -2788,12 +2775,33 @@ Using TensorFlow results in a much better execution time. Try it!

    67 return lambda g: (vjp(g),) 68 elif L == 2: -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:297, in grad_np_sum(ans, x, axis, keepdims, dtype) - 294 return lambda g: anp.sum(g, axis=broadcast_axes, keepdims=True) - 295 defvjp(anp.broadcast_to, grad_broadcast_to) ---> 297 def grad_np_sum(ans, x, axis=None, keepdims=False, dtype=None): - 298 shape, dtype = anp.shape(x), anp.result_type(x) - 299 return lambda g: repeat_to_match_shape(g, shape, dtype, axis, keepdims)[0] +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:37, in <lambda>(ans, x, y) + 32 defvjp(anp.add, lambda ans, x, y : unbroadcast_f(x, lambda g: g), + 33 lambda ans, x, y : unbroadcast_f(y, lambda g: g)) + 34 defvjp(anp.multiply, lambda ans, x, y : unbroadcast_f(x, lambda g: y * g), + 35 lambda ans, x, y : unbroadcast_f(y, lambda g: x * g)) + 36 defvjp(anp.subtract, lambda ans, x, y : unbroadcast_f(x, lambda g: g), +---> 37 lambda ans, x, y : unbroadcast_f(y, lambda g: -g)) + 38 defvjp(anp.divide, lambda ans, x, y : unbroadcast_f(x, lambda g: g / y), + 39 lambda ans, x, y : unbroadcast_f(y, lambda g: - g * x / y**2)) + 40 defvjp(anp.maximum, lambda ans, x, y : unbroadcast_f(x, lambda g: g * balanced_eq(x, ans, y)), + 41 lambda ans, x, y : unbroadcast_f(y, lambda g: g * balanced_eq(y, ans, x))) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:659, in unbroadcast_f(target, f) + 658 def unbroadcast_f(target, f): +--> 659 target_meta = anp.metadata(target) + 660 return lambda g: unbroadcast(f(g), target_meta) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:61, in notrace_primitive.<locals>.f_wrapped(*args, **kwargs) + 58 @wraps(f_raw) + 59 def f_wrapped(*args, **kwargs): + 60 argvals = map(getval, args) +---> 61 return f_raw(*argvals, **kwargs) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:148, in metadata(A) + 146 @notrace_primitive + 147 def metadata(A): +--> 148 return _np.shape(A), _np.ndim(A), _np.result_type(A), _np.iscomplexobj(A) KeyboardInterrupt:
    diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html index dbaa20168..5f68c7e9a 100644 --- a/doc/LectureNotes/_build/html/chapter2.html +++ b/doc/LectureNotes/_build/html/chapter2.html @@ -1315,10 +1315,10 @@ covariance matrix through the np.linalg.eig() function.

    -
    0.04718566894028431
-4.11080997912276
-[[ 1.10517643  3.48455788]
- [ 3.48455788 12.00216162]]
+
    -0.014394967608286841
+4.011594819155615
+[[ 1.24508783  3.8595836 ]
+ [ 3.8595836  12.92663007]]
    @@ -1355,10 +1355,10 @@ a more brute force way. Here we scale the mean values for each column of the des
    -
    0.07836997022107646
-1.1378267322316808
-[[1.         0.63980097]
- [0.63980097 1.        ]]
+
    0.07630326327869198
+1.6893421391051477
+[[1.        0.6373454]
+ [0.6373454 1.       ]]
    @@ -1388,30 +1388,30 @@ this matrix we easily see that it is a positive definite matrix.

    -
    [[ 1.34931214  3.06139439]
- [-0.44476964 -2.60794187]
- [ 0.02225493  0.16388664]
- [-1.91193672 -3.82324216]
- [-0.2044881  -1.56027537]
- [-1.15572395 -3.25982474]
- [ 0.94217756  1.49888671]
- [ 0.28472162  2.92474572]
- [ 2.38943     7.14118216]
- [-1.27097785 -3.5388115 ]]
+
    [[ 0.20396326  0.93053605]
+ [ 0.51936974  2.03440431]
+ [-0.53851084 -1.2527027 ]
+ [-0.50429483  0.72966563]
+ [ 0.71314288  1.60421343]
+ [-0.29995377 -2.31356849]
+ [-0.14484831 -2.19932442]
+ [-0.00570826 -0.32643011]
+ [-0.22821607  0.56775345]
+ [ 0.28505619  0.22545286]]
           0         1
-0  1.349312  3.061394
-1 -0.444770 -2.607942
-2  0.022255  0.163887
-3 -1.911937 -3.823242
-4 -0.204488 -1.560275
-5 -1.155724 -3.259825
-6  0.942178  1.498887
-7  0.284722  2.924746
-8  2.389430  7.141182
-9 -1.270978 -3.538811
-          0         1
-0  1.000000  0.950873
-1  0.950873  1.000000
+0  0.203963  0.930536
+1  0.519370  2.034404
+2 -0.538511 -1.252703
+3 -0.504295  0.729666
+4  0.713143  1.604213
+5 -0.299954 -2.313568
+6 -0.144848 -2.199324
+7 -0.005708 -0.326430
+8 -0.228216  0.567753
+9  0.285056  0.225453
+        0       1
+0  1.0000  0.6373
+1  0.6373  1.0000
    @@ -1468,37 +1468,40 @@ this matrix we easily see that it is a positive definite matrix.

         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.074334  0.080585  0.077061  0.078751  0.080220  0.070657  0.071406   
-2   0.0  0.080585  0.088425  0.082009  0.084289  0.086338  0.074009  0.075052   
-3   0.0  0.077061  0.082009  0.085147  0.086339  0.087297  0.081324  0.081796   
-4   0.0  0.078751  0.084289  0.086339  0.087789  0.089007  0.081926  0.082537   
-5   0.0  0.080220  0.086338  0.087297  0.089007  0.090492  0.082307  0.083061   
-6   0.0  0.070657  0.074009  0.081324  0.081926  0.082307  0.079874  0.080032   
-7   0.0  0.071406  0.075052  0.081796  0.082537  0.083061  0.080032  0.080271   
-8   0.0  0.072148  0.076101  0.082240  0.083128  0.083801  0.080150  0.080474   
-9   0.0  0.072902  0.077180  0.082670  0.083714  0.084548  0.080237  0.080651   
-10  0.0  0.063646  0.065859  0.075320  0.075498  0.075472  0.075478  0.075409   
-11  0.0  0.064071  0.066452  0.075576  0.075838  0.075897  0.075542  0.075525   
-12  0.0  0.064514  0.067074  0.075838  0.076189  0.076340  0.075602  0.075639   
-13  0.0  0.064980  0.067731  0.076108  0.076555  0.076803  0.075658  0.075753   
-14  0.0  0.065472  0.068429  0.076389  0.076938  0.077292  0.075711  0.075868   
+1   0.0  0.072835  0.076616  0.071253  0.074193  0.077337  0.063283  0.065305   
+2   0.0  0.076616  0.082149  0.073794  0.077643  0.081811  0.064373  0.066929   
+3   0.0  0.071253  0.073794  0.075423  0.077534  0.079745  0.070414  0.071978   
+4   0.0  0.074193  0.077643  0.077534  0.080199  0.083034  0.071575  0.073483   
+5   0.0  0.077337  0.081811  0.079745  0.083034  0.086576  0.072733  0.075028   
+6   0.0  0.063283  0.064373  0.070414  0.071575  0.072733  0.068008  0.068984   
+7   0.0  0.065305  0.066929  0.071978  0.073483  0.075028  0.068984  0.070181   
+8   0.0  0.067523  0.069754  0.073667  0.075563  0.077550  0.070010  0.071459   
+9   0.0  0.069964  0.072888  0.075496  0.077839  0.080330  0.071092  0.072826   
+10  0.0  0.055866  0.055925  0.064324  0.064790  0.065183  0.063660  0.064182   
+11  0.0  0.057298  0.057682  0.065508  0.066192  0.066830  0.064471  0.065139   
+12  0.0  0.058866  0.059624  0.066786  0.067719  0.068637  0.065330  0.066162   
+13  0.0  0.060590  0.061775  0.068171  0.069388  0.070625  0.066241  0.067260   
+14  0.0  0.062489  0.064164  0.069675  0.071217  0.072820  0.067209  0.068440   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.072148  0.072902  0.063646  0.064071  0.064514  0.064980  0.065472  
-2   0.076101  0.077180  0.065859  0.066452  0.067074  0.067731  0.068429  
-3   0.082240  0.082670  0.075320  0.075576  0.075838  0.076108  0.076389  
-4   0.083128  0.083714  0.075498  0.075838  0.076189  0.076555  0.076938  
-5   0.083801  0.084548  0.075472  0.075897  0.076340  0.076803  0.077292  
-6   0.080150  0.080237  0.075478  0.075542  0.075602  0.075658  0.075711  
-7   0.080474  0.080651  0.075409  0.075525  0.075639  0.075753  0.075868  
-8   0.080766  0.081038  0.075293  0.075463  0.075634  0.075809  0.075988  
-9   0.081038  0.081411  0.075136  0.075363  0.075595  0.075834  0.076082  
-10  0.075293  0.075136  0.072406  0.072329  0.072240  0.072140  0.072028  
-11  0.075463  0.075363  0.072329  0.072286  0.072234  0.072173  0.072101  
-12  0.075634  0.075595  0.072240  0.072234  0.072220  0.072199  0.072171  
-13  0.075809  0.075834  0.072140  0.072173  0.072199  0.072221  0.072238  
-14  0.075988  0.076082  0.072028  0.072101  0.072171  0.072238  0.072303  
+1   0.067523  0.069964  0.055866  0.057298  0.058866  0.060590  0.062489  
+2   0.069754  0.072888  0.055925  0.057682  0.059624  0.061775  0.064164  
+3   0.073667  0.075496  0.064324  0.065508  0.066786  0.068171  0.069675  
+4   0.075563  0.077839  0.064790  0.066192  0.067719  0.069388  0.071217  
+5   0.077550  0.080330  0.065183  0.066830  0.068637  0.070625  0.072820  
+6   0.070010  0.071092  0.063660  0.064471  0.065330  0.066241  0.067209  
+7   0.071459  0.072826  0.064182  0.065139  0.066162  0.067260  0.068440  
+8   0.073021  0.074713  0.064703  0.065823  0.067032  0.068341  0.069761  
+9   0.074713  0.076776  0.065219  0.066524  0.067943  0.069492  0.071186  
+10  0.064703  0.065219  0.060678  0.061187  0.061707  0.062241  0.062786  
+11  0.065823  0.066524  0.061187  0.061793  0.062424  0.063082  0.063768  
+12  0.067032  0.067943  0.061707  0.062424  0.063180  0.063978  0.064823  
+13  0.068341  0.069492  0.062241  0.063082  0.063978  0.064935  0.065960  
+14  0.069761  0.071186  0.062786  0.063768  0.064823  0.065960  0.067192  
+
    +
    +
    @@ -1979,13 +1982,11 @@ We select values of the hyperparameter 
    [2. 2.]
-
    -
    -
    Training MSE for OLS
+Training MSE for OLS
 3.0
    -_images/chapter2_252_2.png +_images/chapter2_252_1.png

    We see here that we reach a plateau for the Ridge results. Writing out the coefficients \(\boldsymbol{\beta}\), we observe that they are getting smaller and smaller and our error stabilizes since the predicted values of \(\tilde{\boldsymbol{y}}\) approach zero.

    diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index 1bb44cff4..77d462af5 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -869,10 +869,10 @@ number \(i\) is left out. Usin
    -
    Runtime: 0.154751 sec
+
    Runtime: 0.136066 sec
 Jackknife Statistics :
 original           bias      std. error
- 99.9896        99.9796        0.149524
+ 99.8762        99.8662        0.150735
    @@ -1091,7 +1091,7 @@ theorem.

    Bootstrap Statistics :
 original           bias      std. error
- 100.307  14.9693        100.309        0.149416
+ 99.9623  14.9594        99.9606         0.14934
    @@ -1298,9 +1298,7 @@ Error: 0.08426840630693411 Bias^2: 0.0796891867672603 Var: 0.004579219539673834 0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413 -
    -
    -
    Polynomial degree: 2
+Polynomial degree: 2
 Error: 0.10398646080125035
 Bias^2: 0.1007711427354898
 Var: 0.0032153180657605116
@@ -1327,9 +1325,7 @@ Error: 0.037813671417389005
 Bias^2: 0.033657685071527665
 Var: 0.00415598634586135
 0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902
-
    -
    -
    Polynomial degree: 7
+Polynomial degree: 7
 Error: 0.02760977349102253
 Bias^2: 0.022999498260366312
 Var: 0.004610275230656212
@@ -1356,9 +1352,7 @@ Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
 0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
-
    -
    -
    Polynomial degree: 12
+Polynomial degree: 12
 Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
@@ -1370,7 +1364,7 @@ Var: 0.20867052175034223
 0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
    -_images/chapter3_66_6.png +_images/chapter3_66_3.png

    The bias-variance tradeoff summarizes the fundamental tension in @@ -1661,12 +1655,12 @@ Mean squared error on test data: 877.21517262 Degree of polynomial: 23 Mean squared error on training data: 0.00085892 Mean squared error on test data: 5567.04664255 - - -

    Degree of polynomial:  24
+Degree of polynomial:  24
 Mean squared error on training data: 0.00084707
 Mean squared error on test data: 1325.26124692
-Degree of polynomial:  25
+
    +
    +
    Degree of polynomial:  25
 Mean squared error on training data: 0.00079125
 Mean squared error on test data: 129012.83870189
 Degree of polynomial:  26
@@ -1675,19 +1669,19 @@ Mean squared error on test data: 18388.59354079
 Degree of polynomial:  27
 Mean squared error on training data: 0.00069123
 Mean squared error on test data: 2351.97979891
-
    -
    -
    Degree of polynomial:  28
+Degree of polynomial:  28
 Mean squared error on training data: 0.00062592
 Mean squared error on test data: 3983.63037846
-Degree of polynomial:  29
+
    +
    +
    Degree of polynomial:  29
 Mean squared error on training data: 0.00060704
 Mean squared error on test data: 3262.26814548
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -1921,7 +1915,7 @@ cross-validation (LOOCV).
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
@@ -2810,7 +2804,7 @@ linear system as an equation would reduce this down to
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2954,7 +2948,7 @@ with the form utilized in linear regression, viz.
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2994,7 +2988,7 @@ cost function is given by
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -3029,7 +3023,7 @@ cost function is given by
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57183/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59625/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -3082,43 +3076,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
    -
      0%|                                                                                                                                              | 0/10 [00:00<?, ?it/s]
+
      0%|                                                                                                                   | 0/10 [00:00<?, ?it/s]
 
    
 
    
 
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
   model = cd_fast.enet_coordinate_descent(
 
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+ 10%|██████████▋                                                                                                | 1/10 [00:00<00:07,  1.23it/s]
 
    
 
    
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     20%|█████████████████████▍                                                                                     | 2/10 [00:01<00:06,  1.15it/s]
 
    
 
    
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diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index 5e85b0cbf..7691aa0a4 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -797,9 +797,9 @@ predicting the target features of query instances is as follows:
    
 
    
 
    
 
    2nd degree coefficients:
-zero power:  -0.22158725223474995
-first power:  0.24121476598003452
-second power:  -0.0009532583857747255
+zero power:  4.58641507967355
+first power:  -0.2107546970521115
+second power:  0.0003919793592273513
 
    
 
    
 _images/chapter6_1_1.png
@@ -1662,11 +1662,13 @@ attributes at each step while growing the tree.
    
 
    
 
    (426, 30)
 (143, 30)
+Test set accuracy with Logistic Regression: 0.94
 
    
 
    
-
    Test set accuracy with Logistic Regression: 0.94
-Test set accuracy with SVM: 0.63
-Test set accuracy with Decision Trees: 0.90
+
    Test set accuracy with SVM: 0.63
+
    
+
    
+
    Test set accuracy with Decision Trees: 0.90
 Test set accuracy Logistic Regression with scaled data: 0.96
 Test set accuracy SVM with scaled data: 0.96
 Test set accuracy with Decision Trees and scaled data: 0.89
diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html
index a38643236..9fc918120 100644
--- a/doc/LectureNotes/_build/html/chapter8.html
+++ b/doc/LectureNotes/_build/html/chapter8.html
@@ -751,10 +751,10 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.1001408041761458
-4.2807716628772665
-[[ 1.15654145  3.54867722]
- [ 3.54867722 11.70485195]]
+
    0.05665875086534638
+4.128393685824704
+[[ 0.93987367  2.98650457]
+ [ 2.98650457 10.47544463]]
 
    
 
    
 
    
@@ -794,10 +794,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 
    
 
    
 
    
-
    0.09543871010617433
-1.6888043337746685
-[[1.        0.7167077]
- [0.7167077 1.       ]]
+
    0.07617734331359052
+1.6957182489166325
+[[1.         0.68029423]
+ [0.68029423 1.        ]]
 
    
 
    
 
    
@@ -826,30 +826,30 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
    
 
    
-
    [[-0.20575734  0.01384583]
- [-0.89876098 -3.04065686]
- [-0.76289128 -3.17080691]
- [-0.0334136   0.16124569]
- [ 2.73970542  9.28885103]
- [ 0.75413023  2.98474769]
- [-1.87894459 -5.48121459]
- [-1.26814205 -2.4848097 ]
- [ 0.18114057 -0.9889962 ]
- [ 1.37293361  2.71779401]]
+
    [[-0.29087396  0.13244119]
+ [-1.21617146 -2.69678073]
+ [-1.37024276 -3.76728511]
+ [ 0.49342785  1.50638863]
+ [ 0.4155974  -0.07435812]
+ [ 0.64813145  1.94455739]
+ [-0.48364163 -2.62178739]
+ [-0.3807176  -1.25007671]
+ [ 1.73036439  5.00709577]
+ [ 0.45412633  1.81980509]]
           0         1
-0 -0.205757  0.013846
-1 -0.898761 -3.040657
-2 -0.762891 -3.170807
-3 -0.033414  0.161246
-4  2.739705  9.288851
-5  0.754130  2.984748
-6 -1.878945 -5.481215
-7 -1.268142 -2.484810
-8  0.181141 -0.988996
-9  1.372934  2.717794
+0 -0.290874  0.132441
+1 -1.216171 -2.696781
+2 -1.370243 -3.767285
+3  0.493428  1.506389
+4  0.415597 -0.074358
+5  0.648131  1.944557
+6 -0.483642 -2.621787
+7 -0.380718 -1.250077
+8  1.730364  5.007096
+9  0.454126  1.819805
           0         1
-0  1.000000  0.970965
-1  0.970965  1.000000
+0  1.000000  0.961042
+1  0.961042  1.000000
 
    
 
    
 
    
@@ -906,37 +906,37 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.090565  0.089065  0.093226  0.091674  0.090154  0.086287  0.084847   
-2   0.0  0.089065  0.087946  0.092421  0.091068  0.089735  0.085988  0.084674   
-3   0.0  0.093226  0.092421  0.102147  0.100816  0.099494  0.098144  0.096731   
-4   0.0  0.091674  0.091068  0.100816  0.099622  0.098431  0.097115  0.095803   
-5   0.0  0.090154  0.089735  0.099494  0.098431  0.097365  0.096077  0.094862   
-6   0.0  0.086287  0.085988  0.098144  0.097115  0.096077  0.096630  0.095395   
-7   0.0  0.084847  0.084674  0.096731  0.095803  0.094862  0.095395  0.094243   
-8   0.0  0.083459  0.083405  0.095358  0.094527  0.093680  0.094189  0.093115   
-9   0.0  0.082121  0.082180  0.094027  0.093288  0.092530  0.093011  0.092013   
-10  0.0  0.078708  0.078711  0.091732  0.090935  0.090118  0.091871  0.090804   
-11  0.0  0.077431  0.077523  0.090387  0.089668  0.088926  0.090626  0.089626   
-12  0.0  0.076203  0.076378  0.089086  0.088441  0.087772  0.089417  0.088481   
-13  0.0  0.075021  0.075274  0.087828  0.087255  0.086655  0.088242  0.087369   
-14  0.0  0.073883  0.074212  0.086611  0.086107  0.085573  0.087102  0.086289   
+1   0.0  0.068131  0.073423  0.069509  0.072262  0.074935  0.062568  0.064540   
+2   0.0  0.073423  0.079638  0.075544  0.078741  0.081836  0.068222  0.070480   
+3   0.0  0.069509  0.075544  0.075689  0.079013  0.082264  0.070991  0.073416   
+4   0.0  0.072262  0.078741  0.079013  0.082596  0.086101  0.074265  0.076876   
+5   0.0  0.074935  0.081836  0.082264  0.086101  0.089859  0.077488  0.080287   
+6   0.0  0.062568  0.068222  0.070991  0.074265  0.077488  0.068479  0.070924   
+7   0.0  0.064540  0.070480  0.073416  0.076876  0.080287  0.070924  0.073514   
+8   0.0  0.066545  0.072773  0.075883  0.079535  0.083138  0.073418  0.076157   
+9   0.0  0.068597  0.075114  0.078409  0.082256  0.086058  0.075974  0.078867   
+10  0.0  0.055105  0.060160  0.064322  0.067367  0.070380  0.063327  0.065650   
+11  0.0  0.056730  0.062004  0.066330  0.069526  0.072694  0.065375  0.067819   
+12  0.0  0.058409  0.063910  0.068406  0.071759  0.075086  0.067492  0.070063   
+13  0.0  0.060149  0.065884  0.070556  0.074073  0.077566  0.069686  0.072388   
+14  0.0  0.061956  0.067932  0.072787  0.076473  0.080139  0.071962  0.074802   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.083459  0.082121  0.078708  0.077431  0.076203  0.075021  0.073883  
-2   0.083405  0.082180  0.078711  0.077523  0.076378  0.075274  0.074212  
-3   0.095358  0.094027  0.091732  0.090387  0.089086  0.087828  0.086611  
-4   0.094527  0.093288  0.090935  0.089668  0.088441  0.087255  0.086107  
-5   0.093680  0.092530  0.090118  0.088926  0.087772  0.086655  0.085573  
-6   0.094189  0.093011  0.091871  0.090626  0.089417  0.088242  0.087102  
-7   0.093115  0.092013  0.090804  0.089626  0.088481  0.087369  0.086289  
-8   0.092064  0.091034  0.089755  0.088642  0.087560  0.086508  0.085486  
-9   0.091034  0.090075  0.088726  0.087675  0.086653  0.085659  0.084694  
-10  0.089755  0.088726  0.088455  0.087327  0.086227  0.085155  0.084112  
-11  0.088642  0.087675  0.087327  0.086256  0.085212  0.084195  0.083203  
-12  0.087560  0.086653  0.086227  0.085212  0.084222  0.083257  0.082316  
-13  0.086508  0.085659  0.085155  0.084195  0.083257  0.082342  0.081450  
-14  0.085486  0.084694  0.084112  0.083203  0.082316  0.081450  0.080605  
+1   0.066545  0.068597  0.055105  0.056730  0.058409  0.060149  0.061956  
+2   0.072773  0.075114  0.060160  0.062004  0.063910  0.065884  0.067932  
+3   0.075883  0.078409  0.064322  0.066330  0.068406  0.070556  0.072787  
+4   0.079535  0.082256  0.067367  0.069526  0.071759  0.074073  0.076473  
+5   0.083138  0.086058  0.070380  0.072694  0.075086  0.077566  0.080139  
+6   0.073418  0.075974  0.063327  0.065375  0.067492  0.069686  0.071962  
+7   0.076157  0.078867  0.065650  0.067819  0.070063  0.072388  0.074802  
+8   0.078953  0.081823  0.068024  0.070318  0.072693  0.075155  0.077711  
+9   0.081823  0.084858  0.070460  0.072885  0.075396  0.078000  0.080704  
+10  0.068024  0.070460  0.059480  0.061447  0.063482  0.065591  0.067780  
+11  0.070318  0.072885  0.061447  0.063517  0.065661  0.067883  0.070190  
+12  0.072693  0.075396  0.063482  0.065661  0.067917  0.070258  0.072688  
+13  0.075155  0.078000  0.065591  0.067883  0.070258  0.072721  0.075281  
+14  0.077711  0.080704  0.067780  0.070190  0.072688  0.075281  0.077975  
 
    
 
    
 
    
@@ -1125,12 +1125,10 @@ We can write our own code or simply use either the functionaly of numpy<
 
    
 
    
 
              0         1
-0  4.114499  2.071143
-1  2.071143  2.061388
-
    
-
    
-
    [[4.11449851 2.07114326]
- [2.07114326 2.0613875 ]]
+0  4.060824  2.038890
+1  2.038890  2.029664
+[[4.06082419 2.03888998]
+ [2.03888998 2.02966421]]
 
    
 
    
 
    
@@ -1157,8 +1155,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
 
    
 
    
 
    Centered covariance using own code
-[[4.11449851 2.07114326]
- [2.07114326 2.0613875 ]]
+[[4.06082419 2.03888998]
+ [2.03888998 2.02966421]]
 
    
 
    
 _images/chapter8_65_1.png
@@ -1218,16 +1216,16 @@ questions.
    
 
    
 
    
 
    Eigenvalues of Covariance matrix
-5.399533503407795
-0.776352510835556
+5.323066643161066
+0.7674217625964539
 First eigenvector
-[0.84973247 0.52721412]
+[0.85025162 0.52637646]
 Second eigenvector
-[-0.52721412  0.84973247]
+[-0.52637646  0.85025162]
 
    
 
    
 
    Eigenvector of largest eigenvalue
-[-0.84973247 -0.52721412]
+[-0.85025162 -0.52637646]
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/exercisesweek41.html b/doc/LectureNotes/_build/html/exercisesweek41.html
index 39e5531ee..e613551dd 100644
--- a/doc/LectureNotes/_build/html/exercisesweek41.html
+++ b/doc/LectureNotes/_build/html/exercisesweek41.html
@@ -804,15 +804,15 @@ regression.
    
 
    
 
    
 
    Own inversion
-[[4.1729993]
- [3.0170097]]
-Eigenvalues of Hessian Matrix:[0.28192769 4.68753434]
+[[3.99556258]
+ [2.99418621]]
+Eigenvalues of Hessian Matrix:[0.29411652 4.64408368]
 theta from own gd
-[[4.1729993]
- [3.0170097]]
+[[3.99556258]
+ [2.99418621]]
 theta from own sdg
-[[4.14043884]
- [2.99071523]]
+[[4.03364384]
+ [2.99870462]]
 
    
 
    
 _images/exercisesweek41_5_1.png
@@ -934,14 +934,14 @@ first example shows results with ordinary leats squares.
    
 
    
 
    
 
    Own inversion
-[[4.03696458]
- [3.0324793 ]]
-Eigenvalues of Hessian Matrix:[0.30959659 4.4150026 ]
+[[3.94033034]
+ [3.07147868]]
+Eigenvalues of Hessian Matrix:[0.26240613 4.76140219]
 
    
 
    
 
    theta from own gd
-[[4.03696458]
- [3.0324793 ]]
+[[3.94033034]
+ [3.07147868]]
 
    
 
    
 _images/exercisesweek41_16_2.png
@@ -1012,73 +1012,73 @@ Eigenvalues of Hessian Matrix:[0.30959659 4.4150026 ]
 
    Own inversion
 [[4.]
  [3.]]
-Eigenvalues of Hessian Matrix:[0.23469347 4.90407685]
-0 [-18.02987043] [-22.42501752]
-1 [-0.32329897] [0.25206371]
-2 [-0.30782691] [0.24000075]
-3 [-0.2930953] [0.22851508]
-4 [-0.27906869] [0.21757908]
-5 [-0.26571335] [0.20716644]
-6 [-0.25299716] [0.19725211]
-7 [-0.24088952] [0.18781225]
-8 [-0.22936132] [0.17882416]
-9 [-0.21838482] [0.17026621]
-10 [-0.20793362] [0.16211781]
-11 [-0.19798258] [0.15435937]
-12 [-0.18850776] [0.14697222]
-13 [-0.17948638] [0.1399386]
-14 [-0.17089674] [0.13324158]
-15 [-0.16271817] [0.12686507]
-16 [-0.15493099] [0.12079371]
-17 [-0.14751649] [0.11501291]
-18 [-0.14045682] [0.10950876]
-19 [-0.13373501] [0.10426802]
-20 [-0.12733488] [0.09927808]
-21 [-0.12124104] [0.09452695]
-22 [-0.11543883] [0.09000319]
-23 [-0.10991429] [0.08569593]
-24 [-0.10465415] [0.08159479]
-25 [-0.09964573] [0.07768993]
-26 [-0.094877] [0.07397193]
-27 [-0.09033649] [0.07043187]
-28 [-0.08601328] [0.06706123]
-29 [-0.08189696] [0.06385189]
+Eigenvalues of Hessian Matrix:[0.30332201 4.25820894]
+0 [-14.184119] [-15.76632434]
+1 [-0.27465664] [0.23807169]
+2 [-0.25509222] [0.2211133]
+3 [-0.23692142] [0.20536289]
+4 [-0.22004496] [0.19073442]
+5 [-0.20437065] [0.17714797]
+6 [-0.18981286] [0.16452931]
+7 [-0.17629205] [0.15280951]
+8 [-0.16373437] [0.14192454]
+9 [-0.15207119] [0.13181493]
+10 [-0.14123881] [0.12242545]
+11 [-0.13117805] [0.1137048]
+12 [-0.12183393] [0.10560535]
+13 [-0.11315542] [0.09808284]
+14 [-0.1050951] [0.09109617]
+15 [-0.09760893] [0.08460718]
+16 [-0.09065603] [0.07858042]
+17 [-0.08419839] [0.07298295]
+18 [-0.07820074] [0.06778421]
+19 [-0.07263033] [0.06295579]
+20 [-0.0674567] [0.0584713]
+21 [-0.06265161] [0.05430625]
+22 [-0.05818879] [0.0504379]
+23 [-0.05404387] [0.04684509]
+24 [-0.0501942] [0.04350821]
+25 [-0.04661875] [0.04040902]
+26 [-0.04329799] [0.03753059]
+27 [-0.04021377] [0.0348572]
+28 [-0.03734925] [0.03237424]
+29 [-0.03468878] [0.03006815]
 theta from own gd
-[[3.66774691]
- [3.25904489]]
-0 [-0.07797763] [0.06079614]
-1 [-0.07424587] [0.05788664]
-2 [-0.06957317] [0.05424351]
-3 [-0.06484181] [0.05055465]
-4 [-0.06031928] [0.04702861]
-5 [-0.05607583] [0.04372016]
-6 [-0.05211919] [0.04063532]
-7 [-0.04843794] [0.03776519]
-8 [-0.04501548] [0.03509683]
-9 [-0.04183444] [0.0326167]
-10 [-0.03887807] [0.03031173]
-11 [-0.03613058] [0.02816961]
-12 [-0.03357723] [0.02617887]
-13 [-0.03120433] [0.02432881]
-14 [-0.02899912] [0.02260949]
-15 [-0.02694975] [0.02101168]
-16 [-0.02504521] [0.01952679]
-17 [-0.02327527] [0.01814683]
-18 [-0.0216304] [0.01686439]
-19 [-0.02010178] [0.01567258]
-20 [-0.01868119] [0.014565]
-21 [-0.01736099] [0.01353569]
-22 [-0.01613409] [0.01257912]
-23 [-0.01499389] [0.01169016]
-24 [-0.01393427] [0.01086401]
-25 [-0.01294954] [0.01009625]
-26 [-0.01203439] [0.00938275]
-27 [-0.01118392] [0.00871967]
-28 [-0.01039355] [0.00810345]
-29 [-0.00965904] [0.00753078]
+[[3.89378344]
+ [3.09206825]]
+0 [-0.03221782] [0.02792633]
+1 [-0.02992287] [0.02593707]
+2 [-0.02710291] [0.02349274]
+3 [-0.02432632] [0.02108599]
+4 [-0.02176052] [0.01886197]
+5 [-0.01944073] [0.01685118]
+6 [-0.01735999] [0.01504759]
+7 [-0.01549917] [0.01343464]
+8 [-0.01383689] [0.01199378]
+9 [-0.01235257] [0.01070717]
+10 [-0.01102737] [0.0095585]
+11 [-0.0098443] [0.00853302]
+12 [-0.00878815] [0.00761755]
+13 [-0.00784531] [0.00680029]
+14 [-0.00700361] [0.00607072]
+15 [-0.00625222] [0.00541941]
+16 [-0.00558145] [0.00483798]
+17 [-0.00498263] [0.00431893]
+18 [-0.00444806] [0.00385557]
+19 [-0.00397085] [0.00344192]
+20 [-0.00354483] [0.00307265]
+21 [-0.00316452] [0.002743]
+22 [-0.00282501] [0.00244871]
+23 [-0.00252192] [0.002186]
+24 [-0.00225136] [0.00195147]
+25 [-0.00200982] [0.0017421]
+26 [-0.00179419] [0.0015552]
+27 [-0.0016017] [0.00138835]
+28 [-0.00142986] [0.0012394]
+29 [-0.00127645] [0.00110643]
 theta from own gd wth momentum
-[[3.96175251]
- [3.02982009]]
+[[3.99624324]
+ [3.00325635]]
 
    
 
    
 
    
@@ -1131,17 +1131,17 @@ theta from own gd wth momentum
 
    
 
    
 
    Own inversion
-[[4.15451852]
- [2.83230774]]
-Eigenvalues of Hessian Matrix:[0.30616802 4.24299211]
-0 [-10.57502449] [-11.57610367]
-1 [-4.47905601e-15] [-4.07372439e-16]
-2 [-6.9388939e-16] [-7.21432413e-16]
-3 [-6.9388939e-16] [-7.21432413e-16]
-4 [-6.9388939e-16] [-7.21432413e-16]
+[[3.86346614]
+ [3.11457699]]
+Eigenvalues of Hessian Matrix:[0.29561686 4.57324367]
+0 [-14.07510754] [-17.31973052]
+1 [1.77982629e-15] [1.66047885e-15]
+2 [1.66533454e-16] [2.95162248e-16]
+3 [1.66533454e-16] [2.95162248e-16]
+4 [1.66533454e-16] [2.95162248e-16]
 beta from own Newton code
-[[4.15451852]
- [2.83230774]]
+[[3.86346614]
+ [3.11457699]]
 
    
 
    
 
    
@@ -1230,20 +1230,18 @@ beta from own Newton code
 
    
 
    
 
    Own inversion
-[[4.04601419]
- [3.12204312]]
-Eigenvalues of Hessian Matrix:[0.33604208 4.45709724]
+[[4.08540984]
+ [2.92466997]]
+Eigenvalues of Hessian Matrix:[0.32128064 4.10407019]
+theta from own gd
+[[4.08540984]
+ [2.92466997]]
 
    
 
    
-
    theta from own gd
-[[4.04601419]
- [3.12204312]]
-
    
-
    
-_images/exercisesweek41_22_2.png
+_images/exercisesweek41_22_1.png
 
    theta from own sdg
-[[4.02781444]
- [3.13976073]]
+[[4.02171224]
+ [2.94008244]]
 
    
 
    
 
    
@@ -1325,15 +1323,17 @@ Eigenvalues of Hessian Matrix:[0.33604208 4.45709724]
 
    
 
    
 
    Own inversion
-[[3.96417888]
- [3.06634473]]
-Eigenvalues of Hessian Matrix:[0.32962444 4.18715465]
+[[3.72066289]
+ [3.08799729]]
+Eigenvalues of Hessian Matrix:[0.28726748 4.35313779]
 theta from own gd
-[[3.9639885]
- [3.0665111]]
-theta from own sdg with momentum
-[[4.00842216]
- [3.14285244]]
+[[3.72055288]
+ [3.08809115]]
+
    
+
    
+
    theta from own sdg with momentum
+[[3.72183526]
+ [3.12143852]]
 
    
 
    
 
    
@@ -1408,9 +1408,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own AdaGrad
-[[1.99969895]
- [3.00167058]
- [3.99835872]]
+[[2.00001365]
+ [2.99991971]
+ [4.00007964]]
 
    
 
    
 
    
@@ -1492,9 +1492,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own RMSprop
-[[1.99852187]
- [3.03868311]
- [3.95744254]]
+[[2.00264795]
+ [3.00057362]
+ [3.99959681]]
 
    
 
    
 
    
@@ -1580,9 +1580,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own ADAM
-[[1.99996471]
- [3.00026784]
- [3.99973141]]
+[[1.99992477]
+ [3.00056337]
+ [3.99950283]]
 
    
 
    
 
    
@@ -1655,7 +1655,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
   return asarray(x, dtype=self.dtype)
 
    
 
    
-
    [<matplotlib.lines.Line2D at 0x11892e7f0>]
+
    [<matplotlib.lines.Line2D at 0x1522ef130>]
 
    
 
    
 _images/exercisesweek41_39_2.png
@@ -1690,7 +1690,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
 
    
 
    
 
    
-
    <matplotlib.collections.PathCollection at 0x118995f70>
+
    <matplotlib.collections.PathCollection at 0x11c3bd700>
 
    
 
    
 _images/exercisesweek41_41_1.png
diff --git a/doc/LectureNotes/_build/html/linalg.html b/doc/LectureNotes/_build/html/linalg.html
index 6593bcd76..20d3ef4af 100644
--- a/doc/LectureNotes/_build/html/linalg.html
+++ b/doc/LectureNotes/_build/html/linalg.html
@@ -653,8 +653,8 @@ matrices and vectors.
    
 
    
 
    
 
    
-
    [ 1.34851141  0.11232457 -0.59560497  0.79558423 -0.74632311  0.97068015
-  1.45921927  0.39225935 -0.87171492  0.44129766]
+
    [-0.03609183  1.12393064 -0.46049816  0.86567539  2.03530044  0.18872206
+  0.67747547 -1.387672    0.30136959 -0.51164347]
 
    
 
    
 
    
@@ -875,36 +875,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
 
    
 
    
 
    
-
    [[6.95039348e-01 4.77602426e-01 4.58218587e-02 7.93416236e-01
-  9.55643340e-01 7.98621772e-01 4.69806017e-01 1.11371176e-01
-  6.52359414e-01 4.86833907e-01]
- [6.34549184e-01 4.06225028e-01 6.55208611e-01 1.10909182e-02
-  6.45609936e-01 2.42839645e-01 2.14335140e-01 4.88477745e-01
-  7.33046180e-01 3.04253288e-01]
- [9.24911588e-01 6.09812323e-01 3.17886464e-01 8.85018556e-01
-  9.82435177e-01 9.43167881e-01 7.43951798e-01 7.31787864e-01
-  4.80369722e-01 8.57475388e-01]
- [1.16436977e-01 8.00829346e-01 3.64493447e-01 4.94082113e-02
-  5.68163369e-01 4.84993546e-03 2.34121963e-01 2.49849206e-01
-  3.86274590e-01 8.78949699e-01]
- [4.91568958e-01 9.21047414e-02 7.84745063e-01 6.96054705e-01
-  6.19052323e-02 8.28645331e-01 8.30191327e-01 2.48853875e-01
-  7.24382159e-01 3.97218946e-01]
- [3.79942527e-01 1.36403018e-02 7.08949703e-02 5.98556486e-01
-  5.10274274e-01 5.86030593e-01 8.96762953e-02 9.27993904e-01
-  5.79873884e-01 4.12085937e-01]
- [4.75733813e-01 3.83164611e-01 9.64968814e-01 2.10369090e-01
-  6.15338896e-04 5.29509520e-01 4.33492417e-01 9.06496916e-01
-  1.39646454e-01 4.21846767e-01]
- [1.24848631e-01 9.48875568e-01 8.00598510e-01 4.10923006e-01
-  9.72407267e-01 9.52121734e-01 1.56665044e-01 5.06276815e-01
-  5.30196557e-01 8.40012736e-01]
- [3.25145374e-01 1.83317854e-01 7.66763324e-02 9.66707072e-01
-  8.23006632e-01 4.30655251e-01 4.69811070e-02 8.72758060e-01
-  6.59088350e-01 7.28365323e-01]
- [5.96282505e-01 9.69432059e-01 1.10687432e-01 9.36439409e-01
-  1.82212861e-01 9.15905706e-01 4.35826030e-01 3.89219980e-01
-  4.21786054e-01 4.13799037e-02]]
+
    [[0.58452753 0.23142852 0.47865692 0.18572822 0.31436277 0.90536337
+  0.4096949  0.68748147 0.94493877 0.97696399]
+ [0.95000259 0.40310129 0.74533876 0.01597958 0.74471533 0.36031586
+  0.12031117 0.72690169 0.49008744 0.43492736]
+ [0.05224627 0.62033782 0.14697641 0.34601775 0.97622642 0.8644215
+  0.24629299 0.5128288  0.24777879 0.43007284]
+ [0.76134837 0.76159011 0.55582327 0.81598832 0.40209956 0.31639624
+  0.20393163 0.87700882 0.11331834 0.20621146]
+ [0.00922905 0.79619113 0.72444668 0.05605667 0.38949027 0.92292668
+  0.84659427 0.10325557 0.55170845 0.63144217]
+ [0.25206089 0.00866766 0.06506624 0.53397188 0.25345918 0.67527216
+  0.6696885  0.29142853 0.78198192 0.10425587]
+ [0.72882234 0.32052031 0.58799372 0.04235439 0.60740312 0.12152902
+  0.17158357 0.79041505 0.22523018 0.63353014]
+ [0.13518746 0.54977724 0.1793636  0.31983943 0.12702643 0.3265097
+  0.81592028 0.53488344 0.51698843 0.81385326]
+ [0.85786373 0.84416298 0.775204   0.84047912 0.21501369 0.66603461
+  0.53869015 0.88147037 0.06512065 0.38535182]
+ [0.60586595 0.55084297 0.9202867  0.39376038 0.50558976 0.09347829
+  0.50861923 0.79443618 0.17728993 0.29414225]]
 
    
 
    
 
    
@@ -964,13 +954,13 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.11406297261341168
-4.669908227073521
-0.2806901924685401
-[[ 0.98292239  2.97664837  2.73565966]
- [ 2.97664837 10.21905519  8.21690241]
- [ 2.73565966  8.21690241 13.03354844]]
-[20.77597019  0.09165112  3.36790471]
+
    0.0684990216069429
+4.3391965479837245
+0.32336220689648504
+[[0.81091776 2.49361065 1.78904605]
+ [2.49361065 8.68609856 5.49742477]
+ [1.78904605 5.49742477 6.25106936]]
+[13.8211787   0.07429213  1.85261484]
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 35b1a9f0d..a9e5c206c 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Project 2 on Machine Learning, deadline November 4 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with examples"],titleterms:{"0":39,"1":[0,15,16,17,18,22,25,31,32,38,39],"11":38,"14":39,"16":35,"2":[0,15,16,17,18,22,26,31,32,33,38,39],"2023":29,"2024":[25,36,37,38,39],"21":[],"23":36,"26":32,"27":36,"3":[0,15,16,22,31,32,38,39],"30":37,"34":[15,31],"35":[16,32],"36":[17,33],"37":[18,34],"38":[19,35],"39":[20,21,36],"4":[0,22,26,32,39],"40":[21,37],"41":[21,38],"42":[22,39],"5":[0,22],"6":22,"7":[25,38],"9":34,"case":[8,10,28,32,33,35,36],"class":[35,38],"do":[1,33,34,36,37,38,39],"final":[12,21,26,32,33,36,37,38,39],"float":38,"function":[0,1,6,7,8,10,11,12,13,21,25,26,28,31,32,33,34,35,36,37,38,39],"import":[5,21,24,31,32,33,37,38,39],"new":[4,33,34,38],A:[0,1,4,8,9,21,31,33,34,35,37,38,39],AND:[37,38],And:[21,31,32,33,35,36,37],But:[21,36,37],For:32,In:[29,38],Is:[38,39],Ising:6,OR:[37,38],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,31,32,33,34,35,36,37,38,39],To:[31,32],With:[4,33],about:[31,32],abov:[33,38,39],activ:[1,12,22,26,33,37,38,39],ad:[0,6,17,25,31,32,37,38,39],adaboost:10,adagrad:[13,21,36,37],adam:[13,21,36,37],adapt:[10,21,36,37],adjust:[1,39],advanc:[21,37],adversari:4,again:[3,9,35],ai:[25,31],aim:[8,9,17,18,19,20,21,22,31],aka:[31,32],al:[21,37],algebra:[24,31],algorithm:[9,10,11,12,21,26,32,36,37,38,39],algortithm:[13,35,36],all:[8,38,39],an:[0,4,10,31,38],analys:[5,32],analysi:[0,5,6,11,23,25,26,28,31,32,33,34,38],analyt:[0,16,17,21],analyz:[38,39],ani:[13,35,36],anoth:[9,33,34],appli:23,approach:[0,8,14,31,34,36,37],approxim:[12,38],architectur:[1,39],argument:[36,37],arrai:[24,31],artifici:[37,38],assist:29,assumpt:[33,34],august:32,autocorrel:28,autograd:[2,13,21,36,37],automat:[13,21,36,37,38],avoid:[],b:[17,25,26,36],back:[1,11,12,38,39],background:[23,25,26,34],bag:10,base:[13,21,34,36,37],basic:[0,5,7,9,10,11,24,32,33,34,35,38],batch:[1,36,37,38,39],bay:[5,33,34],befor:11,bengio:[38,39],beta:[33,34],better:[8,37,38],bia:[6,25,34],bias:[38,39],binari:[1,39],bind:31,bird:10,boldsymbol:[32,33,34],book:[38,39],boost:10,bootstrap:[6,10,34],boston:0,breast:[1,39],brief:[31,34,35,36],bring:[12,38,39],build:[1,3,9,39],c:[25,26,31],calcul:32,can:[21,31,34,36,37,38],cancer:[1,7,9,11,35,39],cart:9,center:32,central:[13,23,28,34,35,36],chain:[12,38,39],challeng:35,chang:10,channel:31,chi:[0,31],choic:[38,39],choos:[1,39],cifar01:3,classic:11,classif:[1,9,10,26,35,39],classifi:[8,35],clip:[1,38,39],cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,21,26,31,32,33,34,35,36,37,38,39],collect:[1,3,39],come:35,commun:31,compact:[35,38,39],compar:[2,10],comparison:33,compet:[21,36,37],complet:[32,38,39],complex:[0,6,25,32],complic:[6,36,37,38],compon:11,comput:[9,36,37],computation:34,computerlab:31,con:9,concept:28,condit:[33,34,35,36],confid:34,conjug:[13,36],consider:[38,39],construct:[38,39],continu:37,contn:31,convex:[8,13,35,36],convolut:[3,12,37,38],correctli:[33,34],correl:[11,32,35],correspond:[35,36],cost:[1,10,32,33,34,35,36,38,39],count:38,cours:[23,30,31],covari:[5,11,28,32],cover:31,critic:26,cross:[6,25,34,35],custom:22,cython:31,d:[25,26],data:[0,1,3,6,7,9,11,15,16,22,23,25,28,31,32,33,35,38,39],dataset:[1,3,39],deadlin:[25,26,31],decai:[2,36,37],decis:[9,10],decomposit:[5,11,17,24,32],deep:[1,2,31,35,38,39],defin:[1,31,38,39],definit:[38,39],degre:[0,32],deliveri:[25,26],delta:34,dens:[0,31],deriv:[5,12,32,33,34,35,36,38,39],descent:[2,10,13,21,26,35,36,37],descript:25,design:32,detail:[3,31],develop:[1,39],diagon:11,differ:[8,26,36,37],different:21,differenti:[2,13,36,37,38],diffus:2,dimension:[2,3,8,25,32],directli:[36,37],disadvantag:9,discret:28,discuss:35,distribut:[5,28,33,34],distrubut:34,doe:[32,33,37,38],domain:28,dot:36,down:[1,38,39],dropout:[1,38,39],e:[25,26],each:[22,35],economi:32,electron:[25,26],element:[0,28,31,36,37],elimin:24,elu:[38,39],energi:31,ensembl:10,entri:[38,39],entropi:[9,35],environ:[0,15,31],equat:[0,2,12,31,32,33,35,36,38,39],error:[0,10,31,32,34],essenti:31,estim:[33,34],et:[21,37],etc:31,euler:2,evalu:[1,26,38,39],exampl:[0,1,2,3,4,6,7,8,9,10,21,31,32,33,34,35,36,37,38,39],exercis:[0,6,15,16,17,18,19,20,21,22,31,32,38],expect:[18,28,33,34],expens:34,experi:28,explicit:[38,39],explod:[38,39],explor:[0,15,16,31],exponenti:2,express:[17,18,32,35,36,38,39],extend:[35,36,38],extrapol:4,extrem:[10,31],ey:10,f:[25,26],fall:29,famili:[1,31,32,38,39],famou:24,fantast:32,featur:[9,24,32],feed:[1,12,37,38,39],find:[34,36],fine:[1,38,39],first:[4,12,26,31,32,33,35,36,38,39],fit:[0,10,31,33],fix:32,fold:[34,35],forc:3,forest:10,format:[25,26,31],forward:[1,2,12,37,38,39],fourier:3,frank:[6,25,32],freedom:[0,32],frequent:32,frequentist:[0,31],fridai:[],from:[5,10,12,21,26,32,33,34,35,36,37,38,39],full:[2,39],funtion:[38,39],further:[3,5,32],g:25,gan:4,gate:[37,38,39],gaussian:24,gd:[13,21,36,37],gener:[4,9,31,38],geometr:[11,35,36],get:[21,37,38],gini:9,glorot:[38,39],good:[0,31],goodfellow:[21,37],grade:[29,31],gradient:[1,2,10,13,21,26,35,36,37,38,39],grid:35,group:35,growth:2,ha:23,hand:[38,39],handl:[24,31,32],happen:[33,34],hard:[],hessian:[32,35,36],hidden:[2,38,39],h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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Exercises week 42","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Project 2 on Machine Learning, deadline November 4 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and  Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network code","Week 42 Constructing a Neural Network code with 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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/statistics.html b/doc/LectureNotes/_build/html/statistics.html
index 8d84333e9..6d5e7a8bd 100644
--- a/doc/LectureNotes/_build/html/statistics.html
+++ b/doc/LectureNotes/_build/html/statistics.html
@@ -1025,27 +1025,27 @@ uncorrelated.
    
 
    
 
    
 
    
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@@ -1313,15 +1313,15 @@ more practically oriented methods like the blocking technique.
    
 
    
 
    
 
    
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-0.06926151969326948
-1.1885906477601598 12.229598952352728 16.227738984674172
-3.6795562921258607 3.451702023099923 10.293665700387852
-[[ 1.18859065  3.67955629  3.45170202]
- [ 3.67955629 12.22959895 10.2936657 ]
- [ 3.45170202 10.2936657  16.22773898]]
-[25.73550536  0.06057711  3.84984612]
+
    -0.19350397125914334
+3.2837242622462304
+-0.8221979401496963
+1.227072949395066 12.297476846305301 41.228467858509994
+3.7100707615589594 5.460688958585373 17.510697484548
+[[ 1.22707295  3.71007076  5.46068896]
+ [ 3.71007076 12.29747685 17.51069748]
+ [ 5.46068896 17.51069748 41.22846786]]
+[50.34205649  0.0976598   4.31330137]
 
    
 
    
 
    
@@ -1651,7 +1651,7 @@ assumption for approximating \(\sigma
 
    
 
    
 
    
-
    0.03974553487733608 1.0433282860079154
+
    -0.006352536528464608 1.0383613556521865
 
    
 
    
 _images/statistics_188_1.png
diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html
index 820c34147..ad2adc8aa 100644
--- a/doc/LectureNotes/_build/html/week34.html
+++ b/doc/LectureNotes/_build/html/week34.html
@@ -1713,8 +1713,8 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 
    
 
    
 
    
-
    [ 1.81737781  0.45847011  0.53332849  1.04937026  0.53235952  2.24033384
- -0.05605892 -0.02193078 -0.37343957  0.17849836]
+
    [ 2.69749114 -0.06437266  0.55440601 -0.59770229  1.83671148  0.02661264
+ -1.45840653  1.96917669  0.32786329 -1.51690815]
 
    
 
    
 
    
@@ -1939,26 +1939,26 @@ lowercase letters for vectors and uppercase letters for matrices)
    
 
    
 
    
 
    
-
    [[0.15586219 0.68891428 0.38675231 0.63546905 0.97286326 0.423102
-  0.42841423 0.3478095  0.66130478 0.63443371]
- [0.88685415 0.82561703 0.56766321 0.43258818 0.99057275 0.40058341
-  0.84581375 0.64630711 0.03266152 0.57599494]
- [0.10145028 0.9074157  0.39386718 0.12831838 0.46242424 0.09280524
-  0.88415819 0.26443391 0.55095316 0.85897711]
- [0.69243548 0.33745439 0.14452065 0.29030256 0.8063779  0.60720029
-  0.42914251 0.44895662 0.09310479 0.48442601]
- [0.95152814 0.83294718 0.11005335 0.8758851  0.19375828 0.73888203
-  0.83203197 0.69203997 0.65147818 0.35195241]
- [0.21506004 0.24874378 0.31370028 0.9525328  0.71672791 0.05879106
-  0.45007578 0.36388542 0.50937003 0.57854115]
- [0.80033616 0.45273617 0.18038547 0.49557088 0.36209091 0.44512218
-  0.84078641 0.28924386 0.99166852 0.22896144]
- [0.77579275 0.83519517 0.40640797 0.66272614 0.18234499 0.97628064
-  0.19808709 0.1280526  0.33700495 0.32114535]
- [0.95462534 0.72047148 0.24512233 0.18474924 0.69169665 0.68763036
-  0.8861811  0.54193001 0.87830277 0.79251831]
- [0.13092444 0.41452482 0.40447213 0.89714357 0.25360039 0.80373997
-  0.51279028 0.58161787 0.08742496 0.45104086]]
+
    [[0.72863433 0.17780089 0.4565147  0.92353194 0.4565685  0.45408759
+  0.55900177 0.02561386 0.63461575 0.70015185]
+ [0.52488859 0.52729975 0.49035757 0.2383703  0.9206617  0.2766892
+  0.89708415 0.52998985 0.45993741 0.74506271]
+ [0.67036507 0.42813351 0.79628257 0.00872006 0.06083072 0.80873785
+  0.74407544 0.32616922 0.81337164 0.97183244]
+ [0.23449217 0.64638876 0.5291335  0.03625417 0.47382705 0.03068149
+  0.72365764 0.53228519 0.63483713 0.01924105]
+ [0.99849105 0.05293216 0.52012715 0.77037707 0.28836035 0.7080469
+  0.91692081 0.50152186 0.08734023 0.69892546]
+ [0.96154578 0.52229337 0.78888045 0.59879372 0.38240257 0.55451651
+  0.01380833 0.4659454  0.51857188 0.98893579]
+ [0.38422061 0.39516594 0.22569872 0.40434293 0.80846898 0.33300231
+  0.45647803 0.60116948 0.55862544 0.20607165]
+ [0.50029371 0.11487977 0.17093974 0.40903389 0.40362686 0.63204599
+  0.99864751 0.7033198  0.61402242 0.72219757]
+ [0.78025829 0.65744898 0.93708805 0.68532803 0.22974904 0.00166889
+  0.33180288 0.26196737 0.62846663 0.4390092 ]
+ [0.23570126 0.11877604 0.81642349 0.76688358 0.82757017 0.92808577
+  0.89790867 0.97165491 0.98806419 0.48333419]]
 
    
 
    
 
    
@@ -2013,13 +2013,13 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    -0.0458524213754298
-3.614161296466206
--0.22985907723809532
-[[0.72589774 2.09219464 1.64672839]
- [2.09219464 6.9187554  4.62198131]
- [1.64672839 4.62198131 6.70530438]]
-[12.05431945  0.07101262  2.22462544]
+
    0.15294186382924843
+4.473730000968826
+0.7333854378748542
+[[ 0.89730533  2.66990322  2.88518   ]
+ [ 2.66990322  8.78338459  8.44007188]
+ [ 2.88518     8.44007188 16.31799354]]
+[22.48904404  0.06826275  3.44137667]
 
    
 
    
 
    
@@ -2244,7 +2244,7 @@ Name: Aragorn, dtype: object
 
    
 
    ---------------------------------------------------------------------------
 AttributeError                            Traceback (most recent call last)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57294/1326197715.py in ?()
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59734/1326197715.py in ?()
 ----> 6 new_hobbit = {'First Name': ["Peregrin"],
       7               'Last Name': ["Took"],
       8               'Place of birth': ["Shire"],
diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index 0aa1ad28d..6d87e99c4 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -1681,7 +1681,7 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    0.9953466931203151
+
    0.9950865473984227
 
    
 
    
 
    
@@ -1698,7 +1698,7 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    0.010175219431920396
+
    0.009998596494598343
 
    
 
    
 
    
@@ -1713,23 +1713,23 @@ Since we are not using Scikit-Learn here we can define our own
 
    
 
    
 
    
-
    [0.03699304 0.0218856  0.02021288 0.0334505  0.02960445 0.01369614
- 0.00606112 0.01429853 0.01174937 0.02585059 0.02044223 0.00174884
- 0.06133572 0.03159246 0.04435458 0.00125802 0.00100423 0.00230308
- 0.01055384 0.02352843 0.06971862 0.01300036 0.00547332 0.05554795
- 0.01142354 0.01181458 0.00148216 0.01419341 0.0305221  0.01272102
- 0.01700746 0.01552039 0.01616657 0.10695771 0.00405576 0.02979087
- 0.0529838  0.01420773 0.06220192 0.04104182 0.00653725 0.07170448
- 0.00997215 0.02490769 0.02580654 0.01682317 0.03221473 0.01838531
- 0.02028936 0.0064349  0.04323964 0.02705202 0.03692828 0.01975755
- 0.05636265 0.02880987 0.05692207 0.04085864 0.01085261 0.01457889
- 0.03418643 0.01919791 0.00101555 0.00636951 0.04217103 0.0476266
- 0.01169528 0.04544327 0.00978267 0.04046984 0.0032882  0.02072876
- 0.05526935 0.05461692 0.00877816 0.00724303 0.00045923 0.00059105
- 0.00917881 0.04254787 0.08728977 0.04513394 0.01606644 0.08956994
- 0.02687671 0.07449122 0.04497158 0.01713187 0.02553907 0.0397137
- 0.03313193 0.00738299 0.01743124 0.02953975 0.01131825 0.0864086
- 0.01925934 0.02439287 0.09331672 0.01721277]
+
    [0.0442242  0.01222848 0.01176967 0.00562142 0.00392853 0.02034593
+ 0.01294799 0.07286621 0.00893112 0.00155695 0.03181997 0.00965205
+ 0.00523789 0.03510464 0.04098879 0.04626298 0.03733016 0.05632246
+ 0.02241952 0.03770824 0.05984726 0.00875286 0.04355106 0.01981665
+ 0.06929519 0.03426934 0.00410974 0.0142117  0.00099936 0.04030508
+ 0.05247827 0.05227563 0.02499437 0.01459912 0.00154737 0.03099794
+ 0.06159762 0.00052051 0.04268493 0.01479672 0.01149099 0.02280634
+ 0.04740084 0.00617261 0.00103024 0.00740838 0.00577229 0.00142146
+ 0.00140253 0.01112157 0.01180692 0.00039821 0.02257687 0.03196582
+ 0.01289266 0.03194307 0.00192165 0.08543112 0.01377529 0.06267966
+ 0.10637914 0.00872869 0.00331168 0.03291795 0.08933713 0.00786896
+ 0.01299711 0.01870931 0.03870024 0.0089204  0.0180908  0.05893076
+ 0.00110867 0.0374535  0.03500569 0.00381349 0.01974315 0.01109955
+ 0.0193386  0.01273648 0.00520618 0.00420536 0.02597861 0.01698477
+ 0.02535973 0.03901061 0.06862038 0.01811373 0.02478142 0.00293201
+ 0.06915429 0.01105668 0.01370129 0.03923714 0.02045223 0.00193326
+ 0.01717238 0.01441661 0.04590873 0.00871267]
 
    
 
    
 
    
@@ -1798,15 +1798,15 @@ but now splitting the data into a training set and a test set.
    
 
    
 
    
 
    
-
    [ 1.94735263  0.70175778  2.99348646  1.86611894 -0.45576546]
+
    [  2.08019747  -1.5544062   12.12040799 -11.41377873   5.90871271]
 Training R2
-0.9959836634296064
+0.9958244721767004
 Training MSE
-0.0085274606925055
+0.010405448150048726
 Test R2
-0.992232777849821
+0.9929666457835595
 Test MSE
-0.010638334964957053
+0.018365206985554446
 
    
 
    
 
    
@@ -2477,7 +2477,9 @@ Feature min values before scaling:
  2.89126914e-10 1.00934327e-10 3.52362157e-11 1.65571174e-11
  5.78009660e-12 2.01783414e-12 7.04426744e-13 2.45915671e-13
  8.58492636e-14]
-Feature max values before scaling:
+
    
+
    
+
    Feature max values before scaling:
  [1.         0.99970894 0.99978365 0.99941797 0.99949266 0.99956735
  0.99912709 0.99920175 0.99927642 0.9993511  0.99883628 0.99891093
  0.99898558 0.99906023 0.99913489 0.99854557 0.99862019 0.99869482
diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html
index fd3f4425a..c530d08e1 100644
--- a/doc/LectureNotes/_build/html/week37.html
+++ b/doc/LectureNotes/_build/html/week37.html
@@ -1669,7 +1669,7 @@ theorem.
    
 
    
 
    Bootstrap Statistics :
 original           bias      std. error
- 100.091  15.1529        100.089         0.15052
+ 100.203  14.9405        100.203        0.148913
 
    
 
    
 
    
@@ -1894,7 +1894,9 @@ Error: 0.10398646080125035
 Bias^2: 0.1007711427354898
 Var: 0.0032153180657605116
 0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032
-Polynomial degree: 3
+
    
+
    
+
    Polynomial degree: 3
 Error: 0.06547790180152355
 Bias^2: 0.06208238634231949
 Var: 0.0033955154592040936
@@ -1904,14 +1906,14 @@ Error: 0.06844519414009445
 Bias^2: 0.06453579006728324
 Var: 0.003909404072811226
 0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446
-
    
-
    
-
    Polynomial degree: 5
+Polynomial degree: 5
 Error: 0.05227921801205686
 Bias^2: 0.0481872773043029
 Var: 0.004091940707753939
 0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844
-Polynomial degree: 6
+
    
+
    
+
    Polynomial degree: 6
 Error: 0.037813671417389005
 Bias^2: 0.033657685071527665
 Var: 0.00415598634586135
@@ -1938,9 +1940,7 @@ Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
 0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022
-
    
-
    
-
    Polynomial degree: 11
+Polynomial degree: 11
 Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
@@ -1950,14 +1950,16 @@ Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
 0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497
-Polynomial degree: 13
+
    
+
    
+
    Polynomial degree: 13
 Error: 0.22842468702219465
 Bias^2: 0.01975416527185249
 Var: 0.20867052175034223
 0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
 
    
 
    
-_images/week37_139_4.png
+_images/week37_139_5.png
 
    
 
    
 
    
@@ -2388,9 +2390,9 @@ Mean squared error on training data: 0.00063866
 Mean squared error on test data: 3099.60342978
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57311/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59752/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57311/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59752/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -2475,7 +2477,7 @@ Mean squared error on test data: 3099.60342978
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57311/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59752/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week39.html b/doc/LectureNotes/_build/html/week39.html
index 326218d67..f88ef4844 100644
--- a/doc/LectureNotes/_build/html/week39.html
+++ b/doc/LectureNotes/_build/html/week39.html
@@ -1812,7 +1812,7 @@ which equals
    -
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11ffa0ee0>
+
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x127d8ceb0>
 
    
 
    
 _images/week39_82_1.png
@@ -1870,7 +1870,7 @@ which equals
    -
    [<matplotlib.lines.Line2D at 0x12ccea880>]
+
    [<matplotlib.lines.Line2D at 0x1322a9a00>]
 
    
 
    
 _images/week39_90_1.png
@@ -2164,11 +2164,11 @@ when \(||\nabla_\beta C(\beta_k) || \
    -
    Eigenvalues of Hessian Matrix:[0.33433563 4.2051784 ]
-[[3.94121002]
- [3.08191754]]
-[[3.94121002]
- [3.08191754]]
+
    Eigenvalues of Hessian Matrix:[0.28754232 4.16543524]
+[[3.78714005]
+ [3.07335575]]
+[[3.78714005]
+ [3.07335575]]
 
    
 
    
 _images/week39_153_1.png
@@ -2199,9 +2199,9 @@ when \(||\nabla_\beta C(\beta_k) || \
    -
    [[3.91483251]
- [3.09848937]]
-[3.87616945] [3.07001431]
+
    [[4.02163476]
+ [2.9518022 ]]
+[4.06200442] [3.03827385]
    @@ -2301,11 +2301,11 @@ minimum of this function.

    -
    Eigenvalues of Hessian Matrix:[0.26638638 4.40906565]
-[[4.26969649]
- [2.78617455]]
-[[4.26953857]
- [2.78630865]]
+
    Eigenvalues of Hessian Matrix:[0.36579128 3.8614682 ]
+[[3.94096731]
+ [3.09895512]]
+[[3.94091671]
+ [3.09900258]]
 
    
 
    
 _images/week39_166_1.png
@@ -3878,16 +3878,12 @@ beta from own Newton code
 [[4.0586484]
  [3.0718316]]
 Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]
-
    -
    -
    theta from own gd
-
    -
    -
    [[4.0586484]
+theta from own gd
+[[4.0586484]
  [3.0718316]]
    -_images/week39_269_3.png +_images/week39_269_1.png 
    theta from own sdg
 [[4.02496085]
  [3.12081773]]
diff --git a/doc/LectureNotes/_build/html/week40.html b/doc/LectureNotes/_build/html/week40.html
index 121bbc483..f0c6a72e5 100644
--- a/doc/LectureNotes/_build/html/week40.html
+++ b/doc/LectureNotes/_build/html/week40.html
@@ -1314,17 +1314,17 @@ We summarize some of these here for the methods we hvae studied in project one,
 
    Parameters for OLS using gradient descent
-[[3.8184887 ]
- [3.47851966]
- [4.77551387]]
+[[3.78243922]
+ [3.4825169 ]
+ [4.79102612]]
 Parameters for Ridge using gradient descent
-[[3.92021197]
- [3.11388017]
- [4.9458396 ]]
+[[3.94463114]
+ [3.02606605]
+ [4.9912346 ]]
 Parameters for Lasso using gradient descent
-[[3.87323528]
- [3.3008836 ]
- [4.86284277]]
+[[3.79673108]
+ [3.54406885]
+ [4.74100159]]
    @@ -1376,11 +1376,11 @@ Parameters for Lasso using gradient descent [[4.] [3.] [5.]] -0 [-31.91417133] [-44.48017159] -1 [3.48805429e-13] [4.67477123e-13] -2 [6.75015599e-16] [1.30675215e-15] -3 [-1.17239551e-15] [-1.78477759e-15] -4 [6.75015599e-16] [1.30675215e-15] +0 [-31.16622624] [-43.76852722] +1 [1.2420287e-13] [1.07341744e-13] +2 [-6.21724894e-16] [-7.8406741e-16] +3 [1.0658141e-15] [1.70040367e-15] +4 [4.4408921e-16] [8.75488337e-16] beta from own Newton code [[4.] [3.] @@ -1722,18 +1722,20 @@ function.

    Own inversion
-[[3.90300704]
- [3.16913489]]
-Eigenvalues of Hessian Matrix:[0.2964378  4.12443871]
-theta from own gd
-[[3.90300704]
- [3.16913489]]
-theta from own sdg
-[[3.93272428]
- [3.16328315]]
    -_images/week40_34_1.png +
    [[3.79540402]
+ [3.14383145]]
+Eigenvalues of Hessian Matrix:[0.29866395 3.79522963]
+theta from own gd
+[[3.79540402]
+ [3.14383145]]
+theta from own sdg
+[[3.78596158]
+ [3.12461758]]
+
    +
    +_images/week40_34_2.png
    @@ -2444,12 +2446,12 @@ first example shows results with ordinary leats squares.

    Own inversion
-[[4.13791264]
- [2.92552059]]
-Eigenvalues of Hessian Matrix:[0.27874136 4.16226023]
+[[4.35914932]
+ [2.77699722]]
+Eigenvalues of Hessian Matrix:[0.32641558 4.49529361]
 theta from own gd
-[[4.13791264]
- [2.92552059]]
+[[4.35914932]
+ [2.77699722]]
    _images/week40_100_1.png @@ -2520,73 +2522,73 @@ theta from own gd 
    Own inversion
 [[4.]
  [3.]]
-Eigenvalues of Hessian Matrix:[0.32606365 3.80499859]
-0 [-10.98955596] [-10.8332972]
-1 [-0.26421616] [0.25444295]
-2 [-0.24157456] [0.23263884]
-3 [-0.22087319] [0.2127032]
-4 [-0.20194579] [0.19447592]
-5 [-0.18464035] [0.1778106]
-6 [-0.16881787] [0.16257339]
-7 [-0.15435127] [0.1486419]
-8 [-0.14112437] [0.13590426]
-9 [-0.12903093] [0.12425815]
-10 [-0.11797382] [0.11361003]
-11 [-0.10786423] [0.10387439]
-12 [-0.09862097] [0.09497303]
-13 [-0.09016979] [0.08683446]
-14 [-0.08244283] [0.07939331]
-15 [-0.07537801] [0.07258982]
-16 [-0.06891861] [0.06636934]
-17 [-0.06301273] [0.06068192]
-18 [-0.05761295] [0.05548187]
-19 [-0.05267589] [0.05072744]
-20 [-0.04816191] [0.04638043]
-21 [-0.04403475] [0.04240593]
-22 [-0.04026126] [0.03877201]
-23 [-0.03681113] [0.0354495]
-24 [-0.03365665] [0.03241171]
-25 [-0.0307725] [0.02963424]
-26 [-0.02813549] [0.02709478]
-27 [-0.02572446] [0.02477293]
-28 [-0.02352005] [0.02265005]
-29 [-0.02150453] [0.02070909]
+Eigenvalues of Hessian Matrix:[0.31711533 4.28519494]
+0 [-8.99246366] [-10.02181163]
+1 [-0.20915094] [0.17948385]
+2 [-0.19367324] [0.16620159]
+3 [-0.17934093] [0.15390225]
+4 [-0.16606924] [0.14251309]
+5 [-0.1537797] [0.13196676]
+6 [-0.14239961] [0.12220088]
+7 [-0.13186167] [0.11315771]
+8 [-0.12210357] [0.10478375]
+9 [-0.1130676] [0.09702948]
+10 [-0.10470031] [0.08984905]
+11 [-0.09695222] [0.0832]
+12 [-0.08977751] [0.07704298]
+13 [-0.08313374] [0.07134161]
+14 [-0.07698164] [0.06606215]
+15 [-0.0712848] [0.06117338]
+16 [-0.06600954] [0.05664639]
+17 [-0.06112467] [0.05245442]
+18 [-0.05660129] [0.04857266]
+19 [-0.05241265] [0.04497816]
+20 [-0.04853398] [0.04164966]
+21 [-0.04494234] [0.03856748]
+22 [-0.04161649] [0.03571339]
+23 [-0.03853677] [0.0330705]
+24 [-0.03568495] [0.0306232]
+25 [-0.03304417] [0.02835701]
+26 [-0.03059882] [0.02625852]
+27 [-0.02833443] [0.02431532]
+28 [-0.02623761] [0.02251592]
+29 [-0.02429596] [0.02084969]
 theta from own gd
-[[3.93969971]
- [3.05806981]]
-0 [-0.01966173] [0.01893445]
-1 [-0.01797685] [0.0173119]
-2 [-0.01593089] [0.01534161]
-3 [-0.01395192] [0.01343585]
-4 [-0.01216264] [0.01171276]
-5 [-0.0105836] [0.01019212]
-6 [-0.00920294] [0.00886253]
-7 [-0.00800011] [0.00770419]
-8 [-0.00695371] [0.00669649]
-9 [-0.0060439] [0.00582034]
-10 [-0.00525303] [0.00505872]
-11 [-0.00456562] [0.00439674]
-12 [-0.00396815] [0.00382137]
-13 [-0.00344887] [0.0033213]
-14 [-0.00299754] [0.00288666]
-15 [-0.00260527] [0.0025089]
-16 [-0.00226433] [0.00218058]
-17 [-0.00196801] [0.00189522]
-18 [-0.00171047] [0.0016472]
-19 [-0.00148663] [0.00143164]
-20 [-0.00129209] [0.00124429]
-21 [-0.001123] [0.00108146]
-22 [-0.00097604] [0.00093994]
-23 [-0.00084831] [0.00081693]
-24 [-0.0007373] [0.00071003]
-25 [-0.00064081] [0.00061711]
-26 [-0.00055695] [0.00053635]
-27 [-0.00048407] [0.00046616]
-28 [-0.00042072] [0.00040516]
-29 [-0.00036566] [0.00035214]
+[[3.92905422]
+ [3.06088245]]
+0 [-0.022498] [0.01930676]
+1 [-0.02083309] [0.01787801]
+2 [-0.01879191] [0.01612637]
+3 [-0.01678891] [0.01440748]
+4 [-0.01494559] [0.01282563]
+5 [-0.01328658] [0.01140194]
+6 [-0.01180564] [0.01013106]
+7 [-0.01048771] [0.00900007]
+8 [-0.00931621] [0.00799475]
+9 [-0.00827534] [0.00710152]
+10 [-0.00735068] [0.00630802]
+11 [-0.00652931] [0.00560316]
+12 [-0.00579972] [0.00497706]
+13 [-0.00515165] [0.00442091]
+14 [-0.00457599] [0.00392691]
+15 [-0.00406466] [0.0034881]
+16 [-0.00361046] [0.00309834]
+17 [-0.00320702] [0.00275212]
+18 [-0.00284866] [0.00244459]
+19 [-0.00253034] [0.00217143]
+20 [-0.0022476] [0.00192879]
+21 [-0.00199645] [0.00171326]
+22 [-0.00177336] [0.00152182]
+23 [-0.0015752] [0.00135176]
+24 [-0.00139918] [0.00120071]
+25 [-0.00124283] [0.00106654]
+26 [-0.00110396] [0.00094737]
+27 [-0.0009806] [0.0008415]
+28 [-0.00087102] [0.00074747]
+29 [-0.00077369] [0.00066395]
 theta from own gd wth momentum
-[[3.99902531]
- [3.00093864]]
+[[3.99783284]
+ [3.00185976]]
    @@ -2675,18 +2677,18 @@ theta from own gd wth momentum 
    Own inversion
-[[4.11762444]
- [3.04098313]]
-Eigenvalues of Hessian Matrix:[0.29738252 4.51279273]
+[[4.04829439]
+ [3.02060364]]
+Eigenvalues of Hessian Matrix:[0.24427624 4.58825417]
 theta from own gd
-[[4.11762444]
- [3.04098313]]
+[[4.04829439]
+ [3.02060364]]
    _images/week40_104_1.png 
    theta from own sdg
-[[4.07058967]
- [3.024004  ]]
+[[4.04658881]
+ [3.02962903]]
    @@ -2768,17 +2770,17 @@ theta from own gd 
    Own inversion
-[[4.4252885 ]
- [2.70944365]]
-Eigenvalues of Hessian Matrix:[0.28973035 4.32089655]
+[[4.08777858]
+ [2.93519647]]
+Eigenvalues of Hessian Matrix:[0.30054056 4.29201128]
 theta from own gd
-[[4.42394588]
- [2.71059619]]
+[[4.08779325]
+ [2.93518383]]
 
    theta from own sdg with momentum
-[[4.44845593]
- [2.72577807]]
+[[4.03961179]
+ [2.924011  ]]
    @@ -2847,9 +2849,9 @@ theta from own gd 
    theta from own AdaGrad
-[[2.00036797]
- [2.99817613]
- [4.00177485]]
+[[1.99968483]
+ [3.00127606]
+ [3.9985605 ]]
    @@ -2925,9 +2927,9 @@ theta from own gd 
    theta from own RMSprop
-[[2.00119865]
- [3.01346635]
- [3.99284588]]
+[[2.00035708]
+ [2.9930344 ]
+ [3.98966074]]
    @@ -3007,9 +3009,9 @@ theta from own gd 
    theta from own ADAM
-[[1.99997244]
- [3.00018876]
- [3.99983332]]
+[[2.00005688]
+ [2.99969642]
+ [4.00028552]]
    @@ -3130,7 +3132,7 @@ It provides composable transformations of Python+NumPy programs: differentiate, return asarray(x, dtype=self.dtype) -
    [<matplotlib.lines.Line2D at 0x10cc52cd0>]
+
    [<matplotlib.lines.Line2D at 0x124252b80>]
 
    
 
    
 _images/week40_120_2.png
@@ -3165,7 +3167,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
    -
    <matplotlib.collections.PathCollection at 0x10fcfaeb0>
+
    <matplotlib.collections.PathCollection at 0x125dbb640>
 
    
 
    
 _images/week40_122_1.png
diff --git a/doc/LectureNotes/_build/html/week42.html b/doc/LectureNotes/_build/html/week42.html
index 3a6eaabc7..b50757efe 100644
--- a/doc/LectureNotes/_build/html/week42.html
+++ b/doc/LectureNotes/_build/html/week42.html
@@ -1566,10 +1566,8 @@ doconce format html week42.do.txt --no_mako -->
 
    Readings and videos.
    
 
      
 
    1. These lecture notes
      2. 
-
    
-
-
-
      
+
    1. Video of lecture
      2. 
+
    2. Whiteboard notes
      3. 
 
    3. For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7.
      4. 
 
    4. Neural Networks demystified at https://www.youtube.com/watch?v=bxe2T-V8XRs&amp;list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&amp;ab_channel=WelchLabs
      5. 
 
    5. Building Neural Networks from scratch at https://www.youtube.com/watch?v=Wo5dMEP_BbI&amp;list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&amp;ab_channel=sentdex
      6. 
@@ -3378,7 +3376,7 @@ the Hadamard product, meaning element-wise multiplication.
      
 
      Old accuracy on training data: 0.1440501043841336
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3714,7 +3712,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3723,7 +3721,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3732,7 +3730,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3741,7 +3739,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3750,7 +3748,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3759,7 +3757,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3768,7 +3766,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3777,11 +3775,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3790,11 +3788,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3803,11 +3801,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3816,11 +3814,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3829,11 +3827,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3842,7 +3840,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3851,11 +3849,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3864,11 +3862,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3877,11 +3875,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3890,11 +3888,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3903,11 +3901,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3916,11 +3914,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3929,11 +3927,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3942,11 +3940,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3999,15 +3997,15 @@ Accuracy score on test set:  0.07777777777777778
 
      
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -4289,30 +4287,32 @@ Accuracy score on test set:  0.8583333333333333
 
      Learning rate  =  0.1
 Lambda =  0.001
 Accuracy score on test set:  0.8722222222222222
-
-Learning rate  =  0.1
+
      
+
      
+
      Learning rate  =  0.1
 Lambda =  0.01
 Accuracy score on test set:  0.9055555555555556
-
      
-
      
-
      Learning rate  =  0.1
-Lambda =  0.1
-Accuracy score on test set:  0.8805555555555555
 
 Learning rate  =  0.1
-Lambda =  1.0
-Accuracy score on test set:  0.8722222222222222
+Lambda =  0.1
+Accuracy score on test set:  0.8805555555555555
 
      
 
      
 
      Learning rate  =  0.1
+Lambda =  1.0
+Accuracy score on test set:  0.8722222222222222
+
+Learning rate  =  0.1
 Lambda =  10.0
 Accuracy score on test set:  0.8666666666666667
-
-Learning rate  =  1.0
+
      
+
      
+
      Learning rate  =  1.0
 Lambda =  1e-05
 Accuracy score on test set:  0.08611111111111111
-
-Learning rate  =  1.0
+
      
+
      
+
      Learning rate  =  1.0
 Lambda =  0.0001
 Accuracy score on test set:  0.10555555555555556
 
      
@@ -4337,8 +4337,9 @@ Accuracy score on test set:  0.08888888888888889
 Learning rate  =  1.0
 Lambda =  10.0
 Accuracy score on test set:  0.09444444444444444
-
-Learning rate  =  10.0
+
      
+
      
+
      Learning rate  =  10.0
 Lambda =  1e-05
 Accuracy score on test set:  0.17222222222222222
 
      
diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
index fb7beb22c..153d76c1f 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb
@@ -1176,7 +1176,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 1,
@@ -1346,7 +1346,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 5,
@@ -2138,16 +2138,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.33433563 4.2051784 ]\n",
-      "[[3.94121002]\n",
-      " [3.08191754]]\n",
-      "[[3.94121002]\n",
-      " [3.08191754]]\n"
+      "Eigenvalues of Hessian Matrix:[0.28754232 4.16543524]\n",
+      "[[3.78714005]\n",
+      " [3.07335575]]\n",
+      "[[3.78714005]\n",
+      " [3.07335575]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
      "
       ]
@@ -2232,9 +2232,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.91483251]\n",
-      " [3.09848937]]\n",
-      "[3.87616945] [3.07001431]\n"
+      "[[4.02163476]\n",
+      " [2.9518022 ]]\n",
+      "[4.06200442] [3.03827385]\n"
      ]
     }
    ],
@@ -2390,16 +2390,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eigenvalues of Hessian Matrix:[0.26638638 4.40906565]\n",
-      "[[4.26969649]\n",
-      " [2.78617455]]\n",
-      "[[4.26953857]\n",
-      " [2.78630865]]\n"
+      "Eigenvalues of Hessian Matrix:[0.36579128 3.8614682 ]\n",
+      "[[3.94096731]\n",
+      " [3.09895512]]\n",
+      "[[3.94091671]\n",
+      " [3.09900258]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "
      "
       ]
@@ -4889,21 +4889,8 @@
       "Own inversion\n",
       "[[4.0586484]\n",
       " [3.0718316]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "theta from own gd"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
+      "Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]\n",
+      "theta from own gd\n",
       "[[4.0586484]\n",
       " [3.0718316]]\n"
      ]
@@ -4917,7 +4904,7 @@
      },
      "metadata": {
       "filenames": {
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+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_269_1.png"
       }
      },
      "output_type": "display_data"
diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png b/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png
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literal 0
HcmV?d00001

diff --git a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
index e992fe3da..ca31dc75c 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb
@@ -145,17 +145,17 @@
      "output_type": "stream",
      "text": [
       "Parameters for OLS using gradient descent\n",
-      "[[3.8184887 ]\n",
-      " [3.47851966]\n",
-      " [4.77551387]]\n",
+      "[[3.78243922]\n",
+      " [3.4825169 ]\n",
+      " [4.79102612]]\n",
       "Parameters for Ridge using gradient descent\n",
-      "[[3.92021197]\n",
-      " [3.11388017]\n",
-      " [4.9458396 ]]\n",
+      "[[3.94463114]\n",
+      " [3.02606605]\n",
+      " [4.9912346 ]]\n",
       "Parameters for Lasso using gradient descent\n",
-      "[[3.87323528]\n",
-      " [3.3008836 ]\n",
-      " [4.86284277]]\n"
+      "[[3.79673108]\n",
+      " [3.54406885]\n",
+      " [4.74100159]]\n"
      ]
     }
    ],
@@ -234,11 +234,11 @@
       "[[4.]\n",
       " [3.]\n",
       " [5.]]\n",
-      "0 [-31.91417133] [-44.48017159]\n",
-      "1 [3.48805429e-13] [4.67477123e-13]\n",
-      "2 [6.75015599e-16] [1.30675215e-15]\n",
-      "3 [-1.17239551e-15] [-1.78477759e-15]\n",
-      "4 [6.75015599e-16] [1.30675215e-15]\n",
+      "0 [-31.16622624] [-43.76852722]\n",
+      "1 [1.2420287e-13] [1.07341744e-13]\n",
+      "2 [-6.21724894e-16] [-7.8406741e-16]\n",
+      "3 [1.0658141e-15] [1.70040367e-15]\n",
+      "4 [4.4408921e-16] [8.75488337e-16]\n",
       "beta from own Newton code\n",
       "[[4.]\n",
       " [3.]\n",
@@ -745,28 +745,35 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Own inversion\n",
-      "[[3.90300704]\n",
-      " [3.16913489]]\n",
-      "Eigenvalues of Hessian Matrix:[0.2964378  4.12443871]\n",
+      "Own inversion"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[[3.79540402]\n",
+      " [3.14383145]]\n",
+      "Eigenvalues of Hessian Matrix:[0.29866395 3.79522963]\n",
       "theta from own gd\n",
-      "[[3.90300704]\n",
-      " [3.16913489]]\n",
+      "[[3.79540402]\n",
+      " [3.14383145]]\n",
       "theta from own sdg\n",
-      "[[3.93272428]\n",
-      " [3.16328315]]\n"
+      "[[3.78596158]\n",
+      " [3.12461758]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "
      "
       ]
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week40_34_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week40_34_2.png"
       }
      },
      "output_type": "display_data"
@@ -2082,17 +2089,17 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.13791264]\n",
-      " [2.92552059]]\n",
-      "Eigenvalues of Hessian Matrix:[0.27874136 4.16226023]\n",
+      "[[4.35914932]\n",
+      " [2.77699722]]\n",
+      "Eigenvalues of Hessian Matrix:[0.32641558 4.49529361]\n",
       "theta from own gd\n",
-      "[[4.13791264]\n",
-      " [2.92552059]]\n"
+      "[[4.35914932]\n",
+      " [2.77699722]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
      "
       ]
@@ -2183,73 +2190,73 @@
       "Own inversion\n",
       "[[4.]\n",
       " [3.]]\n",
-      "Eigenvalues of Hessian Matrix:[0.32606365 3.80499859]\n",
-      "0 [-10.98955596] [-10.8332972]\n",
-      "1 [-0.26421616] [0.25444295]\n",
-      "2 [-0.24157456] [0.23263884]\n",
-      "3 [-0.22087319] [0.2127032]\n",
-      "4 [-0.20194579] [0.19447592]\n",
-      "5 [-0.18464035] [0.1778106]\n",
-      "6 [-0.16881787] [0.16257339]\n",
-      "7 [-0.15435127] [0.1486419]\n",
-      "8 [-0.14112437] [0.13590426]\n",
-      "9 [-0.12903093] [0.12425815]\n",
-      "10 [-0.11797382] [0.11361003]\n",
-      "11 [-0.10786423] [0.10387439]\n",
-      "12 [-0.09862097] [0.09497303]\n",
-      "13 [-0.09016979] [0.08683446]\n",
-      "14 [-0.08244283] [0.07939331]\n",
-      "15 [-0.07537801] [0.07258982]\n",
-      "16 [-0.06891861] [0.06636934]\n",
-      "17 [-0.06301273] [0.06068192]\n",
-      "18 [-0.05761295] [0.05548187]\n",
-      "19 [-0.05267589] [0.05072744]\n",
-      "20 [-0.04816191] [0.04638043]\n",
-      "21 [-0.04403475] [0.04240593]\n",
-      "22 [-0.04026126] [0.03877201]\n",
-      "23 [-0.03681113] [0.0354495]\n",
-      "24 [-0.03365665] [0.03241171]\n",
-      "25 [-0.0307725] [0.02963424]\n",
-      "26 [-0.02813549] [0.02709478]\n",
-      "27 [-0.02572446] [0.02477293]\n",
-      "28 [-0.02352005] [0.02265005]\n",
-      "29 [-0.02150453] [0.02070909]\n",
+      "Eigenvalues of Hessian Matrix:[0.31711533 4.28519494]\n",
+      "0 [-8.99246366] [-10.02181163]\n",
+      "1 [-0.20915094] [0.17948385]\n",
+      "2 [-0.19367324] [0.16620159]\n",
+      "3 [-0.17934093] [0.15390225]\n",
+      "4 [-0.16606924] [0.14251309]\n",
+      "5 [-0.1537797] [0.13196676]\n",
+      "6 [-0.14239961] [0.12220088]\n",
+      "7 [-0.13186167] [0.11315771]\n",
+      "8 [-0.12210357] [0.10478375]\n",
+      "9 [-0.1130676] [0.09702948]\n",
+      "10 [-0.10470031] [0.08984905]\n",
+      "11 [-0.09695222] [0.0832]\n",
+      "12 [-0.08977751] [0.07704298]\n",
+      "13 [-0.08313374] [0.07134161]\n",
+      "14 [-0.07698164] [0.06606215]\n",
+      "15 [-0.0712848] [0.06117338]\n",
+      "16 [-0.06600954] [0.05664639]\n",
+      "17 [-0.06112467] [0.05245442]\n",
+      "18 [-0.05660129] [0.04857266]\n",
+      "19 [-0.05241265] [0.04497816]\n",
+      "20 [-0.04853398] [0.04164966]\n",
+      "21 [-0.04494234] [0.03856748]\n",
+      "22 [-0.04161649] [0.03571339]\n",
+      "23 [-0.03853677] [0.0330705]\n",
+      "24 [-0.03568495] [0.0306232]\n",
+      "25 [-0.03304417] [0.02835701]\n",
+      "26 [-0.03059882] [0.02625852]\n",
+      "27 [-0.02833443] [0.02431532]\n",
+      "28 [-0.02623761] [0.02251592]\n",
+      "29 [-0.02429596] [0.02084969]\n",
       "theta from own gd\n",
-      "[[3.93969971]\n",
-      " [3.05806981]]\n",
-      "0 [-0.01966173] [0.01893445]\n",
-      "1 [-0.01797685] [0.0173119]\n",
-      "2 [-0.01593089] [0.01534161]\n",
-      "3 [-0.01395192] [0.01343585]\n",
-      "4 [-0.01216264] [0.01171276]\n",
-      "5 [-0.0105836] [0.01019212]\n",
-      "6 [-0.00920294] [0.00886253]\n",
-      "7 [-0.00800011] [0.00770419]\n",
-      "8 [-0.00695371] [0.00669649]\n",
-      "9 [-0.0060439] [0.00582034]\n",
-      "10 [-0.00525303] [0.00505872]\n",
-      "11 [-0.00456562] [0.00439674]\n",
-      "12 [-0.00396815] [0.00382137]\n",
-      "13 [-0.00344887] [0.0033213]\n",
-      "14 [-0.00299754] [0.00288666]\n",
-      "15 [-0.00260527] [0.0025089]\n",
-      "16 [-0.00226433] [0.00218058]\n",
-      "17 [-0.00196801] [0.00189522]\n",
-      "18 [-0.00171047] [0.0016472]\n",
-      "19 [-0.00148663] [0.00143164]\n",
-      "20 [-0.00129209] [0.00124429]\n",
-      "21 [-0.001123] [0.00108146]\n",
-      "22 [-0.00097604] [0.00093994]\n",
-      "23 [-0.00084831] [0.00081693]\n",
-      "24 [-0.0007373] [0.00071003]\n",
-      "25 [-0.00064081] [0.00061711]\n",
-      "26 [-0.00055695] [0.00053635]\n",
-      "27 [-0.00048407] [0.00046616]\n",
-      "28 [-0.00042072] [0.00040516]\n",
-      "29 [-0.00036566] [0.00035214]\n",
+      "[[3.92905422]\n",
+      " [3.06088245]]\n",
+      "0 [-0.022498] [0.01930676]\n",
+      "1 [-0.02083309] [0.01787801]\n",
+      "2 [-0.01879191] [0.01612637]\n",
+      "3 [-0.01678891] [0.01440748]\n",
+      "4 [-0.01494559] [0.01282563]\n",
+      "5 [-0.01328658] [0.01140194]\n",
+      "6 [-0.01180564] [0.01013106]\n",
+      "7 [-0.01048771] [0.00900007]\n",
+      "8 [-0.00931621] [0.00799475]\n",
+      "9 [-0.00827534] [0.00710152]\n",
+      "10 [-0.00735068] [0.00630802]\n",
+      "11 [-0.00652931] [0.00560316]\n",
+      "12 [-0.00579972] [0.00497706]\n",
+      "13 [-0.00515165] [0.00442091]\n",
+      "14 [-0.00457599] [0.00392691]\n",
+      "15 [-0.00406466] [0.0034881]\n",
+      "16 [-0.00361046] [0.00309834]\n",
+      "17 [-0.00320702] [0.00275212]\n",
+      "18 [-0.00284866] [0.00244459]\n",
+      "19 [-0.00253034] [0.00217143]\n",
+      "20 [-0.0022476] [0.00192879]\n",
+      "21 [-0.00199645] [0.00171326]\n",
+      "22 [-0.00177336] [0.00152182]\n",
+      "23 [-0.0015752] [0.00135176]\n",
+      "24 [-0.00139918] [0.00120071]\n",
+      "25 [-0.00124283] [0.00106654]\n",
+      "26 [-0.00110396] [0.00094737]\n",
+      "27 [-0.0009806] [0.0008415]\n",
+      "28 [-0.00087102] [0.00074747]\n",
+      "29 [-0.00077369] [0.00066395]\n",
       "theta from own gd wth momentum\n",
-      "[[3.99902531]\n",
-      " [3.00093864]]\n"
+      "[[3.99783284]\n",
+      " [3.00185976]]\n"
      ]
     }
    ],
@@ -2334,17 +2341,17 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.11762444]\n",
-      " [3.04098313]]\n",
-      "Eigenvalues of Hessian Matrix:[0.29738252 4.51279273]\n",
+      "[[4.04829439]\n",
+      " [3.02060364]]\n",
+      "Eigenvalues of Hessian Matrix:[0.24427624 4.58825417]\n",
       "theta from own gd\n",
-      "[[4.11762444]\n",
-      " [3.04098313]]\n"
+      "[[4.04829439]\n",
+      " [3.02060364]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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uXLiARx99FJ07d0bDhg0RHx+PO++8E8eOHVOuwURERBRUFA+GKioq0KVLFyxYsMDhvrNnz2Lbtm144oknsG3bNuTm5uKXX37B3/72NwVaSkRERMHIIIQQSjfCymAwYMWKFRg9erTLfYqKinDNNdfg0KFDaNmypaTjlpeXIzIyEmVlZWjSpIlMrSUiIiJ/CtT1O8xvR/aTsrIyGAwGXHbZZS73qaqqQlVVle12eXl5AFpGREREWqT4MJknKisrMXPmTIwfP77OCDE7OxuRkZG2n6SkpAC2koiIiLREM8HQhQsXcNttt8FsNuPNN9+sc99Zs2ahrKzM9nP48OEAtZKIiIi0RhPDZBcuXMDYsWNx4MABrFu3zu24odFohNFoDFDriIiISMtUHwxZA6F9+/ahoKAA0dHRSjeJiIiIgojiwdCZM2ewf/9+2+0DBw5gx44diIqKQnx8PMaMGYNt27YhPz8fJpMJJSUlAICoqCiEh4cr1WwiIiIKEoqn1hcWFiIlJcVh+4QJEzBnzhy0adPG6eMKCgowePBgSc/B1HoiIiLt0U1q/eDBg1FXPKaiMkhEREQUhDSTTUZERETkD4r3DBEREVEdTCZg40aguBiIiwMGDABCQ5VuVVBhMERERKRWubnAjBnAkSOXtiUmAq++CqSnK9euIMNhMiIiIjXKzQXGjLEPhADg6FHL9txcZdoVhBgMERERqY3JZOkRcpZEZN2WmWnZj3zGYIiIiEhtNm507BGqSQjg8GHLfuQzBkNERERqU1ws735UJwZDREREahMXJ+9+VCcGQ0RERGozYIAla8xgcH6/wQAkJVn2C1bWkgIBwGCIiIhIbUJDLenzgGNAZL09f37w1hvKzQVatwZSUwPydAyGiIiI1Cg9Hfj4YyAhwX57YqJle7DWGXJVUsCPFF+oNRC4UCsREWmWnipQm0yWHqGLgVA5gEgg+BdqJSKiWvR08SP3QkOBwYOVbkVguCsp4CcMhoiI1ITLL5CeKVQqgHOGiIjUgssvkN4pVCqAwRARkRpw+QUi9yUF/ITBEBGRGnD5BaK6Swr4EYMhIiI14PILRBauSgr4EYMhIiI14PILRJekpwMHDwL5+QF5OgZDRERqwOUXiOyFhgbs885giIhIDfS+/AKRghgMERGphV6XXyBSGIsuEhGpSXo6MGoUK1BT8NBARXUGQ0REaqOn5RcouGmkojqHyYiIiEh+GqqozmCIiIiI5KWxiuocJiMiIiJ5eVJRvfaQcM05Rk2a+LWZVgyGiIiISF7eVlR3NscoABgMERFR8NJAJlNQ8qaiunWOkbOhNT/jnCEiIgpOublA69ZASgowfrzld+vWqpq4G7Q8rahe1xyjAGAwREREwUdDmUxBydOK6u7mGPkZgyEiIgouGstkClqeVFSXOsfITzhniIiIgosvmUwkL6kV1aXOMfITBkNERBRcvM1kIv+QUlHdOsfo6FFOoCYiIvKZN5lMWmcyAYWFwJIllt9aGwKsa45RADAYIiKi4OJpJpPWBUvWnKs5RgFgEEKhPLYAKi8vR2RkJMrKytAkQNUsiYhIQdZsMsB+2MUaINWewKtVrmrzeHqeaqrHVKMt5U2aIDI11e/XbwZDRER6oKaLXaA4q2aclGRJ6VZDIOTre2IyWXqAXE0WNxgsPWQHDtR9XBWvLB+o67fiw2QbNmxAWloa4uPjYTAYsHLlSrv7hRCYM2cO4uPjUb9+fQwePBg///yzMo0lItKiYBlG8VR6OnDwIFBQAOTkWH4fOKD4BR6APO+JJ1lzdbWD9ZiUD4YqKirQpUsXLFiwwOn98+bNw8svv4wFCxagqKgIsbGxGDp0KE6fPh3glhIRaZDeL3bWTKZx4yy//d0bZp3I/MEHlh6oDz5wnNAs13via9acSusxmavN+PadnXj82kL0j9kXmCcVKgJArFixwnbbbDaL2NhY8fzzz9u2VVZWisjISPH2229LPm5ZWZkAIMrKyuRsLhGRulVXC5GYKITl0ub4YzAIkZRk2Y98t3y569c7MdFyv5zvSUGB6+PU/Cko8M/jZXS6+LRYMXOLuKvtBtEi5I8aTx+Y67fiPUN1OXDgAEpKSjBs2DDbNqPRiEGDBmHTpk0uH1dVVYXy8nK7HyIi3ZFjGIWkcdXbY3XkiOX+Z5+V7z3xNWtO4XpMv28+ijdvW48RzYvQLC4MNz3fG+/uG4A/zDFogjKMTdqEhXdt8ctz16bqooslJSUAgBYtWthtb9GiBQ4dOuTycdnZ2Zg7d65f20ZEpHosPhgYUhcZFeJSLR13pLwn1to8Y8ZYAh9nWXM11/+qLcD1mMzVZhT9bxfyF59A3vdx+KGyHYBLafRXhB1CWueDSB3XGAMeSEZ4o34oLy/H39+V5enrpOpgyMpQK+oVQjhsq2nWrFnIysqy3S4vL0dSUpLf2kdEpEp6LD6oBE8WGT11Stp+Ut8Ta20eZ9lg7rLmpFR9bt4c6NdPWlucqCitwJpXdiJv+Xms/rUd/jAn2+4LgQn9muxEWv8/kTY5Ce1vvByGkFZeP5cvVB0MxcbGArD0EMXV+GCUlpY69BbVZDQaYTQa/d4+IiJVc3exs6ZeB0vxQaV42rMWFQX8+ad874nU9b9qq6tnyer4ceCKKzxKsz/87THkvbIP+esaYN3xzqhCb9t9TVCGG5J+RtpIgRFZHRDdtov08/QjVc8ZatOmDWJjY7FmzRrbtvPnz2P9+vXo50OkSkSkC3UtcSBlGIWk8bRnbcYMy2853xNvs+akVH12k+Vmrjbju0U/44kBhehafy9a9onHlKWD8NnxXqhCBC4PO4QZXdfjq3nbcPx0fSz9vR/ueKs/ottGeXaOfqR4z9CZM2ewf/9+2+0DBw5gx44diIqKQsuWLZGZmYnnnnsObdu2Rdu2bfHcc8+hQYMGGD9+vIKtJiLSCF+GUTyhx6KOVp4sMpqUBDz2GJCc7P/3RKr0dCA11RIQnTjheL8QlkAtM9PSAxUa6mT4q5NtdzUNf0nm11w1CQoKCgQAh58JEyYIISzp9bNnzxaxsbHCaDSKgQMHip9++smj52BqPRHpXnW1JUU6J8fyW850emcp5dZUcr1YvtySFl9XirrBYP+a+PM98ZTENPuVKfPFiObfCSPO2d3VBH+JsUnfiP+7b6M4vueEbM0K1PWby3EQEZH35FobKxg4W9bCKhDLgPjSO7dkiaUSthvjkIMPMQ4AcHnYIaQlH0TaeGv2V7gvrXcqUNdvBkNEROQdudbGCibWgOToUcvk4+bNLcNP/h429HV9scJCy5Igbkxr8B8kDboSqfcnokPqFTCEuM7slkOgrt+KzxkiIiKN8qSo4+DBAWuWoqwTmQPJVe+cdeKzm965w98ew+q3BG4OiUG0+ThC4NhHIgCYYxPw+pFJQRnYqjqbjIiIVIxFHZXnxfpiNbO/ujXYg5Z94vHARym4z/yW5X44ZrkZDAaEvvFaUAZCAHuGiIjIWyzqqDyJvXPnVn2JL4uiLNlf+69CSa3sr76Nf0bv/lE42vV1JL7/vDqy3AKIwRAREXmHRR2VJ7HX7b70UryHEbbbjVGOGxJ/RtpIE0Y82AHN2l198Z7BwDP3665MAoMhIiLyjq9rY5HvJPa6/Y5WaBP2O9I6HUDa+MYYODkZ4Y36Ot/Zn/OeVFqPisEQERF5L1BFHcmpinbdEdI4BsbTpU4nAZthwJmIZngzJw4dRiXBENIy4G208TXjzY+YWk9EFMwC9Ze4Sv/iD0ZHioqR/8o+5H1VH2uPd8aN+BQfYwwA2GWCCYPBMhVaDbWevKxHxTpDMmIwRES6pOK/xDVJoYDPXG3G1g/2IO+dUuR9H4sd59rb3d8m7Hc8lvA/3P7XAkSUlV66w5dCj3Keqw/1qBgMyYjBEBEFLVcXLVaGlleAA8uK0gp8NX8n8nOrkL+vHUrMLWz3WbO/0vqfQur9ieiYdrH4oVwBjNznKrGgIwoKHOYqsegiERHVzdVF6+WXgaws17Vnai26qXn+7rHxsaihVDWHv9YdT0Yletvua4xyDE/4GWk3mnDjQzWzv2qQY+KzP85VA/Wo2DNERKRFdfX8SP1n3clf4prj7x4bPy45ImX4yz77S/61v+z461zZM0RERLKTUnVYCq1Xhg5Ej43MS444Dn91BNARwKXhr9R+p5D2gHX4K4DZX/5aXkUD9agYDBERaY27i5ZUWq4M7S4glGsoUIYhHp+HvwLFX8NZGqhHxWCIiEhrfO3R8fQvcTWmzQdqkVgvlhypOfyV/30LbD/XAcCl+63DX6m3NcKgqZ1dFz8MNH8ur6LyelQMhoiItMaTi5Gvf4mrNT0/UJNyJQ7xVLTrjrWPfYu85Y7DXwaY0bfxTqQpNfwllb+Hs9LTLT11aguswWCIiEh7pF60Xn4ZePBB7/8SD1AWlVcCtUhsHUM8Apbbz5VNxTPx9VwOf414sD2ad1Bw+EuqQAxn+XOpDx8wm4yISIusgQrg/KJlDVS8HeLyYxaVLKztcxcQytW+3FyIGTNgqPF6/I4kZGI+VsASELYOO4y0jr8hbZx1+MvP2V/+4qw30JcCjj5g0UUZMRgioqDkz4uWD+nQASM1IPTB2RNn8dUrPyFveRU+++UKXCn2IQ7FKEYcvkZ/9G68u9bwl8Gn51MNlcwTY2o9ERHVzZ9zMDRQKM9fk3Kt2V/5ayOwtrSz3fBXGZpgeMJOTBphwrKsMm0Mf3lDpcNZ/sJgiIhIy/x10QrUnBxfyRAQmqvN2JZzsfhhkWP2V83hr4GTk2FsopLsL5INh8mIiMhRoOfkBFjN4a/V+65CsTnWdp81+yu17ymk3Z+ATqOuDJ7hL43hMBkRESnDOl9kzBjLcJNKC+V56uj31uKHjsNfjXAawxN2Im2ECTdmaST7i2TDYIiIiC5xNik7JMQSIFmppFCeO1KHv1Jva4RBUzj85ZJKJlP7E4MhIiKycFVXyBoIWZe3UPHF8OyJs1j76k7kfVyJ/F+uQnGt4od9Gv2MtH4nawx/JSnbYLVTa9FNmXHOEBERqb+uUB0ch7/q2+6rOfw1IrMdYjo1V7ClGuMqOJaxdIE7rDMkIwZDRERuaKGu0EXW4a/8d0uR910LbDvXwe7+VqFHkNbxV0vxwynJMDYxKtRSDVNJcMwJ1EREFDgqryvk+fBXoiLt1BxX84ECtRCuSjAYIiIKRrUvcv36AZs2uZ4Eq3RdIScX5aPbSy3FD7+KwFelnVGJa2y7W4e/Um8w4cYH2yGmU2f/tCuY1TUfqKpK2jGULLopIwZDRETBxtlFLjTUMSOs5iRYf69Y7mF7/0ALTMObtnW/AGfDX8z+8pqr+UBHjli2z5kj7ThKF92UCecMEREFE1cXudqcTYINwFpftVUtXoLwSeMtT1Nju/nircciXkKTgd2Q+vd4JN/UlsUP5eBuPhAAREcDERHAsWOKFt3kBGoZMRgiIs3xpraLlItcTc4uaAFYsfzo98VYPX8fVn9ZD28cvwXxOIoQJ/sJgwGG2u3TQc0bv5M6Wf7WW4GPPrL8f4CC49oCdf129vkjIiIl5eZagpqUFGD8eMvv1q0t2+vibtJrbTUnwVqlpwMHD1qyxnJyLL8PHPDpoifMAlvf3405gwvRo8FuJPaKw30fDETZ8SokugiEAMBQu33evi5kT+o8nzVrLMFQQoL99sTEgARCgcQ5Q0REauJqmOvoUcv2ui5C3k5mrf04GRZ/tWZ/5X9cifxf2uKYuQMASwq8AWb0bvQzMluvBXZKbJ8vrwvZkzrP59QpoFkzS3Ac5L1xDIaIiNTCZLIMUTmbvSCEZXjCWgXa2cXI28msMk2CPbatBPkv/4K8NRFYW5qMc7Wyv4bF/4y0EdWXsr8KTwIpz7g/cEwMMHGi968L2RswAIiKsgQ77hQXyxIcqx2DISIitfC1tou7jLDafMwQE2ZhWfvrv38gvygGW892BHBp9feW1uyvWxti8LTOMDbp41l7re0DdFXzxu9CQy1B9+zZ7vcNkmwxdxgMERGpha+FD0NDLenyY8Y4rjRfm5crz587dQ5r5/+EvGWuh7/S+p5E2n3W7K86ih/W1d6a7Sstlda4IKl5ExCPPQa89hpw8qTz+/1ZSkGFGAwREamFHIUP09Mt82ek1BmSmCF2rOgotv7zY+z5rhxflffCVxgKMywBVEOcwfD4nUgdfgEjH2rvefFDV+2t2b7CQmnH0kkvhixCQ4GFC4Gbb3a8z8tAWctUn1pfXV2NOXPm4IMPPkBJSQni4uIwceJEPP744wgJkZYMx9R6ItIEa2q8u2EjKbVdPK1AXYMwC2xfYhn+OrPpB0w//yKScClQOYY4rG75AFr+fcTF4S8Z1v6qK2VejteFKfnOBaCUgi8Cdv0WKvfMM8+I6OhokZ+fLw4cOCCWLVsmGjVqJObPny/5GGVlZQKAKCsr82NLKWhUVwtRUCBETo7ld3W10i0iPVm+XAiDwfJjufRbfqzbli93fwwvPsNnT54VeU98K/7efr2IDzkmACFuwnJhgkGYarYDEGZP2iIXX16X5cuFSEy0f1xiYmDbr2Yq/jcvUNdv1QdDI0eOFHfddZfdtvT0dHHHHXdIPgaDIZKM/2iSGjj7HCYlSfscevAZPrq1WCzMWC/SWmwR9VFh95DG+EscD4kR5lqBkF0QkpQU2AunN6+LNYhy1v5AB3TksUBdv1U/TPb888/j7bffxpdffomrrroKP/zwA4YNG4b58+dj3Lhxko7BYTKSxFUdkwBWWyWy8WZYx81nWHy0DNurOiLvv38g7ztr9tclNbO/UrqcQnjacPftLCgIbAaXJ6+Lu4rcAVpSgrwXqOu36idQP/rooygrK0P79u0RGhoKk8mEZ599ts5AqKqqClU1VtwtLy8PRFNJy3yt76IkzoUITp7WdnHzGRYAjt0yHb3wO8y1sr9S+1iyvzqn18j+WrJE2vMGOoPLk9fF11IFpBuqD4aWLl2K999/Hzk5OejUqRN27NiBzMxMxMfHY8KECU4fk52djblz5wa4paRpWv1H09nkx9qrkZM+uPkMGwAk4BiG4UvUj2uKtBsu4Mas9miRXCv7yxpc79ol7XnVnMHla6kC0g3VB0MPP/wwZs6cidtuuw0A0LlzZxw6dAjZ2dkug6FZs2YhKyvLdru8vBxJSUkBaS9plBb/0dTT8gTs/aqTMAscXPY92kjY95P/lCL8nhHO73QWXLuihTo0cpQqIF1QfTB09uxZhxT60NBQmM1ml48xGo0wGmVI9ST90No/mloe1vOUlN4vrQRLMrbz3KlzWPfaTuR9dA75e9viSnNPFEp4XPiVrZzf4Sq4dkbuOjT+ev+kVrhWc0BHgeHX6dkymDBhgkhISLCl1ufm5opmzZqJRx55RPIxmE1GblVXW7JUnGWdKJU5U5eCAuftrP1TUKB0S30jJRNIKxmAMrTz2PYS/2R/WT//Uj5TgBDNmwuxbJlqXhe3x/e1VAEphqn1F5WXl4sZM2aIli1bioiICHH55ZeLxx57TFRVVUk+BoMhkkRL/2jm5Ei7aOXkKN1S77m7QBsMQkRHayNt2sv0brPJLLa+v0vMTSkQPRv87PDwpNAjYnJyofjs6SJx7s9z3n+GpQbXcgcsgUp796VUASmKwZCMGAyRZFr5R1MPPUPeXKDV2JsnJair0c6zJ8+K/Nnfifs6rBcJF4sf2naFSfRu+JN4ZkiB+GHZXmE2mR2fz5vPsNTgWs6AxcPXxWcqLixIrrHOkIxYZ4g8ooX5J3Iu26BWS5YA48f7fpxA18GprbAQSElxu1v+0Pn4z099sKakM86hgW17Q5zB0LidSLu49leL5Obun9PTz7DENjrw5XMm9TmVfv9IUawzRKQUT+u7KEHqat9aDYQA+SarK50BKPH5P1gTg1XoDQBICj2KtA77kXZrQwyemoyIy/p49pyefobdTTR2RQjvS05oMYOTgpa0lU4vOnz4sL/aQUSesq72nZBgvz0xMTjS6q0XaGtw5y2lMwAlPn/TiEo8fX0hdizdi0Pn4/HGT4Nww+M9EXFZhH/bB1wKrgHvXm9vAhatZXBSUPNomKxhw4bIysrCzJkz0bBhQ3+2S1YcJqOgpoVhPW9Z070Bx94vIYDoaODUKdUOFRbv+AOfvrQbo3JuRZT5OELg2E4BwBwbj9Ajvyv/vnlSZ6gmb4ay9DDUq3Uq+LclUNdvj3qG1qxZgy+//BJt27bFokWL/NUmCiYmk2VuwJIllt8mk9ItCj7WIZFx4yy/g+nCUVfv1/LlwMKFltu1ezMUGioUZoHtS/bgqesK0avhLsR3a4F73h+Mv5vfAgCY4dhOg8GA0DdeV8f7lp4OHDxoCW5ycoCvvrK89q56iwwGICnJuzo9dfVGBctQr5bl5lqC1ZQUy9y9lBTL7dxcpVvmH97Muv7f//4nEhMTRdeuXUWBBrJVmE2mEK3UfyHfBCJLp67n8CUDUIa2nz15Vqye8524v+N6kRh61Gn219PXF4gDWa8JsxYyFWvzd8kJrWRw6kmgSh5IoPpssnPnziE7OxsvvfQShg0bhn/961+48sor5Y3UZMJhMgVwBXh9UMvaaN6u8O5l24t3/IHVr/yCvC/D8VVJMs7i0rSBBqjAsLifnGd/Wdt59Chw/DjQvLml58Xfww++Dnc4e62Skiw9N3K8zyoYjqGLrMOXroZKAzx8GbDrt7dRVEVFhdi4caPIzMwUISEhwmg0iqysLFFeXi5fqCYT9gwFWKDrh5AyfP3rUcm6Lx623Wwyi205u8VT1xWIXg13OjzMWvzw07nfWYofunvuQPaYyvV8rNOjDyqrYabKootvvfWWuOuuu0Tnzp1FWFiYMBqNolevXmLatGninXfeEdddd51ITEwURUVF/mqvVxgMBZjKvkzkB74GvEoOoUps+7kTZ1wOfwFCXHNx+GvH0j3Oix86E+jhBxUNd5BGqKy6vSqDocTERDFmzBjx4osviq+//lpUVlY67PPss8+KTp06ydZAOTAYCjCVfZnID3wJeJW+QEts+3B8arepAc6I0XGbxTsTN4jiH/7w/HmVqLjMHlrylMr+mA3U9dujootS6gzdfffdeOKJJ7wYsKOgwfohwc/bgnkmk2XuiRCO+wphmY+QmQmMGuW/+QgS294UfyEp9ChS2/+KtLH1kTK9s/Pih1Lnu2zcWHfKuhCeFzCs67n98XwU/NwV4LTOGfImg1DFZK9AHRMTg3Xr1sl9WNISnX6ZdMXbgFcFF+jzjaMRLmG/p5+8gCtmx8MQkuB6J08mYctdcdndc7PCM3lDD9XtnfCozpAUBoMBgwYNkvuwpCWsHxL83FWHdlV/RuqFd+1aWWtSlfxYinfvLMTjUW/ivrQjKEUzmF3tfLHtVz55OwwhdVRjtmZM1g7ujh61bK9dj0XOHlMpz80eWvJWsFe3d4ILtZL/+Dsdl5RVV3VowPk/mp4sCCo1Rd/JUJEwhOCHZb8gb2Ex8r5tjsSKvXgVM5CES59Fa4vtwh2ppR/cpR9b23/w4KWgX66Ky1JTn/fvB664ghWevcV0f1W8BqpPrdcSTqBWENNxg5unBfOsk3qdTaD2ZkK1k+c/FdZc3Buy0LbpJiwXJhiEScqkUKnF/qROMp0717G9vhYw9GSCq78LJgYrFoxVDVVmk2kVgyEiP/I04HV1gfY042n5cmE2GIS51mMsgY9B3IYPxE0tvhGnGzR32Mfu+M2bC/H++9Labj3XqVOlBSSA4wXU14rLnmZrssKzZ5TOdiQ7qq9ArSUcJiNd8XfXthzH93RB0IsLgQqzwA/LfkH+v4/g3sLb0Vz84XTiowCAhEQY/rcYGDJE8vFlbbNVUpLjUJQvr6HUocaa56SC4Q5NUFn1ZeIwmazYM0S64e/ufTmPX10txOOPS+rl2DfwLjG54zqRFHpEAEIMQoG03hGJx3db88pVb4HUHzlrsrgbamT9IO+prMYOBe76LXs2GREpxNPsJqWPHxoKXH+9pF2v3PAuZu66Ez1N36IBKpAWudGz53KnroyqumojSSVn+jqzNf2H5Qh0i8EQkRxMJsvwxZIllt8mk/Nt/nz+uooZApZiht62wV/Hv5iiL1yl6NeQiCNYjptxavEqPLRSYo2qwYO9KwFQk7vaSFLInb6uw9TngGA5At2Svegike44m0sSHW35ffLkpW3+XM3d38UM/XD8yr8qUfDaTzhsuA/3iCchYEAIXPe+GADAYIDxiUctaeNSCnsOHux7ATlfegH8WWA0Pd1SqZtzgeTDgrG6xZ4hIl+4Gjo6edI+EALkG65yxt/d+zIdv+THUrw7aSNuit+CZk2rcePsXrjv8OMYg49xDPHuj28NujZtkj5U5Gsvire9AIEYsgoNtQR848ZZfjMQ8g2HIHWLwRCRtzydS+LtcJKU4TZ/d+97eXxhFvjho714ZkghejfaibguMbh78QCsLO6DCjRCYugx3N9xA+6Zk4To0j3A449Le57iYs+CnPR0SwHEggIgJ8fy+8ABab10UqptR0db9nHXDlI/DkHqElPribzlSTXl2qSkcgPS1776+GPgllvqPpazFG+pPKieXHn6Agpe+wn5y84ib/eVOGyyv6j0avgz0nofR+o9seh6azv7JS/UmjYupdq2EkNWTJn3H762qsDUehkxtZ78QmrxO29SuYWQXvzNmmrt7jk/+si3862jmrHZYBBfpTwtRsdtFg1x2u7u+qgQf4vdIv5z5wZxbHtJ3c+h5rRxtRUvZJVk0gEWXZQRe4bIL/zZM+RJ8beNGz3vTfGWk56qEkMspojXkYsxtm0JIcVIa78PaWPrI2VaMupH1ffsOTxd8yxQ1NJbYH2Nav/zrYbXiEhGgbp+M5uMyFvuMk+ckZqN4kn2VoBqo1T+VYnCXS2RH/k/HD92EiHmahQjDhvFAJgRit4NfsQDV63DoGtNaHVTdxgGDfQuULDO2XA2PCh1kV9/BS3WCctKclfmwGCwzEsbNYrDOkQSMRgi8pY188RZ2rYznmSjeBLg+HHy9B87j2P1S3uQ90U9rClORgV62u6rj7MYGvsT/j1sE26+eh+azp8N7DgC7ACwAL6VEvAlbVzqPCutkqvMgVp6uYjUwK+DcCrBOUPkV87mbkRHW368nV/iybIAMs6zMZvMYsfSPeLp6wtE74Y/CQNMdodKCDkm7u+4XuTP/k6cPXn20vmrZWFLNbXFXzxdqNUZzjcijeCcIRlxzhD5nbO/sgHv//L2IHsLoaE+zbOp/KsShQt2Im9pBfJ3X4HfTfYp4j0b7EJa71Kk3esk+0tNC1uqqS3+5E3GXU2cb0QaEqjrN4Mh0g69det7GuA4Gx5KSnI6z8Zx+KuR7T7r8FfasPO4cUZbxHePdd1GXy/McpLalscft6yJptXPj6eBsrPHBnvASEGDqfUy4jBZENBrt76n6dzV1Zahs5ycS0No4tLw1zNDXA9/3deh1vCXFHIM2cjF01IHWv781FHmoM7hQK7KThoTqOs3J1CT+rnq1rcubxHM3fqeTiSuke1U+VclCrO31xj+agegnW1Xx+EvCROsa/fOxcRIO49ALGzp6XNo+fPjbcYdV2UncorDZKRu7Nb3iHX4K/+LevjSyfDXkBY/IW1YFUZmXlX38JczzobhEhKAykrg1CnPh2zk5u6z4ozWPz+eDh2raViTSALWGSIC/L8au8YJs8BPufuQ9+9jyNvcDN9VdITApRpGCSHFSG1nKX543fRk1I/q7d0TueqdO3bs0jZvV4WXS2gocO+9wOzZ0h+j9c+Pp3WPuCo7kVMMhkjd2K3voKq8CgWv/VRj+OsqAFfZ7u/ZYBdSrylF2j0t0G1ce2nDX3WRUuQvKgqoX9/7IolyadvWu8fp5fNTV20srspOOsZgiNTN36uxa0Tpzxezvz63Dn/ZFz+0H/7qCKCjfE8upXfu5Engq68sF1Els/28/RwE+efHjhwVvomCDIMhUjedduvXHv76vqIdroUJcTiCnqjEr4YrMbL9b0gdE4HrMzu7H/7ypSyB1F6T0lJg3Dhp+/qLp0ukePP5CYYSD75U+CYKQgyGSN101K1fVV6Fwtctw195uy4Nf92EXBzACCTh0l/xIiERhmckLi/h6/IUWuqd82SJFG8+P3W9lloLLtSwzhqRWvg1cV8mR44cEbfffruIiooS9evXF126dBHff/+95MezzlAQ8LTejkb8sbNUvDtpg7gpbrNoiNN2p1cfFeK5yGxhBoTZ2+Ul5FieQsblPgLG2eclNNS3z09dryXguPyKlusYEakEl+O46M8//0S3bt2QkpKCBx54ADExMfj111/RunVrXHHFFZKOwdT6IBEEwxPW4a/8hceQtzka357pBIEQ2/3xIcVIvWof0m6JwHVTOqBBz47elxWQsyyBq2wyq4cfBubNq/sYcpLyWai9T79+wKZNvi2P4mnaPqDNOkZEKsHlOC6aOXMmvvnmG2zcuNHrYzAYIiXVHP7K33UFDtVa+6tHg11Is8v+ungR9bUmjNw1ZR55BPjXv5zfZzAE7qKvxKr0Ul/L2rRex4hIYawzdNGqVaswfPhw3HLLLVi/fj0SEhIwefJk3HvvvS4fU1VVhaqqKtvt8vLyQDSVyMaa/ZX/RT18eawTzjjJ/kodUoXUrDqyv3wtKyBnWQKTCViypO59MjMt82b8edFXqhq5t6n3Wq9jRKQTqg+GfvvtN7z11lvIysrCP//5T3z33XeYPn06jEYj7rzzTqePyc7Oxty5cwPcUgoKXg7FCbPAzhXW7C/r8NelDCW74a/pyWjQTELxQ18nLss58VkNxS+l1DvyV0Dm6+RwvdQxItIo1Q+ThYeHo2fPnti0aZNt2/Tp01FUVITNmzc7fYyznqGkpCQOk1HdPBx+kTr8lXp3C3QfX2P4SypfVieX4/E1LVkCjB/vvs05Of5Lr1dyKQl3r6USbSLSAQ6TXRQXF4eOHe2HEDp06IDly5e7fIzRaITRaPR30yiYSBx+Kf35OD59ZS/yPgtzGP6KwDkMifkJaUMrMTKzLRJ6+lj80NeyAnKWJVBDer2S1cg9SdmvKUjrYBEFmxD3uyirf//+2Lt3r922X375Ba1atVKoRRR03Ay/CAGUjb8f/RvtQGxyNCa9cy1yj/XBGTRGfEgx/t5+A/Ke+A4njwvk/XEN/v7+QCT0lCkosFYLTkiw356YKG1+jK+Pt7IWMzS46N0yGICkJP9e9JUOyFy9ltHRlt+1X5sgq4NFFMxUP0xWVFSEfv36Ye7cuRg7diy+++473HvvvVi4cCFuv/12ScdgNhnVSeLwy2AUYD0Go3v93Ui75g+k3ePl8Jc3fC0rIEdZAmvvGeC8l8nf2WTeDvvJXZLB2fE++cRxiDUpictbEPkoYNdvv1YxkkleXp5ITk4WRqNRtG/fXixcuNCjx7PoYhCqrhaioECInBzLb18K/uXkOC8mWOvnq/5PiCNFx+Q6A21SuviltfBh7eKHropIOmuvv4ohyvmZJCIhBIsuyoo9Q0FGhjozNbO/jm78DW9U3u3+QZwEa6F08Utn77+zXhhX88BYDJFIM1h0UUYMhpxQ+oLmLR8ucFXlVVj/xk7kLTmD/N2X42B1EgAgBCYcRGsk4ChC4GPWFQWGu8+vnNW3iUgxDIZkxGCoFiUq+MrBiwvc8d0nsPqlPTWyvxrbdrdmf6UOqcSYLvsQPfNiIU8l5sOQvJRMwyci2TC1nvxDqQq+cpBY+O/Ac0vw4deJyNsUjS1nOkHgWtsucSElSL3qF6SNicD1M5LRoNk1F+8ZCFzZ1HmQyEmw2qNkGj4RaY7+giGtDg/JQckKvnKQeOH655Oh+BCDbbet2V+pd8Wg+/j2CAmLdf7A9HTLuQfj50Nvn3ul0/CJSFP0FQytWgXMmqW94SG5qGFJBV9IvHCdRBRGxnyHtCGVSH2wLRJ6dgDQQdpzhIaq89x9odVhUV9Y6yK5S8NnMUQigt6CoYwMx21aGB6Si4aHDoRZ4OcTsWhpbI5GVSecTnQWAM41aYGVv/RDgxaNHQ+iR1oeFvWFnNW3iSjoqb4Ctd9Zq4/MmGEZSghmGhs6qCqvwpfZWzG9y3pcbjyCzre0x8SqtwEAZtgXOhQGAwwGAxosejNwgZDJZJmou2SJ5bfaPj/uhkUBy7Co2totF7mqbxNR0NNXNhmAOueiz50LPPlkgFqlADkX7vST47tP4NOX92D1p8DpY+W4DGUoRhw2YgDCcR7Xx/yE6e2+xHV730RYaY0erEBX+9XC0BMzqiz0Nl+KKIgwm0wJs2cDycnquZjJTYVDB8Is8PMn+5H39lHkb47C5tPJGI1SvIoZSMKlQONc4xiEvDYfxonjAFwDmGYpd4HTytCThodFZRWM88CISFbsGaotKSn4C7FJreDrDyYTzn++FnvfK0JBUUO8emg0fjO1tt19E3LxMcbAAGE/EKaWej9aKubHniEi0jgWXZSRR8EQoI+LQ4CHDo7vPoGd0/6NzgWvoZm51Lb9MBLxMObhTMwV+Nt1Fbhr3e32w181qSHQ0FKAoYFhUSKiunCYTEn+GDZQ27wFPw8dWIe/8v99FHmbohB7+hcswxNArSywRBzBEsPtMLz1MRAVBXxYx2uvhtR/LQ09qXBYlIhIjfSVTTZrlrT95M6mys21/IWekgKMH2/53bq1ZXsQOX/mPNY8b8n+usJ4GJ3T22LWF4Ox5XQnzMeDAITDB85w8QeZmZYeDCmUDDQ0lpHHjCoiIvf0NUx26hSadO7s+qLrj2GDIF8525r9lf9ZKL442gmnawxEGlGJ65v/hPs6b8Lf1mW6P9grrwAPPuh+PyWHoLQ69KS2nkkiIgk4TOYPoaHAa69ZghPA/8MGWl/+wglhFtiV9yvy3jqCvE2W7K+aa3/FhvyB1LZ7kXazEdfPSEbDmF7Akv3AOgkHb95c/VWD6xp6Aiy3b77ZEnioKeBgRhURkUv66hmyRpaByqbS0mTbOpw/cx7rF/yEvCVnkL+rDQ5Ut7S7v1v93Ujt+QfS7o5Bj9vbIySs1mCYJ6/DqVN1B6tq6Ulz9hkKDbUvYKi2ukNERBrDbDIZOX0xAzFssGSJZY6QOzk5wLhx8j63j07sPYlPX9qNvE9dD3+lDTmH1AfbIrGXm/kxng4tKZn67wnrZ+iTTyxtq01tARwRkcYwGJJRoF5MBxrqGXI+/HWph8c6/JWabsSQzGQ0jGno2RNY504B0np8tDLHRUt1h4iINIbBkIwUC4ZUPtn2/Jnz2PDmTuTlnEbez14Mf3lKKz0+ntBQwEtEpDWcQB0MVFjnxXH4q7vtPsfhrw4AOsj35OnplsniWujxkUpLdYeIiMgpBkP+Zq3z4mxRzwD0iFiHv/LfPoK8b6Kw+XQnmJ1kf10a/url1/YEHa3VHSIiIgcMhvzNZLJUVn7+eeD4cUv6eEKCX3tEHIe/rgRwpe3+rvX3IK1nSY3hrxZ+aYcDLaz07qkBA9RfDoCIiOrEYMif6rr4yxwIWYe/8j8LxRdHOqLcxfDXyBlXIql3ewDtZX1+t7Sy0runVDgUSkREnuEEan/xc+VpYRbYnX8x+8s2/HXpgtsipBSpV+5F2s3h3mV/ecNVBpgeMq6CcXI4EZHCmE0mo4AHQ366+Ncc/sr/uTV+q25ld7/j8FcAl56rqxcsKkofGVdaKQdARKQRzCbTso0bXQdCgEerr5/YexKfvbIbeatdD3+lXn8OqZkKDX8B7ofAZsyQdhytZ1xxyQsiIk1iMOQPPqRbOx/+upT95Tj8pXD2l5T11z74QNqxmHFFREQKYDDkDx6mW1uHv/KXnEbeztb4zUX2V+qk5uiZ0QEhYTF+aLSXpPSCWbPoTpxgxhUREakOgyF/kJBubYqNx5IcA1ZlbHY6/HVd85+QpvTwlxRSe8Fuv90yf4gZV0REpDIMhvyhjnRrcfE/txbPx/L/DLJtV93wl1RSe8FGjbIEiQoVnyQiInKF2WR+dOGDj2CaMg0RZaW2bb8jCZmYjxVIR5eIvUjrWYy0u6zDXwHM/pKLp+uvMeOKiIgkYjaZRp3cdwqfvrTrYvbXcJzBMQzARsShGCcRjfBmTTByyHm8mnkMSb3bAWindJN942nRQTVnXDFQIyLSJQZDPhJmgT2f/oa8Nw8j75um2FSe7JD9dcUVoUi7+XIMmdEJjWIbKdhaP1F4/TVZBONSIUREJAmHydxx0ltw/pwJG9+6uPbXTsfih0Ex/OUNrfas+LlaOBEReYcVqGXk9YvppLfgRGhzZJpewgfIsG2zZn+lXncWqTOuRMu+CXI2n/xJD0uFEBFpFOcMKUx8vBy45RYAAoYa26NMJ/B/mIBwQzUMV15pyf6a0QmNYjWS/UX2ZKwWTkRE2sRgqIYLZy9gwxs/YXXOX3h4xzS0gEDtAa4QCAgY8E7CbBh2s7dA83yoFk5ERMFBJ5NZXDu57xTef+Ab3NpyE5o1PIshj3THth0hiEOxyxfHAAHDkYu9BaRtHlYLJyKi4KO5YCg7OxsGgwGZmZlePd669te8GwsxMPIHxFwViYy3++Ojw/1QjkjEGI5jXGyBtIPJ1VtgMgGFhcCSJZbfJpM8x9Ubb15Ha7Vwg8H5/QYDkJTEpUKIiIKYpobJioqKsHDhQlx99dVePX7mgK/x5a5O+LX6CgBX2LZ3idiL1B7FSJvUDL0mdETI1ylAylPuDyhHbwFTuuXh7evoaZ0kIiIKOprpGTpz5gxuv/12/Oc//0HTpk29OsZbP16LX6tbIRxVuKFZEd64dT0ObTqKHefa4ZmvB6P33cmWNPhA9RZYU7prT+A9etSyPTfXt+Prha+vo7VOUkKtLMDERKbVExHpgGZS6ydMmICoqCi88sorGDx4MLp27Yr58+c73beqqgpVVVW22+Xl5UhKSsIdl3+G9DGXYeiDye6LH1ovsIDz3gJfL5JM6ZaHnK+jVuskEREFKabW1/Dhhx9i27ZtKCoqkrR/dnY25s6d67D9je39pL+Y/q6qzJRuecj5Oqp5qRAiIvIb1Q+THT58GDNmzMD777+PiIgISY+ZNWsWysrKbD+HDx/27snT04GDB4GCAiAnx/L7wAF5hk2kTr7+5BPfnyuYMTWeiIh8pPqeoa1bt6K0tBQ9evSwbTOZTNiwYQMWLFiAqqoqhNYayjAajTAajfI0wF+9BVInX8+fbxmu4bwV55gaT0REPlL9nKHTp0/j0KFDdtsmTZqE9u3b49FHH0VycrLbYwRqzNEj7ua6WHHuUN2sr+PRo45riwF8/YiINCxQ12/VD5M1btwYycnJdj8NGzZEdHS0pEBItawp3e7UnPNCjmq+jrWz/5gaT0REEqg+GApq6emA1OKRnPPiGlPjiYjIB6ofJpODKofJrAoLgZQU9/sVFDDTyR2mxhMRBRWm1uuFtcCjuzkvXA7CPabGExGRFzhMpjTOeSEiIlIUgyE14JwXIiIixXCYTC3S04FRozjnhYiIKMAYDKkJ57wQEREFHIfJiIiISNcYDBEREZGucZisLqxbQ0REFPQYDLmSmwvMmGG/dlhioiUNntldREREQYPDZM7k5gJjxjguonr0qGV7bq4y7SIiIiLZMRiqzWSy9Ag5qwZt3ZaZadmPiIiINI/BUG0bNzr2CNXEVeSJiIiCCoOh2qSuDs9V5ImIiIICg6Ha4uLk3Y+IiIhUjcFQbdZV5GsvmmplMABJSVxFnoiIKEgwGKqNq8gTERHpCoMhZ7iKPBERkW6w6KIrXEWeiIhIFxgM1SUQq8hzyQ8iIiJFMRhSEpf8ICIiUhznDCmFS34QERGpAoMhJXDJDyIiItVgMKQELvlBRESkGgyGlMAlP4iIiFSDwZASuOQHERGRajAYUgKX/CAiIlINBkNK4JIfREREqsFgSClc8oOIiEgVWHRRSVzyg4iISHEMhpQWiCU/iIiIyCUOkxEREZGuMRgiIiIiXWMwRERERLrGYIiIiIh0jcEQERER6RqDISIiItI1BkNERESkawyGiIiISNdUHwxlZ2ejV69eaNy4MWJiYjB69Gjs3btX6WYRERFRkFB9MLR+/XpMmTIFW7ZswZo1a1BdXY1hw4ahoqJC6aYRERFREDAIIYTSjfDE8ePHERMTg/Xr12PgwIGSHlNeXo7IyEiUlZWhSZMmfm4hERERySFQ12/NrU1WVlYGAIiKinK5T1VVFaqqqmy3y8vL/d4uIiIi0ibVD5PVJIRAVlYWrr32WiQnJ7vcLzs7G5GRkbafpKSkALaSiIiItERTw2RTpkzB6tWr8fXXXyMxMdHlfs56hpKSkjhMRkREpCEcJqtl2rRpWLVqFTZs2FBnIAQARqMRRqMxQC0jIiIiLVN9MCSEwLRp07BixQoUFhaiTZs2SjeJiIiIgojqg6EpU6YgJycHn3zyCRo3boySkhIAQGRkJOrXr69w64iIiEjrVD9nyGAwON2+aNEiTJw4UdIxmFpPRESkPZwzdJHKYzUiIiLSOE2l1hMRERHJjcEQERER6RqDISIiItI1BkNERESkawyGiIiISNcYDBEREZGuMRgiIiIiXWMwRERERLrGYIiIiIh0jcEQERER6RqDISIiItI1BkNERESkawyGiIiISNcYDBEREZGuMRgiIiIiXWMwRERERLrGYIiIiIh0jcEQERER6RqDISIiItI1BkNERESkawyGiIiISNcYDBEREZGuMRgiIiIiXWMwRERERLrGYIiIiIh0jcEQERER6RqDISIiItI1BkNERESkawyGiIiISNcYDBEREZGuMRgiIiIiXWMwRERERLrGYIiIiIh0jcEQERER6RqDISIiItI1BkNERESkawyGiIiISNcYDBEREZGuaSYYevPNN9GmTRtERESgR48e2Lhxo9JNIiIioiCgiWBo6dKlyMzMxGOPPYbt27djwIABGDFiBH7//Xelm0ZEREQaZxBCCKUb4U7v3r3RvXt3vPXWW7ZtHTp0wOjRo5Gdne328eXl5YiMjERZWRmaNGniz6YSERGRTAJ1/VZ9z9D58+exdetWDBs2zG77sGHDsGnTJoVaRURERMEiTOkGuHPixAmYTCa0aNHCbnuLFi1QUlLi9DFVVVWoqqqy3S4rKwNgiTCJiIhIG6zXbX8PYqk+GLIyGAx2t4UQDtussrOzMXfuXIftSUlJfmkbERER+c/JkycRGRnpt+OrPhhq1qwZQkNDHXqBSktLHXqLrGbNmoWsrCzb7b/++gutWrXC77//7tcXU23Ky8uRlJSEw4cP62quFM+b560HPG+etx6UlZWhZcuWiIqK8uvzqD4YCg8PR48ePbBmzRrcdNNNtu1r1qzBqFGjnD7GaDTCaDQ6bI+MjNTVh8iqSZMmPG8d4XnrC89bX/R63iEh/p3irPpgCACysrKQkZGBnj17om/fvli4cCF+//133H///Uo3jYiIiDROE8HQrbfeipMnT+Kpp55CcXExkpOT8emnn6JVq1ZKN42IiIg0ThPBEABMnjwZkydP9uqxRqMRs2fPdjp0Fsx43jxvPeB587z1gOft3/PWRNFFIiIiIn9RfdFFIiIiIn9iMERERES6xmCIiIiIdI3BEBEREemaJoOhN998E23atEFERAR69OiBjRs31rn/+vXr0aNHD0RERODyyy/H22+/7bDP8uXL0bFjRxiNRnTs2BErVqzwV/O95sl55+bmYujQoWjevDmaNGmCvn374osvvrDbZ/HixTAYDA4/lZWV/j4Vj3hy3oWFhU7Pac+ePXb7Bdv7PXHiRKfn3alTJ9s+Wni/N2zYgLS0NMTHx8NgMGDlypVuHxMM329PzztYvt+ennewfL89Pe9g+X5nZ2ejV69eaNy4MWJiYjB69Gjs3bvX7eMC8R3XXDC0dOlSZGZm4rHHHsP27dsxYMAAjBgxAr///rvT/Q8cOIAbb7wRAwYMwPbt2/HPf/4T06dPx/Lly237bN68GbfeeisyMjLwww8/ICMjA2PHjsW3334bqNNyy9Pz3rBhA4YOHYpPP/0UW7duRUpKCtLS0rB9+3a7/Zo0aYLi4mK7n4iIiECckiSenrfV3r177c6pbdu2tvuC8f1+9dVX7c738OHDiIqKwi233GK3n9rf74qKCnTp0gULFiyQtH+wfL89Pe9g+X57et5WWv9+e3rewfL9Xr9+PaZMmYItW7ZgzZo1qK6uxrBhw1BRUeHyMQH7jguNueaaa8T9999vt619+/Zi5syZTvd/5JFHRPv27e223XfffaJPnz6222PHjhU33HCD3T7Dhw8Xt912m0yt9p2n5+1Mx44dxdy5c223Fy1aJCIjI+Vqol94et4FBQUCgPjzzz9dHlMP7/eKFSuEwWAQBw8etG3TwvtdEwCxYsWKOvcJlu93TVLO2xktfr9rknLewfL9rsmb9zsYvt9CCFFaWioAiPXr17vcJ1DfcU31DJ0/fx5bt27FsGHD7LYPGzYMmzZtcvqYzZs3O+w/fPhwfP/997hw4UKd+7g6ZqB5c961mc1mnD592mGxuzNnzqBVq1ZITExEamqqw1+WSvLlvLt164a4uDhcf/31KCgosLtPD+/3O++8gyFDhjhUaVfz++2NYPh+y0GL329faPn7LYdg+X6XlZUBQJ2LsAbqO66pYOjEiRMwmUwOq9W3aNHCYVV7q5KSEqf7V1dX48SJE3Xu4+qYgebNedf20ksvoaKiAmPHjrVta9++PRYvXoxVq1ZhyZIliIiIQP/+/bFv3z5Z2+8tb847Li4OCxcuxPLly5Gbm4t27drh+uuvx4YNG2z7BPv7XVxcjM8++wz33HOP3Xa1v9/eCIbvtxy0+P32RjB8v30VLN9vIQSysrJw7bXXIjk52eV+gfqOa2Y5jpoMBoPdbSGEwzZ3+9fe7ukxleBtG5csWYI5c+bgk08+QUxMjG17nz590KdPH9vt/v37o3v37nj99dfx2muvyddwH3ly3u3atUO7du1st/v27YvDhw/jxRdfxMCBA706plK8bePixYtx2WWXYfTo0XbbtfJ+eypYvt/e0vr32xPB9P32VrB8v6dOnYoff/wRX3/9tdt9A/Ed11TPULNmzRAaGuoQ7ZWWljpEhVaxsbFO9w8LC0N0dHSd+7g6ZqB5c95WS5cuxd13342PPvoIQ4YMqXPfkJAQ9OrVSzV/Sfhy3jX16dPH7pyC+f0WQuDdd99FRkYGwsPD69xXbe+3N4Lh++0LLX+/5aK177cvguX7PW3aNKxatQoFBQVITEysc99Afcc1FQyFh4ejR48eWLNmjd32NWvWoF+/fk4f07dvX4f9v/zyS/Ts2RP16tWrcx9Xxww0b84bsPzFOHHiROTk5GDkyJFun0cIgR07diAuLs7nNsvB2/Oubfv27XbnFKzvN2DJ1ti/fz/uvvtut8+jtvfbG8Hw/faW1r/fctHa99sXWv9+CyEwdepU5ObmYt26dWjTpo3bxwTsOy55qrVKfPjhh6JevXrinXfeEbt27RKZmZmiYcOGtln1M2fOFBkZGbb9f/vtN9GgQQPx4IMPil27dol33nlH1KtXT3z88ce2fb755hsRGhoqnn/+ebF7927x/PPPi7CwMLFly5aAn58rnp53Tk6OCAsLE2+88YYoLi62/fz111+2febMmSM+//xz8euvv4rt27eLSZMmibCwMPHtt98G/Pxc8fS8X3nlFbFixQrxyy+/iJ07d4qZM2cKAGL58uW2fYLx/ba64447RO/evZ0eUwvv9+nTp8X27dvF9u3bBQDx8ssvi+3bt4tDhw4JIYL3++3peQfL99vT8w6W77en522l9e/3Aw88ICIjI0VhYaHd5/bs2bO2fZT6jmsuGBJCiDfeeEO0atVKhIeHi+7du9ul5U2YMEEMGjTIbv/CwkLRrVs3ER4eLlq3bi3eeusth2MuW7ZMtGvXTtSrV0+0b9/e7sulFp6c96BBgwQAh58JEybY9snMzBQtW7YU4eHhonnz5mLYsGFi06ZNATwjaTw57xdeeEFcccUVIiIiQjRt2lRce+21YvXq1Q7HDLb3Wwgh/vrrL1G/fn2xcOFCp8fTwvttTZ129bkN1u+3p+cdLN9vT887WL7f3nzOg+H77eycAYhFixbZ9lHqO2642EAiIiIiXdLUnCEiIiIiuTEYIiIiIl1jMERERES6xmCIiIiIdI3BEBEREekagyEiIiLSNQZDREREpGsMhoiIiEjXGAwRERGRrjEYIiIiIl1jMEREmrRkyRJERETg6NGjtm333HMPrr76apSVlSnYMiLSGq5NRkSaJIRA165dMWDAACxYsABz587Ff//7X2zZsgUJCQlKN4+INCRM6QYQEXnDYDDg2WefxZgxYxAfH49XX30VGzduZCBERB5jzxARaVr37t3x888/48svv8SgQYOUbg4RaRDnDBGRZn3xxRfYs2cPTCYTWrRooXRziEij2DNERJq0bds2DB48GG+88QY+/PBDNGjQAMuWLVO6WUSkQZwzRESac/DgQYwcORIzZ85ERkYGOnbsiF69emHr1q3o0aOH0s0jIo1hzxARacqpU6fQv39/DBw4EP/+979t20eNGoWqqip8/vnnCraOiLSIwRARERHpGidQExERka4xGCIiIiJdYzBEREREusZgiIiIiHSNwRARERHpGoMhIiIi0jUGQ0RERKRrDIaIiIhI1xgMERERka4xGCIiIiJdYzBEREREusZgiIiIiHTt/wHUvtBtY8UfJwAAAABJRU5ErkJggg==",
       "text/plain": [
        "
      "
       ]
@@ -2361,8 +2368,8 @@
      "output_type": "stream",
      "text": [
       "theta from own sdg\n",
-      "[[4.07058967]\n",
-      " [3.024004  ]]\n"
+      "[[4.04658881]\n",
+      " [3.02962903]]\n"
      ]
     }
    ],
@@ -2466,12 +2473,12 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.4252885 ]\n",
-      " [2.70944365]]\n",
-      "Eigenvalues of Hessian Matrix:[0.28973035 4.32089655]\n",
+      "[[4.08777858]\n",
+      " [2.93519647]]\n",
+      "Eigenvalues of Hessian Matrix:[0.30054056 4.29201128]\n",
       "theta from own gd\n",
-      "[[4.42394588]\n",
-      " [2.71059619]]\n"
+      "[[4.08779325]\n",
+      " [2.93518383]]\n"
      ]
     },
     {
@@ -2479,8 +2486,8 @@
      "output_type": "stream",
      "text": [
       "theta from own sdg with momentum\n",
-      "[[4.44845593]\n",
-      " [2.72577807]]\n"
+      "[[4.03961179]\n",
+      " [2.924011  ]]\n"
      ]
     }
    ],
@@ -2588,9 +2595,9 @@
      "output_type": "stream",
      "text": [
       "theta from own AdaGrad\n",
-      "[[2.00036797]\n",
-      " [2.99817613]\n",
-      " [4.00177485]]\n"
+      "[[1.99968483]\n",
+      " [3.00127606]\n",
+      " [3.9985605 ]]\n"
      ]
     }
    ],
@@ -2689,9 +2696,9 @@
      "output_type": "stream",
      "text": [
       "theta from own RMSprop\n",
-      "[[2.00119865]\n",
-      " [3.01346635]\n",
-      " [3.99284588]]\n"
+      "[[2.00035708]\n",
+      " [2.9930344 ]\n",
+      " [3.98966074]]\n"
      ]
     }
    ],
@@ -2786,9 +2793,9 @@
      "output_type": "stream",
      "text": [
       "theta from own ADAM\n",
-      "[[1.99997244]\n",
-      " [3.00018876]\n",
-      " [3.99983332]]\n"
+      "[[2.00005688]\n",
+      " [2.99969642]\n",
+      " [4.00028552]]\n"
      ]
     }
    ],
@@ -2992,7 +2999,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 25,
@@ -3073,7 +3080,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 26,
diff --git a/doc/LectureNotes/_build/jupyter_execute/week40_100_1.png b/doc/LectureNotes/_build/jupyter_execute/week40_100_1.png
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-    "\n",
     "\n",
-    "2. For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7.     \n",
+    "2. [Video of lecture](https://youtu.be/7B2F35gNj2Y)\n",
     "\n",
-    "3. Neural Networks demystified at \n",
+    "3. [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct14.pdf)\n",
     "\n",
-    "4. Building Neural Networks from scratch at \n",
+    "4. For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7.     \n",
     "\n",
-    "5. Video on Neural Networks at \n",
+    "5. Neural Networks demystified at \n",
     "\n",
-    "6. Video on the back propagation algorithm at \n",
+    "6. Building Neural Networks from scratch at \n",
+    "\n",
+    "7. Video on Neural Networks at \n",
+    "\n",
+    "8. Video on the back propagation algorithm at \n",
     "\n",
     "I also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at ."
    ]
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@@ -1284,7 +1286,7 @@
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@@ -1296,7 +1298,7 @@
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@@ -1306,7 +1308,7 @@
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@@ -1318,7 +1320,7 @@
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@@ -1328,7 +1330,7 @@
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@@ -1345,7 +1347,7 @@
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@@ -1357,7 +1359,7 @@
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@@ -1369,7 +1371,7 @@
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@@ -1385,7 +1387,7 @@
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@@ -1402,7 +1404,7 @@
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@@ -1414,7 +1416,7 @@
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@@ -1427,7 +1429,7 @@
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@@ -1439,7 +1441,7 @@
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@@ -1455,7 +1457,7 @@
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@@ -1467,7 +1469,7 @@
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@@ -1483,7 +1485,7 @@
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@@ -1495,7 +1497,7 @@
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@@ -1507,7 +1509,7 @@
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@@ -1517,7 +1519,7 @@
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@@ -1529,7 +1531,7 @@
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@@ -1539,7 +1541,7 @@
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@@ -1551,7 +1553,7 @@
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@@ -1565,7 +1567,7 @@
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@@ -1577,7 +1579,7 @@
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@@ -1587,7 +1589,7 @@
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@@ -1599,7 +1601,7 @@
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@@ -1609,7 +1611,7 @@
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@@ -1621,7 +1623,7 @@
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@@ -1633,7 +1635,7 @@
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@@ -1645,7 +1647,7 @@
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@@ -1655,7 +1657,7 @@
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@@ -1667,7 +1669,7 @@
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@@ -1677,7 +1679,7 @@
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@@ -1689,7 +1691,7 @@
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@@ -1707,7 +1709,7 @@
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@@ -1725,7 +1727,7 @@
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@@ -1737,7 +1739,7 @@
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@@ -1747,7 +1749,7 @@
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@@ -1759,7 +1761,7 @@
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@@ -1771,7 +1773,7 @@
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@@ -1783,7 +1785,7 @@
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@@ -1793,7 +1795,7 @@
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@@ -1805,7 +1807,7 @@
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@@ -1815,7 +1817,7 @@
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@@ -1827,7 +1829,7 @@
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@@ -1845,7 +1847,7 @@
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@@ -1855,7 +1857,7 @@
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@@ -1873,7 +1875,7 @@
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@@ -1883,7 +1885,7 @@
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@@ -1901,7 +1903,7 @@
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@@ -1913,7 +1915,7 @@
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@@ -1925,7 +1927,7 @@
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@@ -1935,7 +1937,7 @@
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@@ -1947,7 +1949,7 @@
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@@ -1959,7 +1961,7 @@
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@@ -1969,7 +1971,7 @@
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@@ -1981,7 +1983,7 @@
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@@ -1991,7 +1993,7 @@
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@@ -2003,7 +2005,7 @@
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@@ -2015,7 +2017,7 @@
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@@ -2041,7 +2043,7 @@
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@@ -2064,7 +2066,7 @@
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@@ -2076,7 +2078,7 @@
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@@ -2088,7 +2090,7 @@
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@@ -2098,7 +2100,7 @@
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@@ -2110,7 +2112,7 @@
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@@ -2124,7 +2126,7 @@
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@@ -2136,7 +2138,7 @@
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@@ -2148,7 +2150,7 @@
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@@ -2158,7 +2160,7 @@
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@@ -2170,7 +2172,7 @@
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@@ -2182,7 +2184,7 @@
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@@ -2192,7 +2194,7 @@
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@@ -2204,7 +2206,7 @@
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@@ -2216,7 +2218,7 @@
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@@ -2239,7 +2241,7 @@
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@@ -2258,7 +2260,7 @@
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@@ -2270,7 +2272,7 @@
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@@ -2280,7 +2282,7 @@
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@@ -2292,7 +2294,7 @@
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@@ -2309,7 +2311,7 @@
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@@ -2450,7 +2452,7 @@
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@@ -2472,7 +2474,7 @@
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@@ -2490,7 +2492,7 @@
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@@ -2513,7 +2515,7 @@
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@@ -2532,7 +2534,7 @@
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@@ -2560,7 +2562,7 @@
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@@ -2580,7 +2582,7 @@
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@@ -2601,7 +2603,7 @@
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@@ -2615,7 +2617,7 @@
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@@ -2627,7 +2629,7 @@
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@@ -2649,7 +2651,7 @@
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@@ -2671,7 +2673,7 @@
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@@ -2700,7 +2702,7 @@
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@@ -2726,7 +2728,7 @@
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@@ -2752,7 +2754,7 @@
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@@ -2772,7 +2774,7 @@
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@@ -2792,7 +2794,7 @@
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@@ -2818,7 +2820,7 @@
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@@ -2847,7 +2849,7 @@
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@@ -2865,7 +2867,7 @@
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@@ -2881,7 +2883,7 @@
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+   "id": "8a4d6517",
    "metadata": {
     "editable": true
    },
@@ -2893,7 +2895,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd701f7d",
+   "id": "322a5ffb",
    "metadata": {
     "editable": true
    },
@@ -2907,7 +2909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84048fb0",
+   "id": "d7c906f4",
    "metadata": {
     "editable": true
    },
@@ -2935,7 +2937,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61ea03ed",
+   "id": "d9b1c4a9",
    "metadata": {
     "editable": true
    },
@@ -2947,7 +2949,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abd0c817",
+   "id": "62795b6a",
    "metadata": {
     "editable": true
    },
@@ -2957,7 +2959,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e0379cc",
+   "id": "a302cc7a",
    "metadata": {
     "editable": true
    },
@@ -2969,7 +2971,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59290cf7",
+   "id": "892da0f2",
    "metadata": {
     "editable": true
    },
@@ -2980,7 +2982,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bc256eb",
+   "id": "0702951f",
    "metadata": {
     "editable": true
    },
@@ -2992,7 +2994,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "831eee08",
+   "id": "cb0f2050",
    "metadata": {
     "editable": true
    },
@@ -3005,7 +3007,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba46bcc0",
+   "id": "9d6da3d7",
    "metadata": {
     "editable": true
    },
@@ -3030,7 +3032,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a475ed33",
+   "id": "eb7f6521",
    "metadata": {
     "editable": true
    },
@@ -3043,7 +3045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "378895ce",
+   "id": "2d3caa00",
    "metadata": {
     "editable": true
    },
@@ -3055,7 +3057,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "268989a6",
+   "id": "b84b3da0",
    "metadata": {
     "editable": true
    },
@@ -3067,7 +3069,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ac5dfe0",
+   "id": "5be6ff37",
    "metadata": {
     "editable": true
    },
@@ -3077,7 +3079,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d3d6bb7",
+   "id": "491b47ff",
    "metadata": {
     "editable": true
    },
@@ -3089,7 +3091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc4e4a53",
+   "id": "c752e63e",
    "metadata": {
     "editable": true
    },
@@ -3101,7 +3103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cc000a1",
+   "id": "efe0f0c7",
    "metadata": {
     "editable": true
    },
@@ -3113,7 +3115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f5ea691",
+   "id": "13ee778d",
    "metadata": {
     "editable": true
    },
@@ -3125,7 +3127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7f4fc8e",
+   "id": "6884cc1e",
    "metadata": {
     "editable": true
    },
@@ -3135,7 +3137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acdecb39",
+   "id": "9d7a0a4e",
    "metadata": {
     "editable": true
    },
@@ -3147,7 +3149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0c3e8c7",
+   "id": "35bfbdc8",
    "metadata": {
     "editable": true
    },
@@ -3157,7 +3159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e130deb",
+   "id": "631ba4a6",
    "metadata": {
     "editable": true
    },
@@ -3169,7 +3171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23e83ce8",
+   "id": "a37f8d69",
    "metadata": {
     "editable": true
    },
@@ -3182,7 +3184,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67c77893",
+   "id": "fcedfc85",
    "metadata": {
     "editable": true
    },
@@ -3194,7 +3196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36ede636",
+   "id": "4c1ab15e",
    "metadata": {
     "editable": true
    },
@@ -3204,7 +3206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4008b26c",
+   "id": "6518cab5",
    "metadata": {
     "editable": true
    },
@@ -3216,7 +3218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "258ae1d4",
+   "id": "ccfcb38c",
    "metadata": {
     "editable": true
    },
@@ -3227,7 +3229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9c648be",
+   "id": "4174ce25",
    "metadata": {
     "editable": true
    },
@@ -3239,7 +3241,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "615f1fce",
+   "id": "4602e3f2",
    "metadata": {
     "editable": true
    },
@@ -3249,7 +3251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "620b34cb",
+   "id": "4b71d88d",
    "metadata": {
     "editable": true
    },
@@ -3261,7 +3263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d09dc1a",
+   "id": "d5f0d911",
    "metadata": {
     "editable": true
    },
@@ -3271,7 +3273,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8589b8ee",
+   "id": "d0c13a16",
    "metadata": {
     "editable": true
    },
@@ -3282,7 +3284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63160327",
+   "id": "7af1d556",
    "metadata": {
     "editable": true
    },
@@ -3295,7 +3297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98614055",
+   "id": "a42c51d7",
    "metadata": {
     "editable": true
    },
@@ -3305,7 +3307,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37435d7c",
+   "id": "6365d360",
    "metadata": {
     "editable": true
    },
@@ -3317,7 +3319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64a4d79f",
+   "id": "557a932f",
    "metadata": {
     "editable": true
    },
@@ -3327,7 +3329,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e3891af",
+   "id": "055db867",
    "metadata": {
     "editable": true
    },
@@ -3339,7 +3341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7205e781",
+   "id": "ccd85582",
    "metadata": {
     "editable": true
    },
@@ -3349,7 +3351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5645092",
+   "id": "d2d1077a",
    "metadata": {
     "editable": true
    },
@@ -3373,7 +3375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09e9b12d",
+   "id": "3dff91e3",
    "metadata": {
     "editable": true
    },
@@ -3423,7 +3425,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "84fd1480",
+   "id": "0527bd02",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3500,7 +3502,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e671e2a8",
+   "id": "b5238d8c",
    "metadata": {
     "editable": true
    },
@@ -3521,7 +3523,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "5d1c4624",
+   "id": "bba6be21",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3568,7 +3570,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b397d4ee",
+   "id": "4a77d660",
    "metadata": {
     "editable": true
    },
@@ -3612,7 +3614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fce534f",
+   "id": "0d276258",
    "metadata": {
     "editable": true
    },
@@ -3652,7 +3654,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c50a158",
+   "id": "f25c6fa1",
    "metadata": {
     "editable": true
    },
@@ -3673,7 +3675,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "4daeba9a",
+   "id": "3a4a6bba",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3699,7 +3701,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f26a835c",
+   "id": "de886e02",
    "metadata": {
     "editable": true
    },
@@ -3727,7 +3729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09f1187b",
+   "id": "baeb1290",
    "metadata": {
     "editable": true
    },
@@ -3764,7 +3766,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "35d71f0e",
+   "id": "b575ea05",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3827,7 +3829,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e318575f",
+   "id": "a981e9cf",
    "metadata": {
     "editable": true
    },
@@ -3858,7 +3860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "788f45d2",
+   "id": "fcb5b7b4",
    "metadata": {
     "editable": true
    },
@@ -3896,7 +3898,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "599a7b8f",
+   "id": "135edd42",
    "metadata": {
     "editable": true
    },
@@ -3930,7 +3932,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc694849",
+   "id": "6a94b210",
    "metadata": {
     "editable": true
    },
@@ -3971,7 +3973,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "e3607c66",
+   "id": "10a0f4b1",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3988,7 +3990,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4073,7 +4075,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a70cdcf",
+   "id": "37e26c5f",
    "metadata": {
     "editable": true
    },
@@ -4094,7 +4096,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0600212",
+   "id": "3721f1b2",
    "metadata": {
     "editable": true
    },
@@ -4108,7 +4110,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4e0c1326",
+   "id": "a2225589",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4218,7 +4220,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "125f8eca",
+   "id": "9d915d49",
    "metadata": {
     "editable": true
    },
@@ -4237,7 +4239,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "cef0e788",
+   "id": "62e979c5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4272,7 +4274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b12d6f86",
+   "id": "32464471",
    "metadata": {
     "editable": true
    },
@@ -4286,7 +4288,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "974faa4e",
+   "id": "c3277c8f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4576,7 +4578,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4594,7 +4596,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4612,7 +4614,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4630,7 +4632,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4648,7 +4650,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4666,7 +4668,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4684,7 +4686,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4702,11 +4704,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4724,11 +4726,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4746,11 +4748,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4768,11 +4770,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4790,11 +4792,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4812,7 +4814,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -4830,11 +4832,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4852,11 +4854,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4874,11 +4876,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4896,11 +4898,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4918,11 +4920,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4940,11 +4942,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4962,11 +4964,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -4984,11 +4986,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -5028,7 +5030,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bb19122",
+   "id": "bb09a45b",
    "metadata": {
     "editable": true
    },
@@ -5039,7 +5041,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "baeb1ea3",
+   "id": "6e2566d5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5049,15 +5051,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_57345/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59845/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -5128,7 +5130,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d75dad8e",
+   "id": "eec163df",
    "metadata": {
     "editable": true
    },
@@ -5151,7 +5153,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d55fae28",
+   "id": "b33a66c0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5628,24 +5630,20 @@
       "Learning rate  =  0.1\n",
       "Lambda =  0.001\n",
       "Accuracy score on test set:  0.8722222222222222\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.1\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.9055555555555556\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  0.1\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.8805555555555555\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.8722222222222222\n",
       "\n"
      ]
     },
@@ -5653,14 +5651,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n",
       "Learning rate  =  0.1\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.8666666666666667\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.08611111111111111\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.10555555555555556\n",
@@ -5696,7 +5710,13 @@
       "Learning rate  =  1.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.09444444444444444\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.17222222222222222\n",
@@ -5761,7 +5781,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "276badf8",
+   "id": "e1d6d9d7",
    "metadata": {
     "editable": true
    },
@@ -5772,7 +5792,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "7d6c714e",
+   "id": "4bc9d5c7",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5846,7 +5866,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d45b429",
+   "id": "c1c46aeb",
    "metadata": {
     "editable": true
    },
@@ -5864,7 +5884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67aec670",
+   "id": "b5205123",
    "metadata": {
     "editable": true
    },
@@ -5899,7 +5919,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "0ab38a83",
+   "id": "b9c0dfe3",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5920,7 +5940,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f53f5b9",
+   "id": "f562b8e8",
    "metadata": {
     "editable": true
    },
@@ -5932,7 +5952,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "5190c7ff",
+   "id": "d4526899",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5945,7 +5965,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "676ff9d7",
+   "id": "59617395",
    "metadata": {
     "editable": true
    },
@@ -5956,7 +5976,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "479149f7",
+   "id": "9c975a67",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5969,7 +5989,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62d1b789",
+   "id": "975357be",
    "metadata": {
     "editable": true
    },
@@ -5984,7 +6004,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "4a11035a",
+   "id": "e558fb1f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5996,7 +6016,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abf44b70",
+   "id": "a6f5c4d4",
    "metadata": {
     "editable": true
    },
@@ -6008,7 +6028,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f163559",
+   "id": "6bb12225",
    "metadata": {
     "editable": true
    },
@@ -6021,7 +6041,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "f7418c1e",
+   "id": "41fd6ccf",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -6076,7 +6096,7 @@
   {
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-   "id": "49ec0156",
+   "id": "c70145e7",
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@@ -6105,7 +6125,7 @@
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-   "id": "302ad127",
+   "id": "f0413064",
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@@ -6135,7 +6155,7 @@
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-   "id": "436a3e0a",
+   "id": "a4ba8bc0",
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     "editable": true
@@ -6162,7 +6182,7 @@
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-   "id": "a26e83e0",
+   "id": "e38856a7",
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@@ -6204,7 +6224,7 @@
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   {
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+   "id": "143ff6b2",
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@@ -6215,7 +6235,7 @@
   {
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-   "id": "cad16bbe",
+   "id": "8830114d",
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@@ -6392,7 +6412,7 @@
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   {
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+   "id": "3a018087",
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@@ -6411,7 +6431,7 @@
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   {
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+   "id": "e513a294",
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@@ -6433,7 +6453,7 @@
   {
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    "execution_count": 24,
-   "id": "409b4250",
+   "id": "5a213611",
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@@ -6574,7 +6594,7 @@
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   {
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+   "id": "961917fd",
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@@ -6590,7 +6610,7 @@
   {
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    "execution_count": 25,
-   "id": "041fc0bf",
+   "id": "e3745f70",
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     "editable": true
@@ -6603,7 +6623,7 @@
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   {
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-   "id": "0e18ef84",
+   "id": "22aca85a",
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@@ -6615,7 +6635,7 @@
   {
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    "execution_count": 26,
-   "id": "8be0e7fd",
+   "id": "3c9a6d4a",
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@@ -6637,7 +6657,7 @@
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   {
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-   "id": "2d2bc7a5",
+   "id": "6cea2036",
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@@ -6653,7 +6673,7 @@
   {
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    "execution_count": 27,
-   "id": "f5cb107e",
+   "id": "76f5c3ab",
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     "editable": true
@@ -6691,7 +6711,7 @@
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   {
    "cell_type": "markdown",
-   "id": "beb4f622",
+   "id": "8cbf4208",
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@@ -6704,7 +6724,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "1b508839",
+   "id": "d6e082d7",
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     "editable": true
@@ -6725,7 +6745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afb2e0af",
+   "id": "110f03f8",
    "metadata": {
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@@ -6741,7 +6761,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "b96d6c89",
+   "id": "3c6712b7",
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     "editable": true
@@ -6799,7 +6819,7 @@
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   {
    "cell_type": "markdown",
-   "id": "0be588f8",
+   "id": "498d3949",
    "metadata": {
     "editable": true
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@@ -6814,7 +6834,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "49b8531e",
+   "id": "33947583",
    "metadata": {
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     "editable": true
@@ -6835,7 +6855,7 @@
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   {
    "cell_type": "markdown",
-   "id": "874d306a",
+   "id": "731fc79c",
    "metadata": {
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@@ -6859,7 +6879,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "c25e9955",
+   "id": "f27ea6ab",
    "metadata": {
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     "editable": true
@@ -7331,7 +7351,7 @@
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   {
    "cell_type": "markdown",
-   "id": "86746540",
+   "id": "bf5cdac7",
    "metadata": {
     "editable": true
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@@ -7343,7 +7363,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "6f232f0a",
+   "id": "c57cb644",
    "metadata": {
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     "editable": true
@@ -7387,7 +7407,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7227f21d",
+   "id": "c5864d33",
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@@ -7403,7 +7423,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "944ba89b",
+   "id": "474c34e0",
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     "editable": true
@@ -7418,7 +7438,7 @@
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   {
    "cell_type": "markdown",
-   "id": "3cef110a",
+   "id": "74f3bc91",
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@@ -7429,7 +7449,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "8eb39d5e",
+   "id": "a47d9dc5",
    "metadata": {
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     "editable": true
@@ -7444,7 +7464,7 @@
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   {
    "cell_type": "markdown",
-   "id": "4c0ab092",
+   "id": "bb2d666b",
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     "editable": true
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@@ -7460,7 +7480,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "af77a32b",
+   "id": "f05cdd60",
    "metadata": {
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     "editable": true
@@ -7474,7 +7494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfdb722f",
+   "id": "0034d61c",
    "metadata": {
     "editable": true
    },
@@ -7489,7 +7509,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "d740dbad",
+   "id": "67ecf987",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7515,7 +7535,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "bfb7c28b",
+   "id": "729ba5dd",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7530,7 +7550,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17a8dc93",
+   "id": "719a054a",
    "metadata": {
     "editable": true
    },
@@ -7541,7 +7561,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "7efc0180",
+   "id": "7e58cd70",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7556,7 +7576,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8255133c",
+   "id": "8225cc6d",
    "metadata": {
     "editable": true
    },
@@ -7567,7 +7587,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "4fa47196",
+   "id": "a134deda",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7587,7 +7607,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "b7b5ed9f",
+   "id": "92aeced5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7602,7 +7622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc0fc41e",
+   "id": "3f0373a0",
    "metadata": {
     "editable": true
    },
@@ -7617,7 +7637,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "4ccf32f6",
+   "id": "02888bf9",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7654,7 +7674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "382301fa",
+   "id": "e7714a6d",
    "metadata": {
     "editable": true
    },
@@ -7667,7 +7687,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "0feb8f2a",
+   "id": "08a9206b",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -7690,7 +7710,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f48285b",
+   "id": "82cfd04b",
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diff --git a/doc/LectureNotes/_build/jupyter_execute/week42.py b/doc/LectureNotes/_build/jupyter_execute/week42.py
index 1c2f4c8ea..4136ed2e1 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week42.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week42.py
@@ -16,18 +16,20 @@
 # **Readings and videos.**
 # 
 # 1. These lecture notes
-# 
-# 
 # 
-# 2. For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7.     
+# 2. [Video of lecture](https://youtu.be/7B2F35gNj2Y)
 # 
-# 3. Neural Networks demystified at 
+# 3. [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct14.pdf)
 # 
-# 4. Building Neural Networks from scratch at 
+# 4. For a more in depth discussion on  neural networks we recommend Goodfellow et al chapters 6 and 7.     
 # 
-# 5. Video on Neural Networks at 
+# 5. Neural Networks demystified at 
 # 
-# 6. Video on the back propagation algorithm at 
+# 6. Building Neural Networks from scratch at 
+# 
+# 7. Video on Neural Networks at 
+# 
+# 8. Video on the back propagation algorithm at 
 # 
 # I also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at .