BA-Chatbot/backend/embeddings/embeddings_and_queries.txt

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Query: - Techniken und Methoden, die im Software-Entwicklungsprojekt benötigt werden; die Inhalte werden bei der
Vorbereitung des Projekts festgelegt
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
3.9864e-03, -2.9182e-03],
...,
[ 1.3344e-02, -3.1891e-02, -2.0538e-02, ..., 1.7365e-02,
3.1719e-03, -1.4481e-02],
[-1.7670e-02, 1.6830e-02, 3.0689e-03, ..., 5.4512e-03,
3.4454e-02, 2.6566e-02],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
[-0.1016, -0.0195, -0.0424, ..., 0.0752, -0.0704, -0.0427],
[-0.0588, 0.0121, 0.0111, ..., 0.0136, -0.0375, -0.0212],
...,
[ 0.0069, -0.0474, 0.0185, ..., 0.0232, 0.0413, 0.0427],
[ 0.0816, 0.1115, 0.1430, ..., -0.0607, 0.0830, 0.1156],
[ 0.0183, -0.0082, 0.0021, ..., 0.0596, -0.0076, -0.0128]]])
2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
[-0.0020, -0.1035, -0.0208, ..., 0.1182, 0.0451, 0.0044],
[-0.0366, -0.0052, 0.0223, ..., 0.0163, -0.0730, -0.0328],
...,
[ 0.0602, 0.0203, 0.0476, ..., 0.0709, 0.0783, 0.0415],
[ 0.1830, 0.1692, 0.1009, ..., -0.0515, -0.0148, 0.0804],
[ 0.0604, 0.0021, -0.0018, ..., 0.0542, -0.0041, -0.0052]]])
3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
[ 0.0063, -0.1105, 0.1225, ..., 0.1197, -0.1124, -0.1199],
[ 0.0570, -0.0177, 0.2330, ..., 0.1012, -0.1598, -0.1215],
...,
[-0.0397, 0.0169, 0.1789, ..., 0.0644, -0.0329, 0.1536],
[ 0.3166, 0.0471, 0.1151, ..., -0.0808, -0.0256, 0.0191],
[ 0.0373, -0.0289, 0.0226, ..., 0.0090, -0.0557, -0.0161]]])
4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
[-0.0628, -0.1661, 0.3024, ..., 0.2507, -0.1214, -0.0883],
[ 0.0489, -0.1734, 0.2030, ..., -0.0738, -0.2820, -0.1909],
...,
[-0.2407, 0.1983, 0.1556, ..., 0.2073, -0.1970, 0.0454],
[ 0.0750, 0.1900, 0.0605, ..., -0.0260, -0.0497, -0.2843],
[ 0.0055, 0.0127, -0.0083, ..., -0.0836, -0.1679, 0.0199]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
[ 0.1375, -0.0402, 0.2607, ..., 0.0044, -0.4515, -0.2382],
...,
[-0.3273, 0.1362, 0.1362, ..., 0.3145, -0.3802, 0.1284],
[-0.0397, 0.2875, 0.1660, ..., 0.1444, -0.1528, -0.2161],
[ 0.0332, 0.1080, 0.0324, ..., -0.0941, -0.2196, 0.1084]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
[-0.0033, -0.2674, 0.3796, ..., -0.0058, -0.3464, -0.3838],
...,
[-0.2488, 0.2334, -0.0063, ..., 0.5604, -0.4782, 0.1591],
[ 0.1110, 0.2124, 0.0601, ..., 0.2029, -0.2595, -0.1615],
[ 0.1187, 0.1183, 0.0833, ..., -0.0076, -0.2954, 0.0488]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
[-0.0632, -0.1760, 0.3805, ..., 0.1593, -0.3386, -0.4046],
...,
[-0.2610, -0.0255, -0.0913, ..., 0.5452, -0.5466, 0.3473],
[-0.2042, 0.4231, 0.1344, ..., 0.4989, -0.2612, -0.0187],
[ 0.0449, 0.0876, 0.3704, ..., 0.0883, -0.2708, -0.0775]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
[-0.2766, -0.5257, 0.8512, ..., -0.0022, -0.4838, -0.1323],
[-0.0459, -0.1794, 0.4229, ..., 0.3498, -0.4034, -0.4652],
...,
[-0.4006, -0.0947, -0.1611, ..., 0.5073, -0.5442, 0.4887],
[-0.1380, 0.5674, 0.0034, ..., 0.7479, -0.3426, -0.1500],
[ 0.0524, 0.1872, 0.3028, ..., -0.0106, -0.2520, 0.0743]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
[-1.2754e-01, -7.2784e-01, 6.2181e-01, ..., -5.9251e-04,
-4.6773e-01, -2.0511e-01],
[-1.5388e-01, -3.8383e-01, 4.7796e-01, ..., 5.4677e-01,
-5.7957e-01, -3.6326e-01],
...,
[-4.1465e-01, 2.4621e-02, -6.6401e-03, ..., 4.3440e-01,
-6.7707e-01, 4.2666e-01],
[-7.6348e-02, 5.5552e-01, 2.1516e-01, ..., 4.9354e-01,
-4.4754e-01, -2.8046e-01],
[ 6.9153e-02, -8.2658e-02, 3.1462e-01, ..., 1.5218e-01,
-3.8560e-01, 3.7848e-02]]])
10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
[-0.1813, -0.8617, 0.3325, ..., -0.0257, -0.4533, -0.0284],
[-0.1423, -0.4132, 0.1746, ..., 0.3776, -0.6512, -0.4133],
...,
[-0.3922, 0.0280, -0.2358, ..., 0.4163, -0.8747, 0.2276],
[-0.0279, 0.4694, 0.0340, ..., 0.2409, -0.2828, -0.6227],
[-0.0225, 0.1148, 0.0195, ..., 0.2354, -0.2500, 0.0208]]])
11: tensor([[[ 0.1602, -0.9107, -0.5598, ..., 0.0381, -0.1310, 0.3694],
[-0.2011, -0.5420, 0.2242, ..., -0.2060, -0.5998, 0.2015],
[-0.0183, -0.6304, 0.1346, ..., 0.1680, -0.5393, -0.5965],
...,
[-0.4617, 0.1246, -0.2851, ..., 0.4256, -1.0517, 0.2154],
[ 0.0197, 0.3603, -0.0421, ..., 0.2653, -0.6022, -0.4686],
[ 0.0139, -0.0218, -0.1051, ..., 0.5144, -0.1847, 0.0323]]])
12: tensor([[[ 0.1804, -0.9203, -0.5095, ..., -0.0744, -0.1174, 0.3818],
[-0.4338, -0.7381, 0.0850, ..., -0.2924, -0.6647, 0.2480],
[-0.2238, -0.8725, 0.1604, ..., 0.4101, -0.5698, -0.4751],
...,
[-0.2974, -0.0147, -0.0945, ..., 0.4220, -1.0275, 0.5405],
[ 0.0864, 0.0296, -0.1722, ..., 0.4326, -0.8750, -0.3819],
[ 0.0966, -0.4393, -0.3462, ..., 0.6157, -0.2707, -0.2134]]])
13: tensor([[[ 0.2579, -1.0105, -0.5391, ..., -0.0350, -0.2018, 0.4527],
[-0.6401, -0.7804, 0.0014, ..., 0.1250, -0.3100, 0.1285],
[-0.0604, -0.9795, 0.4270, ..., 0.1772, -0.6683, -0.4837],
...,
[-0.2119, -0.0020, -0.2063, ..., 0.4648, -1.1749, 0.7726],
[-0.1647, -0.3506, -0.2941, ..., 0.3934, -0.8014, -0.2225],
[ 0.2150, -0.4449, -0.1738, ..., 0.6455, -0.1058, -0.2824]]])
14: tensor([[[ 0.2787, -1.0211, -0.4858, ..., -0.0717, -0.0655, 0.3964],
[-0.6753, -0.9677, 0.0407, ..., -0.0744, -0.4973, -0.2153],
[-0.2201, -0.9099, 0.2618, ..., 0.0895, -0.6634, -0.9823],
...,
[ 0.2662, 0.3558, 0.4173, ..., 0.2606, -1.0965, 0.9151],
[ 0.1518, -0.4462, 0.1005, ..., -0.1489, -0.8307, -0.6411],
[ 0.4775, -0.1171, -0.1291, ..., 0.4147, -0.0842, -0.1730]]])
15: tensor([[[ 0.2775, -1.0251, -0.2647, ..., -0.0380, -0.0748, 0.5438],
[-0.2879, -1.1037, 0.4039, ..., -0.3520, -0.6702, -0.3490],
[ 0.0341, -1.1546, 0.3763, ..., -0.3480, -0.8019, -0.7203],
...,
[ 0.1769, 0.7832, 0.6265, ..., 0.5291, -0.9287, 0.6309],
[ 0.1636, -0.1559, -0.3874, ..., -0.4736, -0.2265, -1.0749],
[ 0.5978, -0.2628, -1.0861, ..., 0.1943, 0.1261, -0.1810]]])
16: tensor([[[ 0.3045, -1.0667, -0.2115, ..., -0.0522, -0.1122, 0.5783],
[-0.1683, -1.4639, 0.4694, ..., -0.1146, -0.9781, -0.5914],
[ 0.1871, -1.3984, 0.1202, ..., -0.1305, -0.6274, -0.8889],
...,
[ 0.1171, 0.8641, 0.6190, ..., 0.1882, -1.1480, 0.9628],
[-0.0776, -0.2368, -0.7555, ..., -0.6507, -0.4964, -0.3102],
[ 0.8497, -0.6779, -1.2999, ..., 0.5633, -0.1490, 0.3697]]])
17: tensor([[[ 0.3259, -1.0477, -0.2252, ..., 0.0335, -0.2652, 0.5971],
[-0.2339, -1.5909, 0.4839, ..., 0.0959, -1.0865, -0.6412],
[ 0.1523, -1.3746, -0.0511, ..., -0.2632, -0.5859, -0.7240],
...,
[-0.3071, 1.1338, 0.1763, ..., -0.0021, -0.8948, 0.7020],
[ 0.0527, -0.3037, -0.9342, ..., -0.8846, -0.0652, -0.3108],
[ 1.0284, -0.1242, -0.8037, ..., 0.6987, -0.4724, 0.4918]]])
18: tensor([[[ 0.3030, -0.9936, -0.3282, ..., -0.0327, -0.3823, 0.7322],
[-0.1053, -1.5004, 0.2582, ..., -0.0130, -1.5225, -1.0915],
[ 0.3871, -1.3789, -0.0732, ..., 0.1195, -0.8012, -0.9241],
...,
[ 0.0681, 0.8117, 0.5805, ..., -0.0528, -0.9058, 0.5053],
[-0.0049, -0.5633, -0.9521, ..., -0.8563, 0.2055, -0.4072],
[ 0.8227, -0.3342, -1.1913, ..., 0.9603, -0.5597, 0.1812]]])
19: tensor([[[ 0.3025, -1.2240, -0.1138, ..., 0.1373, -0.4463, 0.8681],
[ 0.3981, -1.5456, 0.3258, ..., -0.1875, -1.6000, -1.1540],
[ 0.4815, -1.4289, -0.0317, ..., -0.0071, -1.0063, -1.0037],
...,
[-0.2657, 1.2706, 0.8727, ..., -0.0221, -1.1589, 0.5560],
[-0.2300, -0.4623, -1.2880, ..., -0.8934, 0.3851, -0.6455],
[ 0.9547, -0.2643, -1.5496, ..., 1.0177, -0.5803, 0.0225]]])
20: tensor([[[ 0.4686, -1.2657, -0.0419, ..., 0.0919, -0.3436, 0.8809],
[ 0.3482, -1.9316, -0.2961, ..., 0.0607, -1.7172, -0.8600],
[ 0.4599, -1.7459, -0.2516, ..., -0.1474, -1.0419, -0.7416],
...,
[ 0.1391, 0.8728, 1.1317, ..., -0.0228, -1.5161, 1.4338],
[-0.0134, -0.2095, -0.7583, ..., -1.7094, 0.5942, 0.1780],
[ 1.6806, -0.2370, -1.5551, ..., 0.6167, -0.5077, 0.1490]]])
21: tensor([[[ 0.4442, -1.0962, 0.0778, ..., 0.0597, -0.3985, 0.7814],
[ 0.0725, -1.7982, -0.3599, ..., -0.1201, -1.6566, -1.1994],
[ 0.3863, -1.8144, 0.2474, ..., 0.0598, -1.3684, -0.8320],
...,
[ 0.3636, 1.3562, 0.9140, ..., -0.2457, -1.8213, 1.1480],
[-0.2631, -0.3801, -0.9859, ..., -1.8367, 1.1390, -0.1739],
[ 1.8709, -0.2693, -1.4116, ..., 0.7908, -0.7744, 0.0987]]])
22: tensor([[[ 0.5059, -1.1660, 0.2683, ..., -0.0197, -0.3653, 0.8172],
[ 0.4792, -2.1132, 0.1638, ..., -0.0667, -2.0503, -0.8644],
[ 0.2983, -2.1457, 0.5011, ..., -0.4051, -1.3177, -0.6711],
...,
[ 0.0582, 1.8533, 1.1053, ..., -0.6796, -1.9256, 1.5382],
[-0.6789, -0.2645, -0.5712, ..., -2.0945, 1.3588, 0.2196],
[ 1.6458, 0.0501, -0.9480, ..., 0.3314, -1.0105, 1.4925]]])
23: tensor([[[ 0.4414, -1.2350, 0.1653, ..., 0.0749, -0.5487, 0.5796],
[ 0.9800, -2.0098, 0.3139, ..., -0.1216, -2.4571, -0.1834],
[ 0.1384, -2.1878, 0.6559, ..., -0.4342, -1.4843, -0.3502],
...,
[ 0.0449, 2.1695, 0.3033, ..., -1.3733, -1.7934, 1.6283],
[-0.4737, 0.1576, 0.1266, ..., -2.8182, 1.6551, 0.2925],
[ 1.0856, 0.5691, -1.1310, ..., 0.8345, -1.4854, 1.2304]]])
24: tensor([[[ 0.4183, -1.1111, 0.1459, ..., 0.0492, -0.4547, 0.3954],
[ 0.5153, -1.9330, 0.0819, ..., -0.2930, -2.4385, 0.1704],
[ 0.1086, -1.9690, 0.3744, ..., -0.5352, -1.3599, -0.1157],
...,
[ 0.2623, 2.4740, -0.1216, ..., -1.4487, -1.5271, 2.2617],
[-0.0153, 0.3395, 0.3477, ..., -3.3036, 2.1221, 0.1499],
[ 0.6683, 0.1911, -0.9326, ..., 1.0319, -1.7107, 1.5540]]])
25: tensor([[[ 0.4975, -1.1003, 0.2274, ..., 0.1581, -0.4560, 0.4482],
[ 0.4099, -2.1553, -0.0299, ..., -0.4242, -2.9810, 0.1285],
[ 0.1489, -1.9066, 0.3926, ..., -1.2392, -1.9434, -0.0387],
...,
[ 0.1804, 1.8328, 0.2764, ..., -0.6864, -1.6672, 1.9865],
[-0.1213, 0.4279, -0.2065, ..., -3.3918, 2.4159, 0.3501],
[ 1.0494, -0.4176, -1.8091, ..., 0.9949, -1.6156, 1.7068]]])
26: tensor([[[ 0.4632, -1.1296, 0.2315, ..., 0.1767, -0.4738, 0.3622],
[ 0.3349, -2.0610, -0.2346, ..., -0.4175, -3.3164, 0.6413],
[ 0.3103, -2.0197, 0.5268, ..., -1.1227, -2.0411, -0.1146],
...,
[-0.2680, 1.1608, 0.4236, ..., -0.6514, -1.8459, 1.7907],
[-0.2799, 0.0400, -0.5889, ..., -3.2905, 2.6490, 0.6026],
[ 0.7612, -0.8879, -2.2381, ..., 0.8111, -2.1893, 1.6725]]])
27: tensor([[[ 0.5150, -1.0300, 0.1991, ..., 0.2264, -0.4491, 0.3086],
[ 0.2726, -1.9265, -0.1144, ..., -0.5303, -3.5600, 0.5535],
[ 0.5457, -2.0804, 0.5186, ..., -1.0915, -1.9407, 0.1254],
...,
[-0.8516, 0.0432, -0.4162, ..., -1.6667, -1.9084, 1.4546],
[-0.2662, 0.5765, -1.5117, ..., -4.1444, 3.4839, 0.1810],
[ 0.6355, -0.9301, -1.8775, ..., 1.3038, -2.0038, 2.1196]]])
28: tensor([[[ 0.5767, -0.9113, 0.1827, ..., 0.2441, -0.5849, 0.2632],
[ 0.4596, -2.2676, 0.0314, ..., -0.7782, -3.3262, 0.7418],
[ 0.3451, -2.3089, 0.6245, ..., -0.9180, -1.8190, 0.1070],
...,
[ 0.1404, -0.1801, 0.2396, ..., -2.2094, -1.2745, 0.8863],
[-0.1647, 0.1582, -1.3789, ..., -3.7638, 3.3349, 0.2131],
[ 0.9402, -1.4585, -2.2445, ..., 1.3561, -1.7883, 2.1691]]])
29: tensor([[[ 0.5881, -0.8365, 0.1993, ..., 0.2294, -0.7033, 0.1812],
[ 0.8169, -2.5907, 0.3456, ..., -0.9426, -3.3235, 0.5329],
[ 0.5339, -2.1013, 0.5908, ..., -1.0304, -1.7530, 0.1138],
...,
[-0.4074, 0.2659, -0.0707, ..., -2.8490, -1.8629, -0.1232],
[-0.3013, -0.3708, -1.4948, ..., -4.4454, 2.8337, 0.4441],
[ 0.6673, -1.7998, -1.5496, ..., 2.2153, -2.6104, 1.7706]]])
30: tensor([[[ 6.6669e-01, -9.4694e-01, 2.0135e-01, ..., 1.9567e-01,
-7.3406e-01, 1.7867e-01],
[ 8.2389e-01, -2.3207e+00, 6.9412e-02, ..., -7.6211e-01,
-3.7438e+00, 4.4429e-01],
[ 5.5186e-01, -1.9823e+00, 4.1869e-01, ..., -8.9906e-01,
-1.8399e+00, 4.5765e-03],
...,
[-3.0823e-01, -7.0586e-01, -7.3340e-01, ..., -3.7164e+00,
-2.1035e+00, -5.1496e-01],
[ 8.3611e-02, -9.4942e-02, -1.4281e+00, ..., -4.7363e+00,
3.0231e+00, 1.3855e+00],
[ 5.6952e-01, -1.4873e+00, -1.5706e+00, ..., 2.5254e+00,
-2.5297e+00, 1.9802e+00]]])
31: tensor([[[ 0.6655, -0.9598, 0.2447, ..., 0.1706, -0.7776, 0.1788],
[ 0.6942, -2.3152, 0.0467, ..., -1.0002, -4.1053, 0.4592],
[ 0.4674, -1.8792, 0.4066, ..., -1.1411, -1.8999, 0.0861],
...,
[-0.8924, -0.0837, -0.0896, ..., -4.3938, -2.3015, 0.5705],
[ 0.2337, -0.0359, -0.9970, ..., -5.3283, 3.3119, 2.0051],
[ 0.7952, -0.9898, -1.5249, ..., 1.5161, -2.2549, 2.0610]]])
32: tensor([[[ 0.7360, -0.9609, 0.2117, ..., 0.1602, -0.8576, 0.1393],
[ 0.6757, -2.3709, -0.2111, ..., -1.0661, -3.9552, 0.4456],
[ 0.4939, -2.4154, 0.0644, ..., -1.1614, -1.8203, 0.1791],
...,
[-1.2926, -0.4081, 0.5557, ..., -5.3154, -2.3396, -0.3690],
[-0.5728, 0.3703, -1.6873, ..., -6.1867, 3.2846, 2.3667],
[ 0.3572, -1.4811, -1.6651, ..., 1.2623, -2.2308, 1.7896]]])
33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
[ 0.6746, -2.3491, -0.5023, ..., -1.1778, -4.0825, 0.6369],
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...,
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35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
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36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
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37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
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38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
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39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
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40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
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Query: - Verständnis und Analyse eines gestellten Problems, einer Aufgabenstellung
- Ermittlung des Stands der Technik
- Entwicklung eines Konzeptes zur Lösung des Problems
- Durchführung und Validierung der Lösung
- Selbstständiges wissenschaftliches Arbeiten
- Erkennen sich ergebender zukünftiger Ansätze
- Verfassen einer wissenschaftlichen Abschlussarbeit
- Ergebnispräsentation
Embeddings:
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[-0.9848, -0.3770, 1.3044, ..., 2.0763, -2.3734, -0.4867]]])
25: tensor([[[ 0.4975, -1.1003, 0.2274, ..., 0.1581, -0.4560, 0.4482],
[ 0.4099, -2.1553, -0.0299, ..., -0.4242, -2.9810, 0.1285],
[ 0.1489, -1.9066, 0.3926, ..., -1.2392, -1.9434, -0.0387],
...,
[ 1.5994, 1.7823, -1.0906, ..., -0.0412, 1.7821, -0.8750],
[ 2.0812, 2.1172, 1.2566, ..., -4.8743, 1.9421, -3.0682],
[-1.0039, 0.0262, 1.4275, ..., 1.7576, -2.5377, -1.3906]]])
26: tensor([[[ 0.4632, -1.1296, 0.2315, ..., 0.1767, -0.4738, 0.3622],
[ 0.3349, -2.0610, -0.2346, ..., -0.4175, -3.3164, 0.6413],
[ 0.3103, -2.0197, 0.5268, ..., -1.1227, -2.0411, -0.1146],
...,
[ 1.4366, 1.7409, -1.6487, ..., 0.2517, 2.4316, -1.1706],
[ 2.2657, 2.0542, 1.8759, ..., -5.5952, 2.0317, -2.8473],
[-0.8136, 0.3107, 1.2181, ..., 1.8692, -2.7522, -0.7841]]])
27: tensor([[[ 5.1504e-01, -1.0300e+00, 1.9909e-01, ..., 2.2639e-01,
-4.4911e-01, 3.0858e-01],
[ 2.7256e-01, -1.9265e+00, -1.1439e-01, ..., -5.3034e-01,
-3.5600e+00, 5.5347e-01],
[ 5.4575e-01, -2.0804e+00, 5.1859e-01, ..., -1.0915e+00,
-1.9407e+00, 1.2542e-01],
...,
[ 8.5030e-01, 3.1977e+00, -1.7743e+00, ..., 1.1824e-02,
2.3568e+00, -1.1448e+00],
[ 2.1957e+00, 2.1934e+00, 7.5490e-01, ..., -5.4935e+00,
3.9611e+00, -2.5368e+00],
[-8.9509e-01, -2.8564e-03, 1.7505e+00, ..., 2.3737e+00,
-1.6607e+00, -9.2997e-01]]])
28: tensor([[[ 0.5767, -0.9113, 0.1827, ..., 0.2441, -0.5849, 0.2632],
[ 0.4596, -2.2676, 0.0314, ..., -0.7782, -3.3262, 0.7418],
[ 0.3451, -2.3089, 0.6245, ..., -0.9180, -1.8190, 0.1070],
...,
[ 1.1502, 3.8739, -1.4079, ..., -0.9804, 1.9150, -1.1962],
[ 3.3740, 2.2386, 1.0136, ..., -5.6095, 3.4038, -2.3766],
[ 0.0475, 0.2609, 2.4953, ..., 1.9548, -1.9047, -1.1826]]])
29: tensor([[[ 0.5881, -0.8365, 0.1993, ..., 0.2294, -0.7033, 0.1812],
[ 0.8169, -2.5907, 0.3456, ..., -0.9426, -3.3235, 0.5329],
[ 0.5339, -2.1013, 0.5908, ..., -1.0304, -1.7530, 0.1138],
...,
[ 0.6981, 2.3410, -1.0693, ..., -1.9235, 1.8836, -1.1859],
[ 3.7063, 1.7808, 1.5905, ..., -5.4703, 4.0453, -1.7472],
[-0.2172, 0.2488, 1.8600, ..., 2.7203, -3.3375, -1.8443]]])
30: tensor([[[ 6.6669e-01, -9.4694e-01, 2.0135e-01, ..., 1.9567e-01,
-7.3406e-01, 1.7867e-01],
[ 8.2389e-01, -2.3207e+00, 6.9412e-02, ..., -7.6211e-01,
-3.7438e+00, 4.4429e-01],
[ 5.5186e-01, -1.9823e+00, 4.1869e-01, ..., -8.9906e-01,
-1.8399e+00, 4.5765e-03],
...,
[ 5.2489e-01, 2.9373e+00, -9.4187e-01, ..., -2.8495e+00,
2.1334e+00, -7.2000e-01],
[ 4.2761e+00, 2.3016e+00, 1.6153e+00, ..., -5.2009e+00,
3.8475e+00, -1.6408e+00],
[ 4.0766e-03, 9.8916e-01, 2.0998e+00, ..., 3.2752e+00,
-3.5885e+00, -2.1785e+00]]])
31: tensor([[[ 0.6655, -0.9598, 0.2447, ..., 0.1706, -0.7776, 0.1788],
[ 0.6942, -2.3152, 0.0467, ..., -1.0002, -4.1053, 0.4592],
[ 0.4674, -1.8792, 0.4066, ..., -1.1411, -1.8999, 0.0861],
...,
[ 0.7604, 1.8132, 0.0113, ..., -3.3304, 1.4213, -1.0019],
[ 5.8615, 2.5278, 2.5118, ..., -5.5414, 4.1597, -2.1254],
[-0.1166, 1.5274, 3.2274, ..., 2.5461, -3.2624, -1.6560]]])
32: tensor([[[ 0.7360, -0.9609, 0.2117, ..., 0.1602, -0.8576, 0.1393],
[ 0.6757, -2.3709, -0.2111, ..., -1.0661, -3.9552, 0.4456],
[ 0.4939, -2.4154, 0.0644, ..., -1.1614, -1.8203, 0.1791],
...,
[-0.3819, 2.2189, -0.5029, ..., -3.8352, 2.4015, 0.1645],
[ 6.3849, 2.0266, 2.3206, ..., -6.0530, 4.1536, -2.0358],
[-0.8091, 1.0662, 3.0726, ..., 2.0688, -3.2840, -2.1161]]])
33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
[ 0.6746, -2.3491, -0.5023, ..., -1.1778, -4.0825, 0.6369],
[ 0.3326, -2.5822, -0.0435, ..., -0.7961, -1.9972, 0.2151],
...,
[-1.5870, 1.7790, -0.8055, ..., -3.8901, 2.0608, 0.2605],
[ 6.3158, 2.6035, 3.1467, ..., -5.9900, 4.0705, -1.5381],
[-0.7545, 2.0721, 2.9061, ..., 2.5039, -3.7658, -1.1047]]])
34: tensor([[[ 0.8220, -0.9002, 0.5914, ..., 0.1699, -0.9568, -0.2156],
[ 0.8671, -2.4411, -0.2519, ..., -1.0163, -3.7759, 0.2840],
[ 0.7680, -2.6552, 0.3849, ..., -0.8292, -1.9769, 0.5669],
...,
[-1.7486, 1.2801, -0.6162, ..., -4.0939, 2.7502, 0.0691],
[ 6.9290, 2.0857, 3.6434, ..., -6.5145, 4.2774, -1.1752],
[-0.7339, 2.0726, 3.6752, ..., 1.7470, -3.6093, -0.4465]]])
35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
[ 1.4832, -2.1899, -0.7593, ..., -0.9099, -4.0092, -0.0704],
[ 1.1589, -2.4299, -0.0361, ..., -0.9148, -2.3421, 0.3966],
...,
[-1.5190, 0.5623, -0.8457, ..., -2.9911, 2.9899, -0.9417],
[ 7.9691, 0.8393, 3.3738, ..., -6.9073, 3.4076, -1.8653],
[-0.9707, 2.2637, 3.7645, ..., 1.8704, -3.0269, -1.7287]]])
36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
[ 1.8548, -2.4676, -0.7095, ..., -1.1532, -4.4559, -0.9997],
[ 1.2417, -1.9971, 0.1568, ..., -1.1543, -2.6090, 0.1292],
...,
[ 0.0951, -0.7822, 0.0112, ..., -2.5569, 2.0859, -0.1043],
[ 9.5563, 0.0207, 2.5763, ..., -6.1449, 4.3405, -2.5937],
[-0.8976, 1.6794, 4.0731, ..., 1.8662, -3.4456, -2.7742]]])
37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
[ 1.0674, -1.5709, 0.4484, ..., -2.0207, -2.4859, 0.1802],
...,
[ 0.0254, -0.8098, -0.1154, ..., -3.5461, 2.0185, 0.1540],
[10.2479, 0.2402, 3.7616, ..., -7.0593, 4.4061, -1.5158],
[-0.6306, 1.2509, 4.0843, ..., 1.6163, -3.2640, -3.1731]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
[ 0.5371, -1.7457, 0.3418, ..., -1.6540, -2.1705, -0.7345],
...,
[ 0.7655, -0.5426, -0.5697, ..., -3.1507, 2.3042, -0.6479],
[11.4769, 0.5629, 2.8950, ..., -7.5464, 4.5938, -4.3534],
[ 0.0510, 2.5967, 4.1032, ..., 0.2554, -2.5422, -3.8077]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[ 1.6754, -1.3056, -0.2261, ..., -1.8542, 1.3269, -0.5555],
[13.7086, 1.1704, 4.3781, ..., -5.9798, 2.0960, -4.2648],
[ 0.4311, 3.0621, 6.9154, ..., 0.4086, -4.5138, -5.3659]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
[-0.2762, 0.5192, -1.0364, ..., -0.4324, -0.5417, 0.2538],
...,
[ 0.4700, -0.7851, -0.2677, ..., 0.3497, 0.8756, 0.4218],
[ 1.9042, -0.2846, -0.3508, ..., -1.1321, -0.2664, -0.9701],
[-0.3948, 0.1319, 0.5750, ..., -0.2891, -1.1615, -1.0425]]])
Query: - Relationale Datenbanken
- Datenmodelle (z. B. E/R-Diagramme) und Datenmodellierung
- Relationenalgebra
- Datenbankentwurf
- Structured Query Language (SQL) und deren Grundlagen
- NoSQL und BigData
- Anbindung von Datenbanken an moderne (web-basierte) Anwendungen über JDBC und JSON
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
3.9864e-03, -2.9182e-03],
...,
[ 5.5122e-03, 6.7215e-03, 2.3003e-03, ..., -1.0880e-02,
-2.6340e-03, -1.4328e-02],
[ 2.7878e-02, -9.0790e-03, -1.1391e-02, ..., 2.7222e-02,
-6.9275e-03, -2.2903e-02],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
[-0.1016, -0.0195, -0.0424, ..., 0.0752, -0.0704, -0.0427],
[-0.0588, 0.0121, 0.0111, ..., 0.0136, -0.0375, -0.0212],
...,
[ 0.0015, -0.0747, 0.0359, ..., -0.1004, -0.0330, -0.0060],
[-0.0237, 0.0285, 0.0171, ..., 0.0665, -0.0418, 0.0366],
[-0.0063, -0.0289, -0.0320, ..., 0.0374, -0.0205, -0.0215]]])
2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
[-0.0020, -0.1035, -0.0208, ..., 0.1182, 0.0451, 0.0044],
[-0.0366, -0.0052, 0.0223, ..., 0.0163, -0.0730, -0.0328],
...,
[-0.0472, -0.0774, -0.0085, ..., -0.1084, -0.0688, -0.0304],
[ 0.0343, 0.0870, 0.0976, ..., 0.1690, -0.0947, 0.0121],
[ 0.0344, -0.0187, -0.0346, ..., 0.0261, -0.0007, 0.0079]]])
3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
[ 0.0063, -0.1105, 0.1225, ..., 0.1197, -0.1124, -0.1199],
[ 0.0570, -0.0177, 0.2330, ..., 0.1012, -0.1598, -0.1215],
...,
[-0.0193, -0.0925, 0.0298, ..., -0.1330, -0.1234, -0.1114],
[ 0.0761, 0.0498, 0.0784, ..., 0.1884, -0.0599, 0.0009],
[ 0.0304, -0.0581, -0.0274, ..., -0.0046, 0.0068, -0.0025]]])
4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
[-0.0628, -0.1661, 0.3024, ..., 0.2507, -0.1214, -0.0883],
[ 0.0489, -0.1734, 0.2030, ..., -0.0738, -0.2820, -0.1909],
...,
[ 0.0422, -0.0467, -0.0098, ..., -0.1678, -0.0915, -0.1111],
[ 0.0491, 0.1576, 0.0960, ..., 0.1045, -0.0105, 0.1167],
[ 0.0258, -0.0556, -0.0430, ..., -0.0067, -0.0199, 0.0921]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
[ 0.1375, -0.0402, 0.2607, ..., 0.0044, -0.4515, -0.2382],
...,
[ 0.0154, -0.1365, -0.1646, ..., -0.2530, -0.0658, -0.0097],
[ 0.0427, 0.3213, -0.0076, ..., 0.2521, 0.0872, 0.1961],
[ 0.1492, -0.2047, -0.0898, ..., -0.0336, -0.0370, 0.1421]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
[-0.0033, -0.2674, 0.3796, ..., -0.0058, -0.3464, -0.3838],
...,
[-0.0566, -0.0425, -0.2790, ..., -0.2126, 0.1505, -0.0080],
[-0.0664, 0.2760, -0.0018, ..., 0.2970, 0.1859, -0.1387],
[ 0.2535, -0.0729, -0.2175, ..., -0.0067, 0.0611, 0.1607]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
[-0.0632, -0.1760, 0.3805, ..., 0.1593, -0.3386, -0.4046],
...,
[ 0.0831, 0.0078, 0.0284, ..., -0.1483, 0.1970, 0.0564],
[-0.2017, 0.3514, 0.2186, ..., 0.1155, 0.2340, -0.2349],
[ 0.3577, -0.0745, -0.0257, ..., 0.0092, 0.0657, 0.2289]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
[-0.2766, -0.5257, 0.8512, ..., -0.0022, -0.4838, -0.1323],
[-0.0459, -0.1794, 0.4229, ..., 0.3498, -0.4034, -0.4652],
...,
[ 0.3947, -0.1095, -0.0813, ..., -0.0743, 0.0890, 0.0270],
[ 0.0226, 0.3638, -0.1021, ..., 0.1572, 0.0351, -0.3567],
[ 0.6275, -0.2691, -0.0975, ..., 0.0202, -0.1592, 0.2733]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
[-1.2754e-01, -7.2784e-01, 6.2181e-01, ..., -5.9251e-04,
-4.6773e-01, -2.0511e-01],
[-1.5388e-01, -3.8383e-01, 4.7796e-01, ..., 5.4677e-01,
-5.7957e-01, -3.6326e-01],
...,
[ 3.6271e-01, 1.2119e-01, -2.7959e-01, ..., -2.0293e-01,
2.1802e-01, -8.5566e-02],
[ 1.0840e-02, 3.7504e-01, -1.1996e-01, ..., 3.3768e-01,
-2.4579e-03, -5.0539e-01],
[ 4.0429e-01, -2.2633e-01, -1.5222e-01, ..., -2.7472e-02,
7.3961e-02, 6.8930e-01]]])
10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
[-0.1813, -0.8617, 0.3325, ..., -0.0257, -0.4533, -0.0284],
[-0.1423, -0.4132, 0.1746, ..., 0.3776, -0.6512, -0.4133],
...,
[ 0.5063, -0.1321, -0.5802, ..., -0.0460, 0.3617, 0.0255],
[-0.1473, -0.0551, -0.2273, ..., 0.5800, -0.3334, -0.1116],
[ 0.4060, -0.2487, -0.3703, ..., 0.3153, -0.1058, 0.8158]]])
11: tensor([[[ 0.1602, -0.9107, -0.5598, ..., 0.0381, -0.1310, 0.3694],
[-0.2011, -0.5420, 0.2242, ..., -0.2060, -0.5998, 0.2015],
[-0.0183, -0.6304, 0.1346, ..., 0.1680, -0.5393, -0.5965],
...,
[ 0.3111, -0.1380, -0.5345, ..., 0.1779, 0.4709, -0.0219],
[-0.4888, -0.0558, -0.2755, ..., 0.3622, -0.2475, 0.0628],
[ 0.2561, -0.0532, -0.4510, ..., 0.2544, -0.1149, 0.8719]]])
12: tensor([[[ 0.1804, -0.9203, -0.5095, ..., -0.0744, -0.1174, 0.3818],
[-0.4338, -0.7381, 0.0850, ..., -0.2924, -0.6647, 0.2480],
[-0.2238, -0.8725, 0.1604, ..., 0.4101, -0.5698, -0.4751],
...,
[ 0.2311, -0.3620, -0.6903, ..., 0.3850, 0.4025, -0.0670],
[-0.0727, 0.1431, -0.1661, ..., 0.5706, -0.6223, -0.0953],
[ 0.4716, -0.3997, -0.2679, ..., 0.4808, -0.4622, 0.6338]]])
13: tensor([[[ 0.2579, -1.0105, -0.5391, ..., -0.0350, -0.2018, 0.4527],
[-0.6401, -0.7804, 0.0014, ..., 0.1250, -0.3100, 0.1285],
[-0.0604, -0.9795, 0.4270, ..., 0.1772, -0.6683, -0.4837],
...,
[-0.0859, -0.4541, -0.7863, ..., 0.6186, 0.1252, -0.4191],
[-0.3356, -0.1839, -0.1720, ..., 0.9124, -0.7675, -0.5880],
[ 0.3265, -0.2299, -0.3092, ..., 0.3233, -0.8572, 0.7809]]])
14: tensor([[[ 0.2787, -1.0211, -0.4858, ..., -0.0717, -0.0655, 0.3964],
[-0.6753, -0.9677, 0.0407, ..., -0.0744, -0.4973, -0.2153],
[-0.2201, -0.9099, 0.2618, ..., 0.0895, -0.6634, -0.9823],
...,
[ 0.3085, -0.5019, -0.4538, ..., 0.4254, -0.0192, -0.3304],
[-0.4167, -0.3514, -0.3005, ..., 0.6883, -0.6932, -0.6715],
[ 0.6593, -0.4328, -0.0781, ..., 0.2429, -0.7662, 0.4267]]])
15: tensor([[[ 0.2775, -1.0251, -0.2647, ..., -0.0380, -0.0748, 0.5438],
[-0.2879, -1.1037, 0.4039, ..., -0.3520, -0.6702, -0.3490],
[ 0.0341, -1.1546, 0.3763, ..., -0.3480, -0.8019, -0.7203],
...,
[-0.1409, -0.3259, -0.3606, ..., 0.5684, 0.0154, -0.5155],
[-0.2114, -0.1444, -0.2275, ..., 0.8932, -0.7446, -0.5262],
[ 0.7949, -0.5304, -0.6832, ..., 0.5363, -1.0480, 0.2443]]])
16: tensor([[[ 0.3045, -1.0667, -0.2115, ..., -0.0522, -0.1122, 0.5783],
[-0.1683, -1.4639, 0.4694, ..., -0.1146, -0.9781, -0.5914],
[ 0.1871, -1.3984, 0.1202, ..., -0.1305, -0.6274, -0.8889],
...,
[-0.0336, -1.0119, -0.1057, ..., 0.3325, -0.0408, -0.6378],
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19: tensor([[[ 0.3025, -1.2240, -0.1138, ..., 0.1373, -0.4463, 0.8681],
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20: tensor([[[ 0.4686, -1.2657, -0.0419, ..., 0.0919, -0.3436, 0.8809],
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23: tensor([[[ 0.4414, -1.2350, 0.1653, ..., 0.0749, -0.5487, 0.5796],
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27: tensor([[[ 0.5150, -1.0300, 0.1991, ..., 0.2264, -0.4491, 0.3086],
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28: tensor([[[ 0.5767, -0.9113, 0.1827, ..., 0.2441, -0.5849, 0.2632],
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29: tensor([[[ 0.5881, -0.8365, 0.1993, ..., 0.2294, -0.7033, 0.1812],
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30: tensor([[[ 6.6669e-01, -9.4694e-01, 2.0135e-01, ..., 1.9567e-01,
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31: tensor([[[ 0.6655, -0.9598, 0.2447, ..., 0.1706, -0.7776, 0.1788],
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33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
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35: tensor([[[ 8.0000e-01, -7.8714e-01, 4.1892e-01, ..., 1.2866e-01,
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36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
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37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
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38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
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[ 0.1865, -0.9694, 4.0658, ..., -2.8617, 2.8727, -2.0334]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
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[ 2.6634, 1.0326, 1.4465, ..., 2.5672, 5.1083, -0.5765],
[ 1.1087, -1.4600, 5.5458, ..., -2.2555, 1.7531, -3.5845]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
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...,
[ 0.2815, -1.4711, -0.7818, ..., -0.4549, 0.0441, 0.6582],
[ 0.4560, 0.5943, -0.8763, ..., 0.4542, 2.0255, 0.0663],
[-0.2933, -0.6003, 0.3018, ..., -0.7392, -0.2702, -1.0215]]])
Query: - Überblick über das Gebiet der Informatik (Aufgaben, Teilbereiche, Tätigkeiten. . . )
- Geschichte der Informatik
- Grundlagen zu Rechnerarchitekturen (Komponenten des Rechners: RAM, ROM, CPU, . . . )
- Zahlensysteme und Kodierung
- Betriebssysteme (Einführung), Kommandozeile (Shell), Graphische Oberflächen
- Rechnernetze und Internet (Einführung)
- Prinzipien IT-Sicherheit (Einführung)
- Aktuelle und zukünftigen Herausforderungen der IT (Mobile Systeme, IOT, Cloud Computing, Big Data Ana-
lysis, . . . )
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
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...,
[-1.7729e-03, 6.2227e-04, 4.5300e-04, ..., -7.2632e-03,
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[-3.6896e-02, 4.3030e-03, 9.6083e-04, ..., 3.0640e-02,
8.6365e-03, 2.9755e-02],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
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2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
[-0.0020, -0.1035, -0.0208, ..., 0.1182, 0.0451, 0.0044],
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3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
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4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
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[ 0.0671, -0.1255, 0.0174, ..., 0.0219, -0.0828, 0.1891]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
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[ 0.1654, -0.1895, 0.0222, ..., -0.0970, -0.0705, 0.3589]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
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[ 0.1984, -0.1533, 0.1190, ..., -0.0493, -0.0516, 0.3498]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
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[ 0.1085, -0.1244, 0.4269, ..., 0.1036, -0.1614, 0.3859]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
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Query: - English for meetings and negotiation
- Business English
- Writing technical reports and project documentation in English
- Giving presentations and presenting project results in English
- Small talk in English
Embeddings:
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[ 3.0817, -2.1391, -1.2149, ..., 0.3329, -3.0375, 3.8526]]])
35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
[ 1.4832, -2.1899, -0.7593, ..., -0.9099, -4.0092, -0.0704],
[ 1.1589, -2.4299, -0.0361, ..., -0.9148, -2.3421, 0.3966],
...,
[ 5.7402, 2.5022, -1.5296, ..., -2.7095, -2.3954, -1.3681],
[ 5.0700, -3.3659, -1.9134, ..., -2.8867, -2.6195, -1.9273],
[ 2.6516, -2.8117, -1.6265, ..., 0.0924, -3.4692, 3.1988]]])
36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
[ 1.8548, -2.4676, -0.7095, ..., -1.1532, -4.4559, -0.9997],
[ 1.2417, -1.9971, 0.1568, ..., -1.1543, -2.6090, 0.1292],
...,
[ 5.7820, 3.0155, -1.8956, ..., -2.4787, -2.6344, -1.2151],
[ 4.0808, -3.6748, -2.1742, ..., -2.6918, -2.2794, -2.7939],
[ 2.2751, -2.3297, -2.5446, ..., -1.3802, -3.7913, 2.4115]]])
37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
[ 1.0674, -1.5709, 0.4484, ..., -2.0207, -2.4859, 0.1802],
...,
[ 5.5040, 3.1839, -2.8336, ..., -4.0324, -3.4884, -2.0793],
[ 3.9983, -3.6839, -1.5382, ..., -2.9435, -2.7594, -2.7126],
[ 2.8967, -1.8728, -2.6846, ..., -1.7451, -3.1434, 2.6663]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
[ 0.5371, -1.7457, 0.3418, ..., -1.6540, -2.1705, -0.7345],
...,
[ 4.6393, 4.8868, -2.9311, ..., -4.2037, -3.5214, -2.4560],
[ 3.1115, -2.3124, -3.3956, ..., -2.8428, -2.7328, -3.9740],
[ 3.2790, -1.5665, -2.2524, ..., -3.7352, -2.6804, 3.0432]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[ 5.4348, 6.8611, -1.6130, ..., -5.1784, -4.6239, -1.2114],
[ 5.0587, -1.0823, -3.7525, ..., -4.1044, -3.1685, -3.9172],
[ 4.7822, -1.5043, -1.9351, ..., -2.6796, -3.2598, 2.0049]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
[-0.2762, 0.5192, -1.0364, ..., -0.4324, -0.5417, 0.2538],
...,
[ 1.5584, 1.7198, -0.0031, ..., -0.3266, -0.3783, 0.2575],
[ 1.7250, 0.4386, -0.9148, ..., -0.3658, 0.2130, -0.3298],
[ 0.6263, -0.2431, -0.6866, ..., -0.7611, -0.9270, -0.0489]]])
Query: - Die Studierenden stellen die Arbeit vor, die sie in ihrem Projekt durchgeführt haben.
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
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...,
[ 2.3972e-02, -2.0278e-04, -1.3125e-04, ..., -3.5553e-02,
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[ 5.7373e-03, 8.8644e-04, 3.9902e-03, ..., -8.1444e-04,
-2.6822e-04, 4.5359e-05],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
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[ 0.0056, -0.0070, -0.0281, ..., 0.0168, -0.0234, -0.0285]]])
2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
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3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
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4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
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5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
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[-0.0578, -0.0821, -0.0015, ..., -0.0959, -0.1038, 0.0429]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
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...,
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[-0.2856, -0.0031, 0.5695, ..., -0.3771, -0.2718, 0.2867]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
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[-1.9804, -0.9961, -0.4632, ..., -0.1531, -0.5983, 1.2083]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
[-0.2766, -0.5257, 0.8512, ..., -0.0022, -0.4838, -0.1323],
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...,
[-0.0353, 0.5249, -0.2622, ..., -0.6903, -0.0917, -0.2150],
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[-2.0497, -0.8909, -0.4648, ..., -0.1490, -0.6804, 1.1916]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
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10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
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11: tensor([[[ 0.1602, -0.9107, -0.5598, ..., 0.0381, -0.1310, 0.3694],
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12: tensor([[[ 0.1804, -0.9203, -0.5095, ..., -0.0744, -0.1174, 0.3818],
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13: tensor([[[ 2.5794e-01, -1.0105e+00, -5.3911e-01, ..., -3.4954e-02,
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14: tensor([[[ 0.2787, -1.0211, -0.4858, ..., -0.0717, -0.0655, 0.3964],
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15: tensor([[[ 0.2775, -1.0251, -0.2647, ..., -0.0380, -0.0748, 0.5438],
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16: tensor([[[ 0.3045, -1.0667, -0.2115, ..., -0.0522, -0.1122, 0.5783],
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17: tensor([[[ 0.3259, -1.0477, -0.2252, ..., 0.0335, -0.2652, 0.5971],
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18: tensor([[[ 0.3030, -0.9936, -0.3282, ..., -0.0327, -0.3823, 0.7322],
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27: tensor([[[ 0.5150, -1.0300, 0.1991, ..., 0.2264, -0.4491, 0.3086],
[ 0.2726, -1.9265, -0.1144, ..., -0.5303, -3.5600, 0.5535],
[ 0.5457, -2.0804, 0.5186, ..., -1.0915, -1.9407, 0.1254],
...,
[-0.7812, 1.2774, -2.6497, ..., -4.1146, 1.3070, -1.0650],
[-1.3812, 2.0349, 0.1347, ..., -1.4919, -0.5718, -2.7548],
[-1.3175, -0.1759, 0.3085, ..., 0.2312, -0.6923, 0.7700]]])
28: tensor([[[ 0.5767, -0.9113, 0.1827, ..., 0.2441, -0.5849, 0.2632],
[ 0.4596, -2.2676, 0.0314, ..., -0.7782, -3.3262, 0.7418],
[ 0.3451, -2.3089, 0.6245, ..., -0.9180, -1.8190, 0.1070],
...,
[-0.3616, 2.4763, -2.3346, ..., -3.7072, 0.5927, -1.0813],
[-0.7618, 2.6444, 0.0855, ..., -2.0002, -0.7370, -3.6717],
[-1.2798, -0.1835, 0.2567, ..., 0.2055, -0.7821, 0.6385]]])
29: tensor([[[ 0.5881, -0.8365, 0.1993, ..., 0.2294, -0.7033, 0.1812],
[ 0.8169, -2.5907, 0.3456, ..., -0.9426, -3.3235, 0.5329],
[ 0.5339, -2.1013, 0.5908, ..., -1.0304, -1.7530, 0.1138],
...,
[ 0.3123, 2.1778, -2.9448, ..., -4.2195, 0.4846, -1.4622],
[-0.7248, 3.6578, -0.6556, ..., -3.3541, -2.3005, -5.0432],
[-1.2628, -0.1232, 0.2391, ..., 0.1820, -0.7166, 0.5295]]])
30: tensor([[[ 6.6669e-01, -9.4694e-01, 2.0135e-01, ..., 1.9567e-01,
-7.3406e-01, 1.7867e-01],
[ 8.2389e-01, -2.3207e+00, 6.9412e-02, ..., -7.6211e-01,
-3.7438e+00, 4.4429e-01],
[ 5.5185e-01, -1.9823e+00, 4.1869e-01, ..., -8.9906e-01,
-1.8399e+00, 4.5760e-03],
...,
[ 2.7111e-01, 2.8599e+00, -3.5875e+00, ..., -4.3285e+00,
6.8919e-01, -2.0124e+00],
[-7.9051e-01, 5.0638e+00, -1.2121e+00, ..., -3.0400e+00,
-2.0513e+00, -5.6133e+00],
[-1.2363e+00, -1.1085e-01, 1.3271e-01, ..., 1.0423e-01,
-7.2271e-01, 4.3488e-01]]])
31: tensor([[[ 0.6655, -0.9598, 0.2447, ..., 0.1706, -0.7776, 0.1788],
[ 0.6942, -2.3152, 0.0467, ..., -1.0002, -4.1053, 0.4592],
[ 0.4674, -1.8792, 0.4066, ..., -1.1411, -1.8999, 0.0861],
...,
[ 0.6516, 2.7482, -3.5039, ..., -4.9964, 1.5581, -1.8802],
[ 0.2962, 6.0264, -0.3587, ..., -3.2570, -1.2621, -5.2897],
[-1.1365, -0.0761, 0.0758, ..., -0.0166, -0.6873, 0.4107]]])
32: tensor([[[ 0.7360, -0.9609, 0.2117, ..., 0.1602, -0.8576, 0.1393],
[ 0.6757, -2.3709, -0.2111, ..., -1.0661, -3.9552, 0.4456],
[ 0.4939, -2.4154, 0.0644, ..., -1.1614, -1.8203, 0.1791],
...,
[ 0.6485, 2.5032, -4.0874, ..., -5.8010, 1.6875, -1.3162],
[ 0.3994, 6.6089, -0.3857, ..., -4.0690, -1.1263, -5.1760],
[-1.1053, 0.0345, -0.0110, ..., -0.0714, -0.6341, 0.3062]]])
33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
[ 0.6746, -2.3491, -0.5023, ..., -1.1778, -4.0825, 0.6369],
[ 0.3326, -2.5822, -0.0435, ..., -0.7961, -1.9972, 0.2151],
...,
[ 0.6526, 1.9058, -3.4074, ..., -5.8650, 1.8857, -0.0278],
[-0.1454, 7.6247, 0.7713, ..., -5.3342, -0.5902, -4.7405],
[-1.3517, -0.0644, 0.2302, ..., -0.2760, -0.2542, 0.3805]]])
34: tensor([[[ 0.8220, -0.9002, 0.5914, ..., 0.1699, -0.9568, -0.2156],
[ 0.8671, -2.4411, -0.2519, ..., -1.0163, -3.7759, 0.2840],
[ 0.7680, -2.6552, 0.3849, ..., -0.8292, -1.9769, 0.5669],
...,
[ 0.4809, 2.1281, -3.0718, ..., -5.6935, 1.4950, -0.1685],
[-0.0746, 7.8515, 1.0757, ..., -4.7387, -1.0454, -5.0983],
[-1.6474, -0.2032, 0.3310, ..., -0.3601, -0.1654, 0.2689]]])
35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
[ 1.4832, -2.1899, -0.7593, ..., -0.9099, -4.0092, -0.0704],
[ 1.1589, -2.4299, -0.0361, ..., -0.9148, -2.3421, 0.3966],
...,
[ 0.8881, 2.5851, -3.6456, ..., -6.1885, 1.2963, -1.5362],
[ 0.9818, 7.7238, 0.6269, ..., -6.1361, -0.3095, -5.6264],
[-1.3262, -0.0615, 0.5307, ..., -0.4797, -0.2088, 0.3896]]])
36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
[ 1.8548, -2.4676, -0.7095, ..., -1.1532, -4.4559, -0.9997],
[ 1.2417, -1.9971, 0.1568, ..., -1.1543, -2.6090, 0.1292],
...,
[ 0.7461, 1.4271, -4.1591, ..., -5.8778, 1.1293, -2.3668],
[ 1.8087, 6.6289, 0.4276, ..., -5.8458, -0.7458, -5.7743],
[-1.3178, 0.1217, 0.1664, ..., -0.3842, -0.4523, 0.6429]]])
37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
[ 1.0674, -1.5709, 0.4484, ..., -2.0207, -2.4859, 0.1802],
...,
[ 1.1313, 2.2636, -3.6805, ..., -5.2630, 0.4008, -1.5084],
[ 1.7066, 7.6131, 0.4980, ..., -4.8408, -1.1042, -5.4813],
[-1.3259, 0.5897, 0.4915, ..., -0.3650, -0.5739, 0.4091]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
[ 0.5371, -1.7457, 0.3418, ..., -1.6540, -2.1705, -0.7345],
...,
[ 1.1155, 1.5973, -3.6675, ..., -5.4247, 1.2067, -3.4170],
[ 0.9700, 9.6785, 1.0319, ..., -6.0327, -1.6970, -3.8280],
[-2.1905, -0.0180, 0.7750, ..., -0.8254, 1.3877, -0.1687]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[ 0.9962, 1.9322, -4.1520, ..., -3.4251, -1.8255, -2.3749],
[ 0.6422, 8.6550, 1.7368, ..., -4.1500, -3.3253, -3.5871],
[-1.3970, 0.3053, 0.2171, ..., -0.6177, 0.9071, -0.9128]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
[-0.2762, 0.5192, -1.0364, ..., -0.4324, -0.5417, 0.2538],
...,
[-0.1141, 0.0607, -2.0946, ..., -1.1622, -0.9038, -0.9474],
[-0.5834, 0.9868, 0.2401, ..., -0.2301, -0.6555, -0.6881],
[-0.0633, 0.0692, 0.2187, ..., -0.2508, -0.0306, -0.2867]]])
Query: - Mengenlehre
- Relationen
- Funktionen
- Aussagenlogik
- Beweismethoden
- Grundlagen der Graphentheorie
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
3.9864e-03, -2.9182e-03],
...,
[ 1.6098e-02, 3.4760e-02, -9.1095e-03, ..., -1.0216e-02,
-1.4565e-02, 3.5706e-03],
[ 7.6437e-04, -8.6899e-03, 1.6922e-02, ..., 2.3422e-02,
3.4904e-03, 6.2904e-03],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
[-0.1016, -0.0195, -0.0424, ..., 0.0752, -0.0704, -0.0427],
[-0.0588, 0.0121, 0.0111, ..., 0.0136, -0.0375, -0.0212],
...,
[ 0.0461, 0.0897, 0.0064, ..., -0.0292, -0.0642, 0.0472],
[ 0.0063, -0.0087, -0.0298, ..., 0.0878, -0.1110, 0.0593],
[ 0.0140, -0.0115, -0.0091, ..., 0.0296, -0.0305, 0.0100]]])
2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
[-0.0020, -0.1035, -0.0208, ..., 0.1182, 0.0451, 0.0044],
[-0.0366, -0.0052, 0.0223, ..., 0.0163, -0.0730, -0.0328],
...,
[ 0.1179, 0.1033, 0.1548, ..., -0.1027, 0.0025, -0.1722],
[ 0.0735, -0.0360, -0.1164, ..., 0.0476, -0.0777, 0.1082],
[ 0.0518, 0.0026, -0.0009, ..., 0.0316, -0.0303, 0.0231]]])
3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
[ 0.0063, -0.1105, 0.1225, ..., 0.1197, -0.1124, -0.1199],
[ 0.0570, -0.0177, 0.2330, ..., 0.1012, -0.1598, -0.1215],
...,
[ 0.1470, 0.1515, 0.3102, ..., -0.1093, 0.0046, -0.0822],
[ 0.0538, 0.0941, 0.1162, ..., -0.1746, -0.1119, 0.1755],
[ 0.1098, -0.0380, 0.0009, ..., -0.0116, -0.0571, 0.0317]]])
4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
[-0.0628, -0.1661, 0.3024, ..., 0.2507, -0.1214, -0.0883],
[ 0.0489, -0.1734, 0.2030, ..., -0.0738, -0.2820, -0.1909],
...,
[ 0.1407, 0.2869, 0.4148, ..., -0.0755, 0.0296, -0.0737],
[-0.1282, 0.3680, 0.1338, ..., -0.1897, -0.0071, -0.1650],
[ 0.1018, 0.0188, 0.0054, ..., 0.0452, -0.1156, 0.1276]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
[ 0.1375, -0.0402, 0.2607, ..., 0.0044, -0.4515, -0.2382],
...,
[ 0.0659, 0.2910, 0.3964, ..., -0.2146, -0.0567, -0.0810],
[-0.1804, 0.2758, 0.1489, ..., -0.0156, 0.1502, -0.2088],
[ 0.1816, -0.0108, -0.0123, ..., -0.0262, -0.1061, 0.2227]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
[-0.0033, -0.2674, 0.3796, ..., -0.0058, -0.3464, -0.3838],
...,
[ 0.0014, 0.2429, 0.3601, ..., -0.4425, -0.1261, 0.0690],
[ 0.0360, 0.1479, 0.2533, ..., -0.0837, 0.3390, -0.0169],
[ 0.2119, -0.1909, 0.0445, ..., -0.1581, -0.1025, 0.4809]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
[-0.0632, -0.1760, 0.3805, ..., 0.1593, -0.3386, -0.4046],
...,
[ 0.0115, 0.1401, 0.4832, ..., -0.3560, -0.0651, 0.2497],
[-0.1056, 0.2176, 0.3350, ..., -0.1631, 0.6191, 0.1026],
[ 0.2731, -0.0777, 0.4990, ..., -0.0329, -0.1890, 0.5276]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
[-0.2766, -0.5257, 0.8512, ..., -0.0022, -0.4838, -0.1323],
[-0.0459, -0.1794, 0.4229, ..., 0.3498, -0.4034, -0.4652],
...,
[-0.0575, 0.2244, 0.4655, ..., -0.5955, 0.3221, 0.1557],
[ 0.1143, 0.9447, 0.3222, ..., -0.4827, 0.8724, 0.2751],
[ 0.3513, -0.1024, 0.3001, ..., -0.0747, -0.3906, 0.4521]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
[-1.2754e-01, -7.2784e-01, 6.2181e-01, ..., -5.9251e-04,
-4.6773e-01, -2.0511e-01],
[-1.5388e-01, -3.8383e-01, 4.7796e-01, ..., 5.4677e-01,
-5.7957e-01, -3.6326e-01],
...,
[-4.9255e-01, 4.1581e-01, 3.0628e-01, ..., -5.5830e-01,
3.9320e-01, 1.6156e-01],
[-1.3316e-01, 7.8164e-01, 3.8502e-01, ..., -5.7117e-01,
9.8560e-01, 3.2164e-01],
[-1.8581e-01, -3.7488e-02, 4.4190e-01, ..., 1.1356e-01,
-2.8790e-01, 4.0804e-01]]])
10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
[-0.1813, -0.8617, 0.3325, ..., -0.0257, -0.4533, -0.0284],
[-0.1423, -0.4132, 0.1746, ..., 0.3776, -0.6512, -0.4133],
...,
[-0.4630, 0.3650, 0.4395, ..., -0.6365, 0.3474, 0.3672],
[-0.3170, 0.5936, 0.3499, ..., -0.4268, 0.6762, 0.3724],
[ 0.2064, -0.2871, 0.1320, ..., 0.0526, -0.6448, 0.4338]]])
11: tensor([[[ 0.1602, -0.9107, -0.5598, ..., 0.0381, -0.1310, 0.3694],
[-0.2011, -0.5420, 0.2242, ..., -0.2060, -0.5998, 0.2015],
[-0.0183, -0.6304, 0.1346, ..., 0.1680, -0.5393, -0.5965],
...,
[-0.5278, 0.3602, 0.1965, ..., -0.8570, 0.4485, 0.4128],
[-0.4668, 0.3502, 0.2331, ..., -0.2967, 0.9081, 0.3723],
[ 0.3500, -0.4878, 0.1879, ..., 0.1599, -0.7058, 0.5221]]])
12: tensor([[[ 1.8037e-01, -9.2031e-01, -5.0948e-01, ..., -7.4439e-02,
-1.1742e-01, 3.8180e-01],
[-4.3383e-01, -7.3806e-01, 8.4982e-02, ..., -2.9242e-01,
-6.6474e-01, 2.4805e-01],
[-2.2383e-01, -8.7245e-01, 1.6042e-01, ..., 4.1013e-01,
-5.6985e-01, -4.7507e-01],
...,
[-5.0083e-01, 5.9027e-01, -5.6436e-04, ..., -6.3920e-01,
1.9974e-01, 5.9026e-01],
[-5.4544e-01, 5.8127e-02, 2.0236e-01, ..., -6.4943e-01,
5.5445e-01, 3.7074e-01],
[ 3.9800e-01, -7.1451e-01, 1.2419e-01, ..., 2.4037e-01,
-5.4010e-01, 4.5544e-01]]])
13: tensor([[[ 0.2579, -1.0105, -0.5391, ..., -0.0350, -0.2018, 0.4527],
[-0.6401, -0.7804, 0.0014, ..., 0.1250, -0.3100, 0.1285],
[-0.0604, -0.9795, 0.4270, ..., 0.1772, -0.6683, -0.4837],
...,
[-0.1448, 0.5132, 0.1092, ..., -0.2989, 0.0790, 0.8673],
[-0.3571, 0.2656, 0.0215, ..., -0.5430, 0.0725, 0.6909],
[ 0.2471, -0.6484, 0.1008, ..., -0.0337, -0.9814, 0.8171]]])
14: tensor([[[ 0.2787, -1.0211, -0.4858, ..., -0.0717, -0.0655, 0.3964],
[-0.6753, -0.9677, 0.0407, ..., -0.0744, -0.4973, -0.2153],
[-0.2201, -0.9099, 0.2618, ..., 0.0895, -0.6634, -0.9823],
...,
[ 0.1374, 0.3741, 0.0261, ..., -0.5894, 0.1455, 0.9694],
[-0.3450, 0.4075, -0.2621, ..., -0.7971, -0.1101, 0.2491],
[ 0.0423, -0.6716, 0.0594, ..., 0.2826, -0.7657, 0.5648]]])
15: tensor([[[ 0.2775, -1.0251, -0.2647, ..., -0.0380, -0.0748, 0.5438],
[-0.2879, -1.1037, 0.4039, ..., -0.3520, -0.6702, -0.3490],
[ 0.0341, -1.1546, 0.3763, ..., -0.3480, -0.8019, -0.7203],
...,
[ 0.1013, 0.1055, -0.0942, ..., -0.9727, 0.0183, 0.7974],
[-0.0940, 0.0345, -0.2147, ..., -0.7905, -0.5264, 0.2794],
[ 0.4020, -0.9309, -0.3201, ..., 0.8048, -0.9418, 0.4986]]])
16: tensor([[[ 0.3045, -1.0667, -0.2115, ..., -0.0522, -0.1122, 0.5783],
[-0.1683, -1.4639, 0.4694, ..., -0.1146, -0.9781, -0.5914],
[ 0.1871, -1.3984, 0.1202, ..., -0.1305, -0.6274, -0.8889],
...,
[-0.3127, -0.1287, 0.3492, ..., -1.0157, 0.1762, 0.8593],
[ 0.0819, -0.3857, -0.3485, ..., -1.0936, -0.3150, -0.1172],
[ 0.1009, -1.2804, -0.4555, ..., 0.6882, -1.3393, 0.9782]]])
17: tensor([[[ 0.3259, -1.0477, -0.2252, ..., 0.0335, -0.2652, 0.5971],
[-0.2339, -1.5909, 0.4839, ..., 0.0959, -1.0865, -0.6412],
[ 0.1523, -1.3746, -0.0511, ..., -0.2632, -0.5859, -0.7240],
...,
[-0.2003, -0.0884, 0.6824, ..., -0.7103, -0.1149, 0.6836],
[ 0.0762, -0.8759, -0.0763, ..., -1.4364, 0.0894, -0.2905],
[ 0.0365, -1.3837, -0.0618, ..., 0.5090, -1.4337, 0.8130]]])
18: tensor([[[ 0.3030, -0.9936, -0.3282, ..., -0.0327, -0.3823, 0.7322],
[-0.1053, -1.5004, 0.2582, ..., -0.0130, -1.5225, -1.0915],
[ 0.3871, -1.3789, -0.0732, ..., 0.1195, -0.8012, -0.9241],
...,
[ 0.1856, 0.2429, 1.4132, ..., -1.0244, -0.1345, 0.8680],
[ 0.8180, -0.8104, 0.3273, ..., -1.8266, 0.1171, -0.8493],
[ 0.2014, -1.3486, -0.2694, ..., 0.7981, -2.1515, 0.5188]]])
19: tensor([[[ 0.3025, -1.2240, -0.1138, ..., 0.1373, -0.4463, 0.8681],
[ 0.3981, -1.5456, 0.3258, ..., -0.1875, -1.6000, -1.1540],
[ 0.4815, -1.4289, -0.0317, ..., -0.0071, -1.0063, -1.0037],
...,
[ 0.1338, 0.4544, 1.6870, ..., -0.8420, -0.3116, 0.9296],
[ 0.5088, -0.6094, 0.2773, ..., -2.0019, -0.0275, -0.9354],
[-0.0840, -1.9257, -1.2878, ..., 0.8514, -1.9142, 0.2702]]])
20: tensor([[[ 0.4686, -1.2657, -0.0419, ..., 0.0919, -0.3436, 0.8809],
[ 0.3482, -1.9316, -0.2961, ..., 0.0607, -1.7172, -0.8600],
[ 0.4599, -1.7459, -0.2516, ..., -0.1474, -1.0419, -0.7416],
...,
[-0.0244, 0.4743, 1.9273, ..., -0.9234, -0.8093, 1.3592],
[ 0.4697, -0.4573, 0.3385, ..., -2.0901, 0.0126, -1.2972],
[ 0.3864, -2.0689, -0.5691, ..., 0.7240, -1.6727, -0.0374]]])
21: tensor([[[ 0.4442, -1.0962, 0.0778, ..., 0.0597, -0.3985, 0.7814],
[ 0.0725, -1.7982, -0.3599, ..., -0.1201, -1.6566, -1.1994],
[ 0.3863, -1.8144, 0.2474, ..., 0.0598, -1.3684, -0.8320],
...,
[-0.1854, 0.8582, 2.3229, ..., -1.3185, -0.7054, 1.5230],
[ 0.5405, -0.8896, 0.4511, ..., -1.9539, 0.3569, -1.4957],
[ 0.4392, -2.3813, -0.4033, ..., 0.6854, -1.7337, 0.1820]]])
22: tensor([[[ 0.5059, -1.1660, 0.2683, ..., -0.0197, -0.3653, 0.8172],
[ 0.4792, -2.1132, 0.1638, ..., -0.0667, -2.0503, -0.8644],
[ 0.2983, -2.1457, 0.5011, ..., -0.4051, -1.3177, -0.6711],
...,
[-0.1151, 0.4913, 2.5855, ..., -1.5962, -1.0009, 0.8460],
[ 0.9764, -0.8184, 0.3044, ..., -1.5943, 0.5501, -1.8421],
[-0.0815, -2.3605, 0.1892, ..., 0.9575, -1.6534, 0.9086]]])
23: tensor([[[ 0.4414, -1.2350, 0.1653, ..., 0.0749, -0.5487, 0.5796],
[ 0.9800, -2.0098, 0.3139, ..., -0.1216, -2.4571, -0.1834],
[ 0.1384, -2.1878, 0.6559, ..., -0.4342, -1.4843, -0.3502],
...,
[-0.0037, 0.5611, 2.7174, ..., -1.6061, -0.8740, 0.3373],
[ 1.3722, -0.6920, 0.2908, ..., -2.2945, 0.2150, -1.6304],
[-0.2611, -1.6973, 0.0835, ..., 0.9255, -1.5313, 0.9388]]])
24: tensor([[[ 0.4183, -1.1111, 0.1459, ..., 0.0492, -0.4547, 0.3954],
[ 0.5153, -1.9330, 0.0819, ..., -0.2930, -2.4385, 0.1704],
[ 0.1086, -1.9690, 0.3744, ..., -0.5352, -1.3599, -0.1157],
...,
[ 0.9761, 1.7388, 2.5815, ..., -2.5067, -0.3804, 0.5286],
[ 1.8137, -0.9440, 0.1030, ..., -3.7492, 1.1473, -1.2198],
[-0.1430, -1.4137, 0.6602, ..., 1.4071, -1.1148, 0.9332]]])
25: tensor([[[ 0.4975, -1.1003, 0.2274, ..., 0.1581, -0.4560, 0.4482],
[ 0.4099, -2.1553, -0.0299, ..., -0.4242, -2.9810, 0.1285],
[ 0.1489, -1.9066, 0.3926, ..., -1.2392, -1.9434, -0.0387],
...,
[ 1.2025, 2.0662, 3.5233, ..., -2.2425, -0.0682, 1.2461],
[ 1.9762, -0.8743, 0.0719, ..., -4.4537, 1.7556, -0.6400],
[ 0.5153, -1.4775, 0.6908, ..., 1.0385, -0.9582, 0.6929]]])
26: tensor([[[ 0.4632, -1.1296, 0.2315, ..., 0.1767, -0.4738, 0.3622],
[ 0.3349, -2.0610, -0.2346, ..., -0.4175, -3.3164, 0.6413],
[ 0.3103, -2.0197, 0.5268, ..., -1.1227, -2.0411, -0.1146],
...,
[ 0.9790, 1.1878, 3.8949, ..., -2.2404, -0.2371, 0.5656],
[ 1.9021, -1.9184, 0.0198, ..., -4.0369, 2.4258, -0.7455],
[ 0.0411, -1.7036, 0.0761, ..., 0.9444, -0.8800, 1.0136]]])
27: tensor([[[ 0.5150, -1.0300, 0.1991, ..., 0.2264, -0.4491, 0.3086],
[ 0.2726, -1.9265, -0.1144, ..., -0.5303, -3.5600, 0.5535],
[ 0.5457, -2.0804, 0.5186, ..., -1.0915, -1.9407, 0.1254],
...,
[ 1.4235, 1.7583, 4.3152, ..., -2.8914, 0.3722, 0.7043],
[ 1.5440, -2.1802, -0.1125, ..., -4.3676, 2.9829, -0.8759],
[-0.5102, -2.3482, 0.5267, ..., 1.5152, -0.0589, 1.1481]]])
28: tensor([[[ 0.5767, -0.9113, 0.1827, ..., 0.2441, -0.5849, 0.2632],
[ 0.4596, -2.2676, 0.0314, ..., -0.7782, -3.3262, 0.7418],
[ 0.3451, -2.3089, 0.6245, ..., -0.9180, -1.8190, 0.1070],
...,
[ 1.1074, 1.8339, 4.5474, ..., -2.8203, 0.4948, 0.9853],
[ 1.3799, -2.3346, -0.5102, ..., -4.0409, 3.3346, -0.8523],
[ 0.0152, -2.5783, 0.8893, ..., 1.9932, -0.2732, 0.8604]]])
29: tensor([[[ 0.5881, -0.8365, 0.1993, ..., 0.2294, -0.7033, 0.1812],
[ 0.8169, -2.5907, 0.3456, ..., -0.9426, -3.3235, 0.5329],
[ 0.5339, -2.1013, 0.5908, ..., -1.0304, -1.7530, 0.1138],
...,
[ 0.6564, 2.4990, 4.9982, ..., -2.3118, 1.4917, 1.0934],
[ 1.7551, -3.3136, -1.1686, ..., -3.6447, 3.3586, -1.4456],
[ 0.5340, -2.7223, 0.7235, ..., 2.1678, -0.8151, 0.6597]]])
30: tensor([[[ 6.6669e-01, -9.4694e-01, 2.0135e-01, ..., 1.9567e-01,
-7.3406e-01, 1.7867e-01],
[ 8.2389e-01, -2.3207e+00, 6.9412e-02, ..., -7.6211e-01,
-3.7438e+00, 4.4429e-01],
[ 5.5186e-01, -1.9823e+00, 4.1869e-01, ..., -8.9906e-01,
-1.8399e+00, 4.5765e-03],
...,
[ 6.3199e-01, 3.2741e+00, 5.0358e+00, ..., -2.0807e+00,
8.6799e-01, 1.7870e+00],
[ 1.7479e+00, -3.1582e+00, -1.7325e+00, ..., -3.9119e+00,
3.2194e+00, -1.0853e+00],
[ 9.5036e-01, -1.9371e+00, 6.6727e-01, ..., 2.2617e+00,
-6.4823e-01, 2.5810e-01]]])
31: tensor([[[ 0.6655, -0.9598, 0.2447, ..., 0.1706, -0.7776, 0.1788],
[ 0.6942, -2.3152, 0.0467, ..., -1.0002, -4.1053, 0.4592],
[ 0.4674, -1.8792, 0.4066, ..., -1.1411, -1.8999, 0.0861],
...,
[ 0.5796, 4.6916, 4.9940, ..., -1.2110, 0.5878, 1.0981],
[ 1.8922, -3.3787, -1.4038, ..., -4.0390, 3.4836, -0.2285],
[ 0.7623, -1.5991, 2.0060, ..., 1.4223, -0.5137, 0.7858]]])
32: tensor([[[ 0.7360, -0.9609, 0.2117, ..., 0.1602, -0.8576, 0.1393],
[ 0.6757, -2.3709, -0.2111, ..., -1.0661, -3.9552, 0.4456],
[ 0.4939, -2.4154, 0.0644, ..., -1.1614, -1.8203, 0.1791],
...,
[ 0.3747, 4.6985, 4.9862, ..., -0.9676, 1.3786, 1.1992],
[ 1.7156, -2.8694, -1.2231, ..., -5.2842, 3.6856, -0.4775],
[-0.1569, -1.8885, 1.9445, ..., 0.9040, -0.5789, 0.1705]]])
33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
[ 0.6746, -2.3491, -0.5023, ..., -1.1778, -4.0825, 0.6369],
[ 0.3326, -2.5822, -0.0435, ..., -0.7961, -1.9972, 0.2151],
...,
[ 0.0974, 4.7795, 4.6941, ..., -1.4672, 1.0888, 0.9218],
[ 1.7496, -2.9414, -1.3257, ..., -5.6713, 4.6880, 0.1936],
[ 0.2487, -0.1877, 1.9572, ..., 1.4479, -0.0331, 1.1446]]])
34: tensor([[[ 0.8220, -0.9002, 0.5914, ..., 0.1699, -0.9568, -0.2156],
[ 0.8671, -2.4411, -0.2519, ..., -1.0163, -3.7759, 0.2840],
[ 0.7680, -2.6552, 0.3849, ..., -0.8292, -1.9769, 0.5669],
...,
[ 0.9262, 4.7050, 4.7234, ..., -1.7600, 1.1052, 1.0700],
[ 1.5848, -2.8924, -1.5291, ..., -6.0260, 4.0944, 0.2600],
[ 0.1654, -0.2146, 2.8077, ..., 0.7136, 0.5567, 1.3989]]])
35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
[ 1.4832, -2.1899, -0.7593, ..., -0.9099, -4.0092, -0.0704],
[ 1.1589, -2.4299, -0.0361, ..., -0.9148, -2.3421, 0.3966],
...,
[ 1.7111, 4.6622, 5.8766, ..., -0.8909, 0.3711, 1.3037],
[ 1.7620, -3.4936, -1.7577, ..., -5.6797, 4.7846, 0.4596],
[ 0.1559, -0.6734, 2.4810, ..., 0.4417, 0.9362, 0.5252]]])
36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
[ 1.8548, -2.4676, -0.7095, ..., -1.1532, -4.4559, -0.9997],
[ 1.2417, -1.9971, 0.1568, ..., -1.1543, -2.6090, 0.1292],
...,
[ 1.3428, 5.6012, 4.6460, ..., -1.2475, -0.8683, 1.3372],
[ 2.2800, -3.6267, -2.0918, ..., -4.6885, 4.4251, 0.2924],
[ 0.1161, -1.0896, 3.3152, ..., 0.4793, 1.0109, -0.2065]]])
37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
[ 1.0674, -1.5709, 0.4484, ..., -2.0207, -2.4859, 0.1802],
...,
[ 1.8976, 5.1624, 4.0970, ..., -2.3888, -2.4494, -0.3276],
[ 2.4674, -3.2610, -0.7130, ..., -4.8163, 4.2108, 0.4165],
[-0.0300, -1.5933, 4.0204, ..., 0.0235, 1.5836, -1.2533]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
[ 0.5371, -1.7457, 0.3418, ..., -1.6540, -2.1705, -0.7345],
...,
[ 2.2090, 3.7503, 5.4528, ..., -3.8951, -3.4449, -0.7205],
[ 2.7076, -2.9306, -1.5168, ..., -4.5041, 3.2534, -0.3942],
[ 0.4526, -1.4406, 4.1080, ..., -1.5814, 2.4001, -1.4191]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[ 2.9123, 3.9260, 7.2365, ..., -3.5860, -3.0253, -1.6691],
[ 2.5081, -4.4568, -1.9197, ..., -2.6698, 2.3524, 0.3838],
[ 1.3931, -1.1944, 5.3884, ..., -0.3634, 1.1472, -3.4749]]])
40: tensor([[[-9.8127e-01, -8.8043e-01, -4.7547e-01, ..., -3.7186e-01,
-1.7917e-01, -4.0121e-01],
[ 9.8056e-02, -3.6278e-01, 1.9399e-01, ..., -1.1206e+00,
-2.0881e+00, -1.0290e+00],
[-2.7625e-01, 5.1922e-01, -1.0364e+00, ..., -4.3238e-01,
-5.4167e-01, 2.5382e-01],
...,
[ 6.3622e-01, -8.2315e-03, 1.3320e+00, ..., -2.0341e-01,
-2.1952e-01, 1.7925e-04],
[ 5.0078e-01, -3.7702e-01, -1.1084e+00, ..., -1.1617e-03,
7.2730e-01, 3.2410e-01],
[-3.5432e-01, -4.9268e-01, 1.8404e-01, ..., -3.3481e-01,
-4.5222e-01, -1.0299e+00]]])
Query: - Zahlentheorie (Primzahlen, Primzahlfaktorisierung, Eulersche Phi-Funktion, Stellenwert-systeme, Zahlensyste-
me mit beliebigen Basen, duale Basis, Zahlendarstellung im Computer)
- Mathematische Grundlagen der Kryptographie (erweiterter Euklidischer Algorithmus, modulare Arithmetik,
Restklassenringe, Invertierbarkeit bzgl. der Multiplika-tion in Restklassenringen, Satz von Fermat-Euler)
- Grundlagen der linearen Algebra und Anwendungen in der Informatik (Lineare Gleichungssysteme, Gauß-
Algorithmus, der Rn als Vektorraum, Vektorrechnung, Skalarprodukt, Kreuzprodukt, Geraden, Ebenen, Ma-
trizen, Drehmatrizen, Spiegelungen, symmetrische Matrizen, inverse Matrix, Determinanten, Eigenwerte)
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
3.9864e-03, -2.9182e-03],
...,
[-1.5388e-02, -1.3588e-02, -2.2507e-02, ..., 9.8495e-03,
1.1246e-02, -1.7776e-03],
[-1.3863e-02, 2.3422e-03, 2.1393e-02, ..., -3.9444e-03,
1.2177e-02, 7.6714e-03],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
[-0.1016, -0.0195, -0.0424, ..., 0.0752, -0.0704, -0.0427],
[-0.0588, 0.0121, 0.0111, ..., 0.0136, -0.0375, -0.0212],
...,
[-0.0425, -0.0036, -0.0008, ..., -0.0131, 0.0063, 0.0406],
[-0.0194, -0.0221, 0.0541, ..., -0.0030, 0.0485, 0.0735],
[ 0.0021, -0.0211, -0.0161, ..., 0.0431, 0.0110, -0.0072]]])
2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
[-0.0020, -0.1035, -0.0208, ..., 0.1182, 0.0451, 0.0044],
[-0.0366, -0.0052, 0.0223, ..., 0.0163, -0.0730, -0.0328],
...,
[-0.0880, 0.0127, 0.0533, ..., 0.0127, -0.0227, 0.0236],
[-0.0637, 0.0377, 0.0661, ..., -0.0123, 0.0416, 0.1095],
[ 0.0564, -0.0149, -0.0280, ..., 0.0115, 0.0094, 0.0064]]])
3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
[ 0.0063, -0.1105, 0.1225, ..., 0.1197, -0.1124, -0.1199],
[ 0.0570, -0.0177, 0.2330, ..., 0.1012, -0.1598, -0.1215],
...,
[-0.1779, 0.2426, 0.0208, ..., 0.1346, 0.0890, 0.0124],
[-0.1130, 0.0345, 0.1217, ..., 0.0141, 0.1236, 0.1026],
[ 0.1674, -0.0143, 0.0490, ..., 0.0311, 0.0629, 0.0974]]])
4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
[-0.0628, -0.1661, 0.3024, ..., 0.2507, -0.1214, -0.0883],
[ 0.0489, -0.1734, 0.2030, ..., -0.0738, -0.2820, -0.1909],
...,
[ 0.0120, 0.3501, 0.0718, ..., 0.1448, 0.1026, -0.1031],
[-0.1880, -0.0301, 0.1600, ..., 0.0536, 0.1607, 0.1117],
[ 0.1381, -0.0777, 0.0629, ..., 0.0262, 0.0447, 0.1854]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
[ 0.1375, -0.0402, 0.2607, ..., 0.0044, -0.4515, -0.2382],
...,
[-0.0039, 0.3987, 0.1563, ..., 0.2051, -0.0120, -0.1093],
[-0.0787, -0.0726, 0.1560, ..., -0.0223, 0.2099, 0.1925],
[ 0.1694, 0.0217, 0.0229, ..., -0.1793, 0.1400, 0.1569]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
[-0.0033, -0.2674, 0.3796, ..., -0.0058, -0.3464, -0.3838],
...,
[-0.0206, 0.5548, 0.4449, ..., -0.0593, -0.0605, -0.1844],
[-0.1833, 0.0138, 0.1977, ..., 0.2960, 0.6368, 0.3367],
[ 0.2714, -0.0307, 0.2515, ..., -0.1149, 0.3557, 0.2931]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
[-0.0632, -0.1760, 0.3805, ..., 0.1593, -0.3386, -0.4046],
...,
[ 0.1499, 0.6762, 0.4034, ..., -0.0936, -0.0659, -0.1602],
[-0.0383, 0.1522, 0.3308, ..., 0.2480, 0.5520, 0.3691],
[ 0.3236, 0.0194, 0.4616, ..., -0.1243, 0.3479, 0.4950]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
[-0.2766, -0.5257, 0.8512, ..., -0.0022, -0.4838, -0.1323],
[-0.0459, -0.1794, 0.4229, ..., 0.3498, -0.4034, -0.4652],
...,
[ 0.1538, 0.5911, 0.5182, ..., -0.2484, 0.1955, -0.2598],
[ 0.1909, 0.2552, 0.0932, ..., 0.2844, 0.5339, 0.4809],
[ 0.1565, -0.1873, 0.3460, ..., -0.4245, 0.1199, 0.5905]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
[-1.2754e-01, -7.2784e-01, 6.2181e-01, ..., -5.9251e-04,
-4.6773e-01, -2.0511e-01],
[-1.5388e-01, -3.8383e-01, 4.7796e-01, ..., 5.4677e-01,
-5.7957e-01, -3.6326e-01],
...,
[-1.4844e-01, 6.2345e-01, 6.1989e-01, ..., -3.8166e-01,
-4.6247e-02, -8.2174e-01],
[ 2.3174e-02, 4.8192e-01, 3.2863e-01, ..., 2.3095e-01,
2.4435e-01, 3.0075e-01],
[-2.6265e-02, -3.4406e-01, 5.5148e-01, ..., -3.5735e-01,
1.6445e-01, 6.1662e-01]]])
10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
[-0.1813, -0.8617, 0.3325, ..., -0.0257, -0.4533, -0.0284],
[-0.1423, -0.4132, 0.1746, ..., 0.3776, -0.6512, -0.4133],
...,
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39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
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40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
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Query: - Kombinatorik
- klassische Wahrscheinlichkeit, Zufallsexperiment
- Ereignisraum, Elementarereignis, Ereignis, Axiome der Wahrscheinlichkeitstheorie, Rechenregeln, Zusammen-
hang mit der klassischen Definition
- Bedingte Wahrscheinlichkeit, Satz von der totalen Wahrscheinlichkeit, Satz von Bayes
- Zufallsgrößen, Wahrscheinlichkeitsfunktionen, Verteilungsfunktionen,Erwartungswert und Varianz, Standard-
abweichung, Rechenregeln
- Zentraler Grenzwertsatz,Grenzwertsatz von Moivre-Laplace,
- Kovarianz und Korrelation, Rechenregeln
- Grundbegriffe der Statistik
- Parameterschätzungen
- Regression
- Maximum-Likelihood-Methode,
- Konfidenzintervalle
- Hypothesentests: Students- und Chi-Quadrat-Verteilung, ROCKurven
Modulhandbuch Fakultät für Informatik
18
Embeddings:
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36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
[ 1.8548, -2.4676, -0.7095, ..., -1.1532, -4.4559, -0.9997],
[ 1.2417, -1.9971, 0.1568, ..., -1.1543, -2.6090, 0.1292],
...,
[-1.9775, -4.0888, -0.1315, ..., 3.2052, 0.1157, -0.2452],
[-0.4852, -2.0224, -2.2545, ..., 2.9970, -0.5921, 0.7368],
[-2.9297, -0.0956, -1.0080, ..., 2.3570, -3.3942, 0.9177]]])
37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
[ 1.0674, -1.5709, 0.4484, ..., -2.0207, -2.4859, 0.1802],
...,
[-2.5197, -4.1751, 0.1121, ..., 2.6917, 0.0420, -1.2030],
[-0.6042, -2.8717, -1.9290, ..., 2.6819, -0.5994, -0.3347],
[-2.9827, -0.9883, -1.2098, ..., 1.7645, -2.9372, 1.0532]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
[ 0.5371, -1.7457, 0.3418, ..., -1.6540, -2.1705, -0.7345],
...,
[-1.9896, -3.6550, -0.1931, ..., 1.7884, -0.8605, -1.6116],
[-1.1046, -3.0902, -1.5775, ..., 2.6341, -2.0642, -0.2962],
[-2.5134, -1.4954, -1.3125, ..., 1.5697, -3.4244, 1.6971]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[-2.4752, -4.8226, -2.1570, ..., 3.7299, -1.9209, -1.5795],
[-2.3194, -5.0134, -3.0856, ..., 4.7717, -3.3695, -0.1986],
[-2.1236, -1.8289, -1.3419, ..., 2.5041, -4.0712, 2.0934]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
[-0.2762, 0.5192, -1.0364, ..., -0.4324, -0.5417, 0.2538],
...,
[-0.9110, -1.0568, -1.3229, ..., 0.4377, -0.9389, -0.2351],
[-0.9540, -1.2079, -1.6306, ..., 0.8405, -1.1080, 0.1097],
[-0.9472, -0.3908, -1.1174, ..., 0.2863, -0.8876, 0.5446]]])
Query: - Theoriethemen
- Übungen zur Konzeption eines mittelständischen Unternehmens (KMU) als Teil eines Konzerns
- Übungen zum IT-Transfer: Abbildung von Geschäftsprozessen von KMUs in einer Business Suite (z. B. SAP
Business ByDesign)
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
3.9864e-03, -2.9182e-03],
...,
[-1.5732e-02, 8.2550e-03, 7.4692e-03, ..., 1.2413e-02,
5.2185e-03, 8.8654e-03],
[-1.3863e-02, 2.3422e-03, 2.1393e-02, ..., -3.9444e-03,
1.2177e-02, 7.6714e-03],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
[-0.1016, -0.0195, -0.0424, ..., 0.0752, -0.0704, -0.0427],
[-0.0588, 0.0121, 0.0111, ..., 0.0136, -0.0375, -0.0212],
...,
[-0.0483, 0.0333, 0.0755, ..., -0.0151, -0.0658, 0.0333],
[-0.0512, -0.0161, 0.0559, ..., -0.0284, 0.0115, 0.0271],
[ 0.0211, -0.0159, -0.0198, ..., 0.0124, 0.0025, 0.0077]]])
2: tensor([[[ 1.0109e-01, -1.8457e-01, 1.5029e-01, ..., -1.7510e-01,
-1.9697e-01, -7.8638e-02],
[-2.0358e-03, -1.0349e-01, -2.0788e-02, ..., 1.1819e-01,
4.5131e-02, 4.3589e-03],
[-3.6629e-02, -5.1585e-03, 2.2301e-02, ..., 1.6284e-02,
-7.3033e-02, -3.2806e-02],
...,
[ 6.9765e-02, 5.2651e-03, -7.7284e-03, ..., -5.2996e-02,
-1.6080e-01, 6.9476e-02],
[-5.8378e-02, -1.8524e-02, 6.7024e-02, ..., -2.7891e-03,
2.2861e-02, 2.5625e-02],
[ 7.8680e-02, -2.2488e-02, -2.2393e-02, ..., 3.6071e-02,
-1.5761e-04, 2.5282e-03]]])
3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
[ 0.0063, -0.1105, 0.1225, ..., 0.1197, -0.1124, -0.1199],
[ 0.0570, -0.0177, 0.2330, ..., 0.1012, -0.1598, -0.1215],
...,
[ 0.2519, -0.0755, 0.0439, ..., 0.0778, 0.2336, -0.3418],
[-0.0329, -0.0525, 0.1533, ..., 0.0778, 0.0128, 0.0209],
[ 0.1499, -0.0507, 0.0177, ..., 0.0433, 0.0188, 0.0455]]])
4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
[-0.0628, -0.1661, 0.3024, ..., 0.2507, -0.1214, -0.0883],
[ 0.0489, -0.1734, 0.2030, ..., -0.0738, -0.2820, -0.1909],
...,
[ 0.1297, -0.0978, -0.2169, ..., 0.2739, 0.0133, -0.4061],
[-0.0750, 0.0389, 0.1304, ..., 0.0943, -0.0126, 0.0392],
[ 0.1359, -0.0421, 0.0174, ..., 0.0470, -0.0563, 0.0740]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
[ 0.1375, -0.0402, 0.2607, ..., 0.0044, -0.4515, -0.2382],
...,
[ 0.1538, -0.2687, -0.1527, ..., 0.3489, 0.0327, -0.5093],
[-0.0808, -0.0240, 0.0940, ..., -0.0243, -0.0718, 0.2346],
[ 0.1572, -0.0612, 0.0147, ..., 0.0015, -0.0021, 0.2792]]])
6: tensor([[[-2.3995e-01, -4.6932e-01, -2.1293e-01, ..., 3.7170e-01,
5.7616e-02, 2.8950e-01],
[-1.1711e-01, -3.2389e-01, 6.7573e-01, ..., 1.7863e-01,
-3.5874e-01, -2.3433e-01],
[-3.3049e-03, -2.6740e-01, 3.7956e-01, ..., -5.8433e-03,
-3.4639e-01, -3.8379e-01],
...,
[-2.8614e-02, -1.7990e-01, 9.6773e-02, ..., 3.0400e-01,
3.6502e-01, -6.4204e-01],
[-1.1027e-01, -1.7073e-01, 3.8821e-01, ..., 1.5272e-01,
2.3705e-01, -3.2543e-04],
[ 3.0266e-01, -2.0971e-01, 7.4628e-02, ..., 3.3301e-02,
7.5871e-02, 2.5270e-01]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
[-0.0632, -0.1760, 0.3805, ..., 0.1593, -0.3386, -0.4046],
...,
[ 0.2823, -0.5142, 0.2109, ..., 0.2586, 0.1342, -0.8073],
[-0.1339, -0.1098, 0.3622, ..., 0.1739, 0.1848, 0.0012],
[ 0.1760, -0.2389, 0.2656, ..., 0.0439, -0.1085, 0.2597]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
[-0.2766, -0.5257, 0.8512, ..., -0.0022, -0.4838, -0.1323],
[-0.0459, -0.1794, 0.4229, ..., 0.3498, -0.4034, -0.4652],
...,
[-0.0854, -0.7546, 0.3508, ..., 0.2079, 0.3151, -0.6979],
[-0.1874, -0.4891, 0.4208, ..., 0.0278, -0.0746, 0.0806],
[ 0.1975, -0.6238, 0.2692, ..., -0.3194, -0.6664, 0.2094]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
[-1.2754e-01, -7.2784e-01, 6.2181e-01, ..., -5.9251e-04,
-4.6773e-01, -2.0511e-01],
[-1.5388e-01, -3.8383e-01, 4.7796e-01, ..., 5.4677e-01,
-5.7957e-01, -3.6326e-01],
...,
[ 3.3322e-02, -5.5208e-01, 5.9400e-01, ..., 5.6603e-02,
7.2141e-01, -7.1338e-01],
[-9.6819e-02, -6.0358e-01, 1.5614e-01, ..., 2.8933e-01,
2.7485e-02, -1.5019e-02],
[-1.4347e-01, -9.5169e-01, 1.7807e-01, ..., -3.6655e-02,
-4.4095e-01, 2.4778e-01]]])
10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
[-0.1813, -0.8617, 0.3325, ..., -0.0257, -0.4533, -0.0284],
[-0.1423, -0.4132, 0.1746, ..., 0.3776, -0.6512, -0.4133],
...,
[ 0.2158, -0.7166, 0.8427, ..., 0.0344, 0.7364, -0.7681],
[-0.0055, -0.6054, -0.3283, ..., 0.2514, 0.0633, -0.1508],
[ 0.1764, -0.7712, 0.1779, ..., -0.1181, -0.5048, 0.0135]]])
11: tensor([[[ 0.1602, -0.9107, -0.5598, ..., 0.0381, -0.1310, 0.3694],
[-0.2011, -0.5420, 0.2242, ..., -0.2060, -0.5998, 0.2015],
[-0.0183, -0.6304, 0.1346, ..., 0.1680, -0.5393, -0.5965],
...,
[ 0.4358, -0.3692, 0.8106, ..., 0.1630, 1.2669, -0.7236],
[ 0.0712, -1.0783, -0.3073, ..., 0.4940, -0.1046, 0.1643],
[-0.0053, -0.8684, 0.4419, ..., 0.2037, -0.8565, -0.2148]]])
12: tensor([[[ 0.1804, -0.9203, -0.5095, ..., -0.0744, -0.1174, 0.3818],
[-0.4338, -0.7381, 0.0850, ..., -0.2924, -0.6647, 0.2480],
[-0.2238, -0.8725, 0.1604, ..., 0.4101, -0.5698, -0.4751],
...,
[ 0.1412, -0.2577, 0.8694, ..., -0.0827, 1.1512, -0.9579],
[ 0.0057, -1.0764, -0.6095, ..., 1.0204, 0.1155, 0.1074],
[-0.1054, -0.7850, 0.3492, ..., 0.1030, -1.1896, -0.1587]]])
13: tensor([[[ 2.5794e-01, -1.0105e+00, -5.3911e-01, ..., -3.4954e-02,
-2.0185e-01, 4.5267e-01],
[-6.4005e-01, -7.8039e-01, 1.3922e-03, ..., 1.2504e-01,
-3.1000e-01, 1.2849e-01],
[-6.0393e-02, -9.7953e-01, 4.2703e-01, ..., 1.7716e-01,
-6.6826e-01, -4.8370e-01],
...,
[-2.8366e-01, -2.7610e-01, 7.9779e-01, ..., -1.9170e-01,
1.4134e+00, -9.6282e-01],
[-3.2821e-01, -1.2620e+00, -5.6965e-01, ..., 9.3386e-01,
-2.2647e-01, -3.6721e-01],
[-3.7231e-01, -8.6096e-01, 2.1687e-01, ..., -3.4412e-01,
-1.4187e+00, 2.4747e-01]]])
14: tensor([[[ 0.2787, -1.0211, -0.4858, ..., -0.0717, -0.0655, 0.3964],
[-0.6753, -0.9677, 0.0407, ..., -0.0744, -0.4973, -0.2153],
[-0.2201, -0.9099, 0.2618, ..., 0.0895, -0.6634, -0.9823],
...,
[ 0.1518, 0.0353, 0.5404, ..., 0.0735, 1.2206, -1.1532],
[-0.1339, -1.4356, -0.0874, ..., 0.4293, 0.0254, -0.4904],
[ 0.0519, -1.1382, 0.1589, ..., -0.1454, -1.5666, 0.0085]]])
15: tensor([[[ 0.2775, -1.0251, -0.2647, ..., -0.0380, -0.0748, 0.5438],
[-0.2879, -1.1037, 0.4039, ..., -0.3520, -0.6702, -0.3490],
[ 0.0341, -1.1546, 0.3763, ..., -0.3480, -0.8019, -0.7203],
...,
[ 0.4179, -0.0613, 0.3381, ..., 0.2488, 1.6403, -1.0376],
[ 0.0043, -1.4951, -0.3074, ..., 0.5188, 0.2055, -0.4219],
[ 0.4067, -1.6352, -0.5835, ..., 0.1707, -1.6975, 0.4433]]])
16: tensor([[[ 0.3045, -1.0667, -0.2115, ..., -0.0522, -0.1122, 0.5783],
[-0.1683, -1.4639, 0.4694, ..., -0.1146, -0.9781, -0.5914],
[ 0.1871, -1.3984, 0.1202, ..., -0.1305, -0.6274, -0.8889],
...,
[ 0.3801, -0.4444, 0.1778, ..., -0.0398, 1.6738, -0.8500],
[-0.2363, -1.5528, -0.6290, ..., 0.3115, 0.3535, -0.3240],
[ 0.6485, -2.3032, -0.7009, ..., -0.2418, -1.8321, 0.8677]]])
17: tensor([[[ 0.3259, -1.0477, -0.2252, ..., 0.0335, -0.2652, 0.5971],
[-0.2339, -1.5909, 0.4839, ..., 0.0959, -1.0865, -0.6412],
[ 0.1523, -1.3746, -0.0511, ..., -0.2632, -0.5859, -0.7240],
...,
[ 0.5914, -0.6899, 0.1008, ..., 0.0997, 1.9314, -0.8540],
[ 0.0266, -1.6109, -0.8935, ..., 0.0390, 0.4864, -0.5276],
[ 0.3348, -1.8527, -0.4143, ..., 0.0705, -2.0230, 1.1469]]])
18: tensor([[[ 0.3030, -0.9936, -0.3282, ..., -0.0327, -0.3823, 0.7322],
[-0.1053, -1.5004, 0.2582, ..., -0.0130, -1.5225, -1.0915],
[ 0.3871, -1.3789, -0.0732, ..., 0.1195, -0.8012, -0.9241],
...,
[ 1.1251, -0.4460, 0.0435, ..., -0.0879, 1.8720, -0.5229],
[ 0.1666, -1.4643, -1.1891, ..., 0.4416, 0.0708, -0.6073],
[ 0.4989, -1.7270, -0.0673, ..., 0.6524, -2.6231, 0.5161]]])
19: tensor([[[ 0.3025, -1.2240, -0.1138, ..., 0.1373, -0.4463, 0.8681],
[ 0.3981, -1.5456, 0.3258, ..., -0.1875, -1.6000, -1.1540],
[ 0.4815, -1.4289, -0.0317, ..., -0.0071, -1.0063, -1.0037],
...,
[ 1.2183, -0.2838, -0.3662, ..., -0.6971, 2.4582, -0.6760],
[-0.0385, -2.2666, -1.7383, ..., 0.1665, 0.7334, -0.3126],
[ 0.8937, -1.9950, -0.8505, ..., 0.6518, -2.3599, 0.2995]]])
20: tensor([[[ 0.4686, -1.2657, -0.0419, ..., 0.0919, -0.3436, 0.8809],
[ 0.3482, -1.9316, -0.2961, ..., 0.0607, -1.7172, -0.8600],
[ 0.4599, -1.7459, -0.2516, ..., -0.1474, -1.0419, -0.7416],
...,
[ 1.5933, -0.5843, -0.3757, ..., -0.5810, 2.6339, -0.6939],
[-0.1284, -1.7194, -1.4731, ..., 0.4135, 0.8510, -0.2524],
[ 1.1016, -2.1314, -0.1456, ..., 0.5704, -2.2756, -0.1838]]])
21: tensor([[[ 0.4442, -1.0962, 0.0778, ..., 0.0597, -0.3985, 0.7814],
[ 0.0725, -1.7982, -0.3599, ..., -0.1201, -1.6566, -1.1994],
[ 0.3863, -1.8144, 0.2474, ..., 0.0598, -1.3684, -0.8320],
...,
[ 1.4767, -1.1923, -0.3724, ..., -1.0477, 3.1372, -1.1576],
[-0.7586, -2.4993, -1.5565, ..., 0.1866, 0.8714, -0.9087],
[ 1.1544, -2.2786, -0.4314, ..., 0.4523, -2.4855, -0.1664]]])
22: tensor([[[ 0.5059, -1.1660, 0.2683, ..., -0.0197, -0.3653, 0.8172],
[ 0.4792, -2.1132, 0.1638, ..., -0.0667, -2.0503, -0.8644],
[ 0.2983, -2.1457, 0.5011, ..., -0.4051, -1.3177, -0.6711],
...,
[ 1.2554, -1.0590, -0.2612, ..., -0.9433, 3.7405, -0.7184],
[-0.0339, -2.7306, -1.6950, ..., -0.0951, 1.2772, -0.8340],
[ 0.6576, -2.5540, 0.1099, ..., 0.3822, -2.3626, 0.7545]]])
23: tensor([[[ 0.4414, -1.2350, 0.1653, ..., 0.0749, -0.5487, 0.5796],
[ 0.9800, -2.0098, 0.3139, ..., -0.1216, -2.4571, -0.1834],
[ 0.1384, -2.1878, 0.6559, ..., -0.4342, -1.4843, -0.3502],
...,
[ 1.0159, -0.9987, -0.0394, ..., -1.1041, 3.6619, -0.8492],
[ 0.1595, -3.1461, -1.3301, ..., -0.9908, 0.8076, -1.1144],
[ 0.8278, -2.2327, -0.1386, ..., 0.2227, -2.1093, 0.4210]]])
24: tensor([[[ 0.4183, -1.1111, 0.1459, ..., 0.0492, -0.4547, 0.3954],
[ 0.5153, -1.9330, 0.0819, ..., -0.2930, -2.4385, 0.1704],
[ 0.1086, -1.9690, 0.3744, ..., -0.5352, -1.3599, -0.1157],
...,
[ 0.6124, -0.9998, -0.3308, ..., -1.3693, 4.1769, -1.0143],
[ 0.5745, -3.1421, -1.5267, ..., -1.7079, 1.4340, -0.7926],
[ 0.9268, -2.0843, 0.1567, ..., 0.4877, -2.2529, -0.0367]]])
25: tensor([[[ 0.4975, -1.1003, 0.2274, ..., 0.1581, -0.4560, 0.4482],
[ 0.4099, -2.1553, -0.0299, ..., -0.4242, -2.9810, 0.1285],
[ 0.1489, -1.9066, 0.3926, ..., -1.2392, -1.9434, -0.0387],
...,
[ 0.8564, -0.8415, -0.0996, ..., -1.6906, 4.5120, -1.1202],
[ 0.9566, -3.2228, -1.3688, ..., -2.5520, 1.5946, -0.7580],
[ 1.4266, -1.6986, 0.2565, ..., 0.2426, -2.3682, -0.6090]]])
26: tensor([[[ 0.4632, -1.1296, 0.2315, ..., 0.1767, -0.4738, 0.3622],
[ 0.3349, -2.0610, -0.2346, ..., -0.4175, -3.3164, 0.6413],
[ 0.3103, -2.0197, 0.5268, ..., -1.1227, -2.0411, -0.1146],
...,
[ 0.8931, -1.2821, -0.3206, ..., -1.2581, 3.8302, -0.6784],
[ 1.8422, -4.0961, -1.8966, ..., -2.0031, 1.4461, -1.6140],
[ 1.0544, -1.8964, -0.3920, ..., 0.1599, -2.7726, -0.2911]]])
27: tensor([[[ 0.5150, -1.0300, 0.1991, ..., 0.2264, -0.4491, 0.3086],
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39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
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40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
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Query: Die Studierenden sind in der Lage,
- ein kleines Projekt selbstständig zu managen,
- eine projektspezifische virtuelle Rechnerinfrastruktur zu entwerfen, zu implementieren und zu warten,
- fachliche und Anwendungsdomänen-spezifische Techiken und Methoden aus dem Projektumfeld anzuwenden
und damit Software-Lösungen zu entwickeln
Modulhandbuch Fakultät für Informatik
22
Embeddings:
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21: tensor([[[ 0.4442, -1.0962, 0.0778, ..., 0.0597, -0.3985, 0.7814],
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22: tensor([[[ 0.5059, -1.1660, 0.2683, ..., -0.0197, -0.3653, 0.8172],
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25: tensor([[[ 0.4975, -1.1003, 0.2274, ..., 0.1581, -0.4560, 0.4482],
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26: tensor([[[ 0.4632, -1.1296, 0.2315, ..., 0.1767, -0.4738, 0.3622],
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28: tensor([[[ 0.5767, -0.9113, 0.1827, ..., 0.2441, -0.5849, 0.2632],
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32: tensor([[[ 0.7360, -0.9609, 0.2117, ..., 0.1602, -0.8576, 0.1393],
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33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
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35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
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36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
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37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
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38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
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39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
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40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
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Query: - Was ist ein Projekt?
- Aufgaben- vs. Terminplanung
- Werkzeuge für Projektmanagement
- Qualitätsmanagement und Projektkontrolle
- Rollen im Projekt
- Klassisches Projektmanagement
- Agiles Projektmanagement (z. B. Scrum und KANBAN)
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
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-1.1311e-03, 9.9945e-03],
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[ 2.7695e-02, -5.2338e-03, 7.8049e-03, ..., -1.6937e-02,
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[-1.3863e-02, 2.3422e-03, 2.1393e-02, ..., -3.9444e-03,
1.2177e-02, 7.6714e-03],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
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1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
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2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
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3: tensor([[[-1.9800e-01, -3.3942e-01, -4.2058e-02, ..., 2.4201e-01,
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4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
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5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
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6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
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7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
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8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
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9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
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10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
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11: tensor([[[ 0.1602, -0.9107, -0.5598, ..., 0.0381, -0.1310, 0.3694],
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26: tensor([[[ 0.4632, -1.1296, 0.2315, ..., 0.1767, -0.4738, 0.3622],
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27: tensor([[[ 0.5150, -1.0300, 0.1991, ..., 0.2264, -0.4491, 0.3086],
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28: tensor([[[ 5.7670e-01, -9.1130e-01, 1.8270e-01, ..., 2.4412e-01,
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29: tensor([[[ 0.5881, -0.8365, 0.1993, ..., 0.2294, -0.7033, 0.1812],
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30: tensor([[[ 6.6669e-01, -9.4694e-01, 2.0135e-01, ..., 1.9567e-01,
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[ 8.2389e-01, -2.3207e+00, 6.9412e-02, ..., -7.6211e-01,
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[ 5.5186e-01, -1.9823e+00, 4.1869e-01, ..., -8.9906e-01,
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31: tensor([[[ 0.6655, -0.9598, 0.2447, ..., 0.1706, -0.7776, 0.1788],
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33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
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35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
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36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
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37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
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38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
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39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[-1.7814, -2.4368, -3.2770, ..., 2.5681, 3.5024, -4.5263],
[-1.4068, -3.2408, -4.1971, ..., -2.0128, -1.6525, -3.3318],
[ 0.5433, -1.3932, 3.4837, ..., -3.4471, -0.1755, -4.8689]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
[-0.2762, 0.5192, -1.0364, ..., -0.4324, -0.5417, 0.2538],
...,
[-0.7238, -0.6798, -2.2171, ..., 0.4116, 0.8068, -0.7546],
[-0.3050, -0.1054, -0.4711, ..., 0.3687, 0.3955, -0.4804],
[-0.5692, -0.5692, -0.1990, ..., -0.9908, -0.5485, -1.1331]]])
Query: - Parallelität und Nebenläufigkeit, Prozesse, Threads, Co-Routinen/Fibers
- Thread-Konzepte in Java
- kritische Abschnitte aufgrund konkurrierenden Zugriffs auf Daten in Java, gegenseitiger Ausschluss mit syn-
chronized (intrinsic lock)
- Deadlocks, Signalisieren (wait/notify) und Bedingungsvariablen (await/signal), Lebenszyklus von Threads, In-
terrupts
- Thread Pools, Callable, Future und FutureTask
- Java Speichermodell bei Nebenläufigkeit, Memory Barriers
- Locks (ReentrantLock, ReadWriteLock, StampedLock) und Semaphore
- synchronisierte, unmodifiable und concurrent Collections in Java
- atomare Variablen und ihre Verwendung
- weitere Architekturen und Frameworks für nebenläufige und parallele Programmierung in Java und ggf. ande-
ren Programmiersprachen: z. B. Actor Model, Communicating Sequential Processes, Fork-Join, Map-Reduce,
Parallel Streams, Software Transactional Memory, RxJava, Datenparallelismus: GPGPU (OpenCL oder CUDA)
- Performanzbetrachtungen
- Entwurfsmuster (für nebenläufige und parallele Programme)
- Praktische Übungen zur Implementierung asynchroner, nebenläufiger und paralleler Algorithmen mit Java und
ggf. anderen Programmiersprachen
Modulhandbuch Fakultät für Informatik
25
Embeddings:
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37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
[ 1.0674, -1.5709, 0.4484, ..., -2.0207, -2.4859, 0.1802],
...,
[-3.0407, -3.5352, -0.7436, ..., 2.3878, 0.6008, -1.0396],
[-3.0988, 1.4671, 1.7932, ..., 0.2465, 1.7020, 0.6110],
[-3.9784, 0.2454, -1.3444, ..., 3.3892, -2.1770, -0.3099]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
[ 0.5371, -1.7457, 0.3418, ..., -1.6540, -2.1705, -0.7345],
...,
[-2.7162, -3.6568, -0.2801, ..., 1.5569, -0.5864, -1.7826],
[-3.4980, 2.1337, 2.4223, ..., -0.2469, 0.1495, 0.8450],
[-3.5012, -0.3025, -0.9156, ..., 3.4120, -2.3986, -0.0935]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[-2.5299, -4.2639, -1.3151, ..., 2.8263, -2.0737, -2.3630],
[-3.3274, 0.3638, 1.5120, ..., 2.4251, -0.5633, -0.2580],
[-2.7056, -0.7525, -0.5690, ..., 4.6096, -3.4630, -0.1801]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
[-0.2762, 0.5192, -1.0364, ..., -0.4324, -0.5417, 0.2538],
...,
[-1.0000, -1.0259, -1.0993, ..., 0.2243, -0.8988, -0.2739],
[-1.2126, -0.2452, -0.8164, ..., 0.3338, -0.3592, 0.1959],
[-1.1320, -0.3460, -0.8497, ..., 0.7005, -0.4503, 0.3224]]])
Query: - Benutzung einer integrierten Entwicklungsumgebung (IDE), inklusive Debugger, Versionsverwaltung, automa-
tisierte Tests
- Konstrukte der imperativen Programmierung: Zuweisung, Bedingung, Schleife, Array, (statische) Methode und
Parameter, Rekursion, RuntimeException
- Speicherverwaltung (Stack und Heap)
- Konstrukte der objektorientierten Programmierung: Klasse, Objekt, Attribut, (dynamisch gebundene) Methode
- Interfaces und ihre Implementierung
- Vererbung, Polymorphismus
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
3.9864e-03, -2.9182e-03],
...,
[ 6.2561e-03, -4.3297e-03, -3.0716e-02, ..., -8.3923e-03,
-3.6287e-04, 6.7940e-03],
[ 1.3321e-02, -1.0612e-02, 1.9852e-02, ..., -1.3893e-02,
3.0884e-02, -3.7323e-02],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
[-0.1016, -0.0195, -0.0424, ..., 0.0752, -0.0704, -0.0427],
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...,
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[ 0.0569, 0.0220, 0.0354, ..., -0.1350, -0.0732, -0.0957],
[ 0.0043, -0.0112, -0.0243, ..., 0.0294, -0.0253, -0.0077]]])
2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
[-0.0020, -0.1035, -0.0208, ..., 0.1182, 0.0451, 0.0044],
[-0.0366, -0.0052, 0.0223, ..., 0.0163, -0.0730, -0.0328],
...,
[ 0.0800, -0.0466, -0.0686, ..., 0.0512, -0.0601, 0.0772],
[ 0.0996, -0.0679, -0.0053, ..., -0.0736, -0.1231, -0.1825],
[ 0.0512, -0.0066, -0.0150, ..., 0.0318, -0.0294, 0.0137]]])
3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
[ 0.0063, -0.1105, 0.1225, ..., 0.1197, -0.1124, -0.1199],
[ 0.0570, -0.0177, 0.2330, ..., 0.1012, -0.1598, -0.1215],
...,
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[ 0.3056, -0.1493, 0.1111, ..., -0.1605, -0.1693, -0.0652],
[ 0.0752, -0.0498, 0.0407, ..., 0.0182, -0.0362, 0.0728]]])
4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
[-0.0628, -0.1661, 0.3024, ..., 0.2507, -0.1214, -0.0883],
[ 0.0489, -0.1734, 0.2030, ..., -0.0738, -0.2820, -0.1909],
...,
[ 0.2406, 0.2166, -0.0931, ..., -0.0727, 0.2990, -0.1414],
[ 0.2364, -0.0223, 0.1975, ..., -0.3686, -0.0272, -0.0270],
[ 0.0629, -0.0562, -0.0035, ..., 0.1251, -0.0483, 0.1731]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
[ 0.1375, -0.0402, 0.2607, ..., 0.0044, -0.4515, -0.2382],
...,
[ 0.3552, -0.2941, -0.2620, ..., -0.0572, 0.2972, -0.1445],
[ 0.4218, -0.3178, 0.0473, ..., -0.5098, 0.0701, -0.0629],
[ 0.0367, -0.2105, -0.0292, ..., -0.0429, -0.0988, 0.1594]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
[-0.0033, -0.2674, 0.3796, ..., -0.0058, -0.3464, -0.3838],
...,
[ 0.3241, -0.3932, -0.3095, ..., 0.0098, 0.5435, -0.3743],
[ 0.5566, -0.2693, 0.2179, ..., -0.3628, 0.3396, -0.3939],
[-0.0548, 0.0085, 0.1298, ..., 0.0482, 0.0424, 0.2413]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
[-0.0632, -0.1760, 0.3805, ..., 0.1593, -0.3386, -0.4046],
...,
[ 0.0280, -0.4435, -0.6161, ..., 0.2994, 0.5601, -0.4625],
[ 0.4408, -0.2854, 0.0440, ..., -0.0597, 0.5932, -0.2005],
[-0.1465, 0.0153, 0.1947, ..., 0.2743, 0.0191, 0.0765]]])
8: tensor([[[ 1.7744e-01, -1.0197e+00, -5.5202e-01, ..., 2.1911e-01,
-7.4777e-02, 3.1519e-01],
[-2.7662e-01, -5.2568e-01, 8.5117e-01, ..., -2.2344e-03,
-4.8378e-01, -1.3226e-01],
[-4.5867e-02, -1.7939e-01, 4.2289e-01, ..., 3.4982e-01,
-4.0342e-01, -4.6520e-01],
...,
[ 8.4518e-02, -6.5072e-01, -6.7599e-01, ..., -2.8214e-04,
7.4727e-01, -3.2714e-01],
[ 7.8141e-01, -4.2100e-01, -3.8100e-01, ..., -2.7671e-01,
8.8057e-01, -3.9993e-01],
[-1.1617e-01, 1.5510e-02, 9.0144e-03, ..., 2.2656e-01,
1.8315e-01, 3.4409e-01]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
[-1.2754e-01, -7.2784e-01, 6.2181e-01, ..., -5.9251e-04,
-4.6773e-01, -2.0511e-01],
[-1.5388e-01, -3.8383e-01, 4.7796e-01, ..., 5.4677e-01,
-5.7957e-01, -3.6326e-01],
...,
[ 2.5890e-01, -8.1049e-01, -6.1663e-01, ..., 1.0131e-01,
8.8164e-01, -4.5695e-01],
[ 5.3196e-01, -4.2753e-01, -1.7606e-01, ..., -3.5020e-02,
9.5330e-01, -6.5877e-01],
[-2.6413e-01, 3.2955e-01, 3.2824e-01, ..., -2.4627e-01,
3.3329e-01, 3.1610e-01]]])
10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
[-0.1813, -0.8617, 0.3325, ..., -0.0257, -0.4533, -0.0284],
[-0.1423, -0.4132, 0.1746, ..., 0.3776, -0.6512, -0.4133],
...,
[ 0.1592, -0.4621, -0.7230, ..., 0.2490, 0.7610, -0.3683],
[ 0.5410, -0.1202, -0.5707, ..., -0.0120, 1.1636, -0.3637],
[-0.1953, 0.6078, 0.1845, ..., -0.2997, -0.0538, 0.3917]]])
11: tensor([[[ 0.1602, -0.9107, -0.5598, ..., 0.0381, -0.1310, 0.3694],
[-0.2011, -0.5420, 0.2242, ..., -0.2060, -0.5998, 0.2015],
[-0.0183, -0.6304, 0.1346, ..., 0.1680, -0.5393, -0.5965],
...,
[-0.2590, -0.4773, -0.8725, ..., 0.3637, 0.4276, -0.3591],
[ 0.0500, -0.0265, -0.5938, ..., -0.0889, 1.1189, -0.6880],
[-0.4658, 0.3202, 0.3378, ..., -0.2187, -0.2572, 0.4964]]])
12: tensor([[[ 0.1804, -0.9203, -0.5095, ..., -0.0744, -0.1174, 0.3818],
[-0.4338, -0.7381, 0.0850, ..., -0.2924, -0.6647, 0.2480],
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...,
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[ 0.1577, -0.3481, -0.4080, ..., -0.3570, 1.1442, -0.6109],
[-0.3515, -0.2615, 0.2388, ..., -0.5715, -0.1345, 0.3371]]])
13: tensor([[[ 2.5794e-01, -1.0105e+00, -5.3911e-01, ..., -3.4954e-02,
-2.0185e-01, 4.5267e-01],
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[-6.0393e-02, -9.7953e-01, 4.2703e-01, ..., 1.7716e-01,
-6.6826e-01, -4.8370e-01],
...,
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[ 1.4016e-01, -4.8973e-01, -4.5123e-01, ..., -9.8427e-02,
1.5187e+00, -5.9835e-01],
[-3.8934e-01, -7.7117e-01, -4.5089e-01, ..., -3.5220e-01,
-2.5865e-01, 4.2437e-01]]])
14: tensor([[[ 2.7874e-01, -1.0211e+00, -4.8583e-01, ..., -7.1688e-02,
-6.5471e-02, 3.9639e-01],
[-6.7533e-01, -9.6769e-01, 4.0668e-02, ..., -7.4380e-02,
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...,
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8.9348e-01, -6.9141e-01],
[ 6.2904e-01, -7.3386e-01, -6.3646e-01, ..., -4.0918e-01,
1.6825e+00, -6.4381e-01],
[ 4.3020e-05, -7.4651e-01, -2.3647e-01, ..., -3.2632e-01,
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15: tensor([[[ 0.2775, -1.0251, -0.2647, ..., -0.0380, -0.0748, 0.5438],
[-0.2879, -1.1037, 0.4039, ..., -0.3520, -0.6702, -0.3490],
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[ 0.1219, -0.8383, -0.7712, ..., -0.1337, -0.0850, 0.1830]]])
16: tensor([[[ 0.3045, -1.0667, -0.2115, ..., -0.0522, -0.1122, 0.5783],
[-0.1683, -1.4639, 0.4694, ..., -0.1146, -0.9781, -0.5914],
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17: tensor([[[ 0.3259, -1.0477, -0.2252, ..., 0.0335, -0.2652, 0.5971],
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18: tensor([[[ 0.3030, -0.9936, -0.3282, ..., -0.0327, -0.3823, 0.7322],
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19: tensor([[[ 3.0254e-01, -1.2240e+00, -1.1377e-01, ..., 1.3727e-01,
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20: tensor([[[ 0.4686, -1.2657, -0.0419, ..., 0.0919, -0.3436, 0.8809],
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24: tensor([[[ 0.4183, -1.1111, 0.1459, ..., 0.0492, -0.4547, 0.3954],
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25: tensor([[[ 4.9750e-01, -1.1003e+00, 2.2737e-01, ..., 1.5806e-01,
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26: tensor([[[ 0.4632, -1.1296, 0.2315, ..., 0.1767, -0.4738, 0.3622],
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...,
[ 0.9461, -1.6268, -2.5969, ..., -0.4555, 5.1858, -0.3664],
[-0.5695, -1.0239, -0.4632, ..., -1.9280, 3.3397, -0.1338],
[-0.6743, -2.5481, -0.7173, ..., -0.6093, -0.9483, 0.4634]]])
27: tensor([[[ 0.5150, -1.0300, 0.1991, ..., 0.2264, -0.4491, 0.3086],
[ 0.2726, -1.9265, -0.1144, ..., -0.5303, -3.5600, 0.5535],
[ 0.5457, -2.0804, 0.5186, ..., -1.0915, -1.9407, 0.1254],
...,
[ 0.2048, -1.4300, -2.8923, ..., -1.2770, 5.4169, -0.4145],
[-0.4119, -1.0215, -1.2594, ..., -2.1001, 3.8726, 0.4676],
[-1.2805, -3.1239, -0.6940, ..., -0.0609, -0.1481, 0.7465]]])
28: tensor([[[ 0.5767, -0.9113, 0.1827, ..., 0.2441, -0.5849, 0.2632],
[ 0.4596, -2.2676, 0.0314, ..., -0.7782, -3.3262, 0.7418],
[ 0.3451, -2.3089, 0.6245, ..., -0.9180, -1.8190, 0.1070],
...,
[ 0.2510, -2.0728, -2.4506, ..., -0.4274, 5.8959, -1.1278],
[-0.1026, -1.7113, -1.8288, ..., -1.6302, 3.6958, 0.9507],
[-0.8449, -4.0338, -0.3865, ..., 0.2650, -0.0129, 0.3348]]])
29: tensor([[[ 0.5881, -0.8365, 0.1993, ..., 0.2294, -0.7033, 0.1812],
[ 0.8169, -2.5907, 0.3456, ..., -0.9426, -3.3235, 0.5329],
[ 0.5339, -2.1013, 0.5908, ..., -1.0304, -1.7530, 0.1138],
...,
[ 0.4648, -2.4061, -3.0885, ..., -0.6934, 6.1237, -1.4505],
[-0.4175, -2.0168, -2.4647, ..., -1.5577, 4.0946, 0.9947],
[-0.7016, -4.0297, -1.0205, ..., 0.7088, -0.5293, 0.0598]]])
30: tensor([[[ 6.6669e-01, -9.4694e-01, 2.0135e-01, ..., 1.9567e-01,
-7.3406e-01, 1.7867e-01],
[ 8.2389e-01, -2.3207e+00, 6.9412e-02, ..., -7.6211e-01,
-3.7438e+00, 4.4429e-01],
[ 5.5186e-01, -1.9823e+00, 4.1869e-01, ..., -8.9906e-01,
-1.8399e+00, 4.5765e-03],
...,
[ 7.6819e-01, -2.8110e+00, -3.0332e+00, ..., -1.3859e+00,
5.5210e+00, -1.7975e+00],
[ 6.4893e-03, -1.6682e+00, -3.1926e+00, ..., -1.8952e+00,
4.7403e+00, 1.3783e+00],
[-6.3846e-01, -3.4325e+00, -1.0866e+00, ..., 3.9981e-01,
-3.1143e-01, -3.8755e-01]]])
31: tensor([[[ 0.6655, -0.9598, 0.2447, ..., 0.1706, -0.7776, 0.1788],
[ 0.6942, -2.3152, 0.0467, ..., -1.0002, -4.1053, 0.4592],
[ 0.4674, -1.8792, 0.4066, ..., -1.1411, -1.8999, 0.0861],
...,
[ 0.9365, -3.3496, -2.2535, ..., -1.6021, 5.2417, -0.6439],
[ 0.3313, -2.7476, -2.4680, ..., -2.5094, 5.3191, 2.0988],
[-1.1988, -3.2279, 0.2587, ..., -0.1979, -0.0204, -0.0547]]])
32: tensor([[[ 0.7360, -0.9609, 0.2117, ..., 0.1602, -0.8576, 0.1393],
[ 0.6757, -2.3709, -0.2111, ..., -1.0661, -3.9552, 0.4456],
[ 0.4939, -2.4154, 0.0644, ..., -1.1614, -1.8203, 0.1791],
...,
[ 0.1051, -2.5783, -1.3162, ..., -2.2203, 5.5938, -0.7008],
[ 0.3298, -2.6773, -2.0555, ..., -2.8349, 5.9639, 1.7339],
[-2.3857, -3.8535, 0.3930, ..., -0.7935, 0.3117, -0.6238]]])
33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
[ 0.6746, -2.3491, -0.5023, ..., -1.1778, -4.0825, 0.6369],
[ 0.3326, -2.5822, -0.0435, ..., -0.7961, -1.9972, 0.2151],
...,
[-0.3390, -2.2437, -1.7588, ..., -2.6167, 5.7788, -0.7034],
[-0.0386, -3.1246, -1.6888, ..., -2.9834, 6.5360, 2.5163],
[-2.4268, -2.5865, 0.3698, ..., -0.4222, 1.0693, 0.1332]]])
34: tensor([[[ 0.8220, -0.9002, 0.5914, ..., 0.1699, -0.9568, -0.2156],
[ 0.8671, -2.4411, -0.2519, ..., -1.0163, -3.7759, 0.2840],
[ 0.7680, -2.6552, 0.3849, ..., -0.8292, -1.9769, 0.5669],
...,
[-1.5630, -0.7159, -1.5077, ..., -2.1762, 5.5606, -0.7084],
[ 0.5190, -2.7534, -1.9859, ..., -3.1148, 6.3014, 2.4836],
[-1.7318, -1.9869, 0.9704, ..., -0.8831, 1.3717, 0.3941]]])
35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
[ 1.4832, -2.1899, -0.7593, ..., -0.9099, -4.0092, -0.0704],
[ 1.1589, -2.4299, -0.0361, ..., -0.9148, -2.3421, 0.3966],
...,
[-1.2289, 0.3995, -1.8059, ..., -2.2856, 4.5948, -1.1875],
[ 0.6242, -3.1415, -2.5143, ..., -2.6151, 6.9108, 2.9908],
[-1.7868, -1.8382, 0.6199, ..., -1.8920, 1.9807, -0.4720]]])
36: tensor([[[ 7.5168e-01, -8.3976e-01, 6.3699e-01, ..., -4.4778e-02,
-7.8688e-01, -4.0149e-01],
[ 1.8548e+00, -2.4676e+00, -7.0953e-01, ..., -1.1532e+00,
-4.4559e+00, -9.9967e-01],
[ 1.2417e+00, -1.9971e+00, 1.5681e-01, ..., -1.1543e+00,
-2.6090e+00, 1.2917e-01],
...,
[-2.7038e+00, 7.5318e-01, -1.2057e+00, ..., -2.1370e+00,
3.4907e+00, -2.0167e-03],
[ 9.2078e-01, -2.4160e+00, -3.0673e+00, ..., -2.6327e+00,
8.2092e+00, 3.2183e+00],
[-1.9186e+00, -1.9033e+00, 1.3054e+00, ..., -2.8160e+00,
2.7040e+00, -9.5225e-01]]])
37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
[ 1.0674, -1.5709, 0.4484, ..., -2.0207, -2.4859, 0.1802],
...,
[-2.9987, 0.1650, -1.8253, ..., -1.0058, 3.6995, -1.1422],
[ 1.8327, -2.7978, -1.4278, ..., -4.0595, 7.6579, 3.5732],
[-2.3813, -2.8849, 2.0098, ..., -2.2924, 3.0640, -1.3987]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
[ 0.5371, -1.7457, 0.3418, ..., -1.6540, -2.1705, -0.7345],
...,
[-3.6718, 0.0903, -1.2764, ..., -1.6568, 1.7425, -0.6367],
[ 3.9897, -2.8906, -1.7595, ..., -4.1426, 8.0339, 1.8401],
[-2.2911, -2.7331, 2.8687, ..., -3.6880, 4.2364, -1.9230]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[-4.0008, 0.1119, -1.7638, ..., -0.9381, 2.3339, -0.7242],
[ 4.6660, -3.1123, -0.6423, ..., -3.0073, 7.7007, 3.5662],
[-1.7475, -2.4602, 4.1187, ..., -3.0683, 3.0824, -3.1800]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
[-0.2762, 0.5192, -1.0364, ..., -0.4324, -0.5417, 0.2538],
...,
[-1.1242, 0.1363, -1.3021, ..., 0.7703, 0.9768, 0.0621],
[ 1.2506, -0.2049, -0.7179, ..., -0.1495, 2.1171, 1.0299],
[-0.9102, -0.5770, 0.0089, ..., -0.8650, 0.1478, -0.9033]]])
Query: - Objektorientierung / Klasse Object (Wiederholung) / Polymorphismus (Vertiefung)
- Innere Klassen und Lambdas
- Input/Output (Grundlagen)
- Exception Handling
- Grundlagen der Nebenläufigkeit (Threads)
- Generics (Grundlagen)
- Collection Framework
- Wichtige Datenstrukturen (Hash, Baum, Tries, Graphen)
- Sortieren
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
3.9864e-03, -2.9182e-03],
...,
[-1.1124e-02, -3.8567e-03, -1.9882e-02, ..., 2.8824e-02,
9.8724e-03, 4.8399e-04],
[ 4.5624e-03, 1.9089e-02, -1.4099e-02, ..., -8.1711e-03,
2.7618e-02, 1.0548e-03],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
[-0.1016, -0.0195, -0.0424, ..., 0.0752, -0.0704, -0.0427],
[-0.0588, 0.0121, 0.0111, ..., 0.0136, -0.0375, -0.0212],
...,
[ 0.0732, 0.0485, 0.0653, ..., 0.0432, 0.0350, 0.0130],
[-0.0307, 0.0438, -0.0632, ..., 0.0151, 0.0664, -0.0311],
[ 0.0161, -0.0189, -0.0315, ..., 0.0200, -0.0306, 0.0144]]])
2: tensor([[[ 0.1011, -0.1846, 0.1503, ..., -0.1751, -0.1970, -0.0786],
[-0.0020, -0.1035, -0.0208, ..., 0.1182, 0.0451, 0.0044],
[-0.0366, -0.0052, 0.0223, ..., 0.0163, -0.0730, -0.0328],
...,
[ 0.1091, 0.1661, 0.0919, ..., -0.0346, 0.1283, 0.0218],
[ 0.1068, -0.0254, -0.0553, ..., -0.0178, 0.2559, -0.0233],
[ 0.0576, -0.0135, -0.0221, ..., 0.0191, -0.0328, 0.0281]]])
3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
[ 0.0063, -0.1105, 0.1225, ..., 0.1197, -0.1124, -0.1199],
[ 0.0570, -0.0177, 0.2330, ..., 0.1012, -0.1598, -0.1215],
...,
[ 0.1259, 0.1863, 0.0966, ..., -0.1241, 0.1698, -0.1208],
[ 0.0561, 0.0732, -0.0688, ..., 0.1623, 0.1871, -0.1743],
[ 0.0446, -0.0252, 0.0175, ..., 0.0368, -0.0098, 0.0648]]])
4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
[-0.0628, -0.1661, 0.3024, ..., 0.2507, -0.1214, -0.0883],
[ 0.0489, -0.1734, 0.2030, ..., -0.0738, -0.2820, -0.1909],
...,
[ 0.0418, 0.2575, 0.1938, ..., -0.2245, 0.0083, -0.1040],
[-0.0204, -0.0409, -0.1317, ..., 0.0913, 0.0630, -0.1711],
[ 0.0200, 0.0326, 0.0342, ..., 0.1544, -0.0285, 0.2029]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
[ 0.1375, -0.0402, 0.2607, ..., 0.0044, -0.4515, -0.2382],
...,
[ 0.2676, 0.2238, 0.1405, ..., -0.1585, 0.0723, -0.0110],
[ 0.2044, -0.0642, -0.0513, ..., -0.0519, 0.0948, -0.3327],
[ 0.0330, -0.0797, 0.0305, ..., 0.0616, -0.2734, 0.3169]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
[-0.0033, -0.2674, 0.3796, ..., -0.0058, -0.3464, -0.3838],
...,
[ 0.2740, 0.2273, 0.0546, ..., 0.0617, 0.1789, -0.0541],
[ 0.3264, -0.2038, 0.0354, ..., -0.2102, 0.3165, -0.1533],
[ 0.1048, -0.0021, 0.0236, ..., 0.2160, 0.0118, 0.4239]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
[-0.0632, -0.1760, 0.3805, ..., 0.1593, -0.3386, -0.4046],
...,
[-0.0475, 0.3918, -0.0848, ..., 0.3929, 0.0753, -0.2622],
[ 0.3104, -0.2680, -0.1103, ..., -0.1492, 0.3801, -0.2749],
[ 0.2077, 0.1219, -0.1288, ..., 0.4683, -0.0701, 0.4362]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
[-0.2766, -0.5257, 0.8512, ..., -0.0022, -0.4838, -0.1323],
[-0.0459, -0.1794, 0.4229, ..., 0.3498, -0.4034, -0.4652],
...,
[-0.0824, 0.4019, -0.3113, ..., 0.2223, -0.1239, -0.3079],
[ 0.0579, -0.4193, 0.0642, ..., -0.1835, 0.3386, -0.0502],
[ 0.3664, 0.1458, -0.0681, ..., 0.3966, -0.0401, 0.4465]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
[-1.2754e-01, -7.2784e-01, 6.2181e-01, ..., -5.9251e-04,
-4.6773e-01, -2.0511e-01],
[-1.5388e-01, -3.8383e-01, 4.7796e-01, ..., 5.4677e-01,
-5.7957e-01, -3.6326e-01],
...,
[-2.1358e-01, 4.2109e-01, 1.4458e-01, ..., -4.1807e-02,
-2.7003e-01, -2.8965e-01],
[-3.0632e-01, -1.6333e-01, 1.0756e-01, ..., -5.0102e-01,
1.8164e-01, -2.4610e-01],
[-1.6808e-01, 3.5729e-01, -1.9740e-01, ..., 4.5758e-02,
5.2039e-02, 2.7813e-01]]])
10: tensor([[[ 0.1494, -0.9942, -0.5780, ..., 0.0968, -0.0897, 0.2783],
[-0.1813, -0.8617, 0.3325, ..., -0.0257, -0.4533, -0.0284],
[-0.1423, -0.4132, 0.1746, ..., 0.3776, -0.6512, -0.4133],
...,
[-0.5016, 0.5276, 0.3060, ..., -0.0342, -0.4063, -0.1651],
[-0.6579, -0.2180, 0.2428, ..., -0.5411, 0.0017, -0.1993],
[-0.1988, 0.6119, 0.0011, ..., -0.0212, -0.1719, 0.5409]]])
11: tensor([[[ 0.1602, -0.9107, -0.5598, ..., 0.0381, -0.1310, 0.3694],
[-0.2011, -0.5420, 0.2242, ..., -0.2060, -0.5998, 0.2015],
[-0.0183, -0.6304, 0.1346, ..., 0.1680, -0.5393, -0.5965],
...,
[-0.6150, 0.6454, 0.1048, ..., 0.1543, -0.4232, 0.0096],
[-0.7571, -0.3709, 0.2068, ..., -0.6872, 0.1126, -0.0165],
[-0.2756, 0.1479, 0.1874, ..., -0.0460, -0.2853, 0.4575]]])
12: tensor([[[ 0.1804, -0.9203, -0.5095, ..., -0.0744, -0.1174, 0.3818],
[-0.4338, -0.7381, 0.0850, ..., -0.2924, -0.6647, 0.2480],
[-0.2238, -0.8725, 0.1604, ..., 0.4101, -0.5698, -0.4751],
...,
[-0.9043, 0.7029, -0.0707, ..., 0.0192, -0.3001, -0.2176],
[-1.0980, -0.1920, 0.3010, ..., -0.7290, 0.1681, -0.0330],
[-0.5303, -0.1746, -0.0272, ..., -0.0065, -0.4026, 0.2335]]])
13: tensor([[[ 0.2579, -1.0105, -0.5391, ..., -0.0350, -0.2018, 0.4527],
[-0.6401, -0.7804, 0.0014, ..., 0.1250, -0.3100, 0.1285],
[-0.0604, -0.9795, 0.4270, ..., 0.1772, -0.6683, -0.4837],
...,
[-0.9794, 0.6397, 0.2401, ..., 0.2164, -0.8089, -0.1613],
[-0.7243, -0.6048, 0.4554, ..., -0.4543, -0.0554, 0.3223],
[-0.5913, -0.5614, -0.4180, ..., 0.0067, -0.9703, 0.7093]]])
14: tensor([[[ 0.2787, -1.0211, -0.4858, ..., -0.0717, -0.0655, 0.3964],
[-0.6753, -0.9677, 0.0407, ..., -0.0744, -0.4973, -0.2153],
[-0.2201, -0.9099, 0.2618, ..., 0.0895, -0.6634, -0.9823],
...,
[-1.0507, 0.8241, 0.1236, ..., 0.5004, -0.7215, -0.4933],
[-0.7812, -0.6984, 0.2745, ..., -0.4809, 0.0322, -0.1105],
[-0.5272, -0.3591, -0.2951, ..., 0.0804, -0.6102, 0.8131]]])
15: tensor([[[ 0.2775, -1.0251, -0.2647, ..., -0.0380, -0.0748, 0.5438],
[-0.2879, -1.1037, 0.4039, ..., -0.3520, -0.6702, -0.3490],
[ 0.0341, -1.1546, 0.3763, ..., -0.3480, -0.8019, -0.7203],
...,
[-1.1496, 0.4257, 0.0253, ..., 0.9817, -0.9141, -1.0957],
[-1.1422, -1.2707, 0.5094, ..., -0.3575, -0.1564, -0.5091],
[-0.4328, -0.8317, -0.2686, ..., 0.3189, -0.5433, 0.3253]]])
16: tensor([[[ 0.3045, -1.0667, -0.2115, ..., -0.0522, -0.1122, 0.5783],
[-0.1683, -1.4639, 0.4694, ..., -0.1146, -0.9781, -0.5914],
[ 0.1871, -1.3984, 0.1202, ..., -0.1305, -0.6274, -0.8889],
...,
[-1.0691, 0.1546, 0.0364, ..., 0.8520, -0.7987, -1.2576],
[-1.1905, -1.4252, 0.5335, ..., -0.7083, 0.2909, -1.0509],
[-0.5749, -1.7731, -0.3212, ..., 0.1966, -0.6330, 0.1943]]])
17: tensor([[[ 0.3259, -1.0477, -0.2252, ..., 0.0335, -0.2652, 0.5971],
[-0.2339, -1.5909, 0.4839, ..., 0.0959, -1.0865, -0.6412],
[ 0.1523, -1.3746, -0.0511, ..., -0.2632, -0.5859, -0.7240],
...,
[-1.9403, 0.1400, -0.0853, ..., 0.2410, -0.8420, -0.9197],
[-1.6487, -1.5584, 0.9137, ..., -1.0453, 0.2916, -0.9138],
[-0.8455, -2.0883, -0.3197, ..., -0.0803, -0.0311, 0.0692]]])
18: tensor([[[ 3.0301e-01, -9.9364e-01, -3.2816e-01, ..., -3.2686e-02,
-3.8228e-01, 7.3221e-01],
[-1.0528e-01, -1.5004e+00, 2.5818e-01, ..., -1.2973e-02,
-1.5225e+00, -1.0915e+00],
[ 3.8707e-01, -1.3789e+00, -7.3249e-02, ..., 1.1954e-01,
-8.0118e-01, -9.2410e-01],
...,
[-2.0543e+00, 1.2894e-01, 7.6079e-02, ..., 3.0637e-02,
-7.5511e-01, -1.2116e+00],
[-2.0635e+00, -2.1441e+00, 7.5517e-01, ..., -1.1357e+00,
1.3999e-03, -1.5936e+00],
[-8.4305e-01, -2.2415e+00, -3.1812e-01, ..., -1.8082e-02,
-9.1467e-01, -3.3262e-01]]])
19: tensor([[[ 0.3025, -1.2240, -0.1138, ..., 0.1373, -0.4463, 0.8681],
[ 0.3981, -1.5456, 0.3258, ..., -0.1875, -1.6000, -1.1540],
[ 0.4815, -1.4289, -0.0317, ..., -0.0071, -1.0063, -1.0037],
...,
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[-2.0168, -1.9778, 0.7610, ..., -1.4703, 0.3061, -2.0790],
[-0.7303, -2.6668, -1.2506, ..., -0.0336, -0.9943, -0.2626]]])
20: tensor([[[ 0.4686, -1.2657, -0.0419, ..., 0.0919, -0.3436, 0.8809],
[ 0.3482, -1.9316, -0.2961, ..., 0.0607, -1.7172, -0.8600],
[ 0.4599, -1.7459, -0.2516, ..., -0.1474, -1.0419, -0.7416],
...,
[-2.2655, 0.3622, 0.1321, ..., -0.8097, -0.4130, -0.9599],
[-2.2290, -2.0926, 0.6436, ..., -2.2932, 1.0268, -2.1751],
[-0.4251, -2.5467, -1.1228, ..., -0.3014, -1.0179, -0.0983]]])
21: tensor([[[ 0.4442, -1.0962, 0.0778, ..., 0.0597, -0.3985, 0.7814],
[ 0.0725, -1.7982, -0.3599, ..., -0.1201, -1.6566, -1.1994],
[ 0.3863, -1.8144, 0.2474, ..., 0.0598, -1.3684, -0.8320],
...,
[-2.5674, 0.9983, 0.3120, ..., -0.1532, -0.2720, -0.6254],
[-1.6568, -1.6883, 0.5432, ..., -2.1147, 0.6790, -2.0657],
[-0.7209, -3.0272, -0.8493, ..., 0.0682, -0.7501, 0.3293]]])
22: tensor([[[ 0.5059, -1.1660, 0.2683, ..., -0.0197, -0.3653, 0.8172],
[ 0.4792, -2.1132, 0.1638, ..., -0.0667, -2.0503, -0.8644],
[ 0.2983, -2.1457, 0.5011, ..., -0.4051, -1.3177, -0.6711],
...,
[-2.3482, 1.5388, 0.2913, ..., -0.6633, -0.5806, -0.9555],
[-1.2798, -1.1474, 0.4292, ..., -2.6196, 0.5422, -2.0510],
[-1.2325, -3.5856, 0.0960, ..., 0.3915, -0.7882, 0.7637]]])
23: tensor([[[ 0.4414, -1.2350, 0.1653, ..., 0.0749, -0.5487, 0.5796],
[ 0.9800, -2.0098, 0.3139, ..., -0.1216, -2.4571, -0.1834],
[ 0.1384, -2.1878, 0.6559, ..., -0.4342, -1.4843, -0.3502],
...,
[-1.7668, 0.9082, 0.2716, ..., -1.3003, -0.5688, -1.2775],
[-0.7629, -1.8067, 1.1208, ..., -3.2678, 0.6296, -2.4400],
[-1.4137, -3.7866, -0.2553, ..., 0.2326, -0.5432, 0.0102]]])
24: tensor([[[ 0.4183, -1.1111, 0.1459, ..., 0.0492, -0.4547, 0.3954],
[ 0.5153, -1.9330, 0.0819, ..., -0.2930, -2.4385, 0.1704],
[ 0.1086, -1.9690, 0.3744, ..., -0.5352, -1.3599, -0.1157],
...,
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[-1.7606, -4.3886, 0.4549, ..., 0.2816, -0.5814, 0.0374]]])
25: tensor([[[ 0.4975, -1.1003, 0.2274, ..., 0.1581, -0.4560, 0.4482],
[ 0.4099, -2.1553, -0.0299, ..., -0.4242, -2.9810, 0.1285],
[ 0.1489, -1.9066, 0.3926, ..., -1.2392, -1.9434, -0.0387],
...,
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[ 0.3494, -2.4886, 1.1520, ..., -4.4449, 1.4402, -2.2029],
[-1.4361, -4.4351, -0.0831, ..., 0.1185, -0.9469, 0.0299]]])
26: tensor([[[ 0.4632, -1.1296, 0.2315, ..., 0.1767, -0.4738, 0.3622],
[ 0.3349, -2.0610, -0.2346, ..., -0.4175, -3.3164, 0.6413],
[ 0.3103, -2.0197, 0.5268, ..., -1.1227, -2.0411, -0.1146],
...,
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[ 0.2013, -3.5592, 1.3700, ..., -4.3174, 2.0388, -1.9853],
[-1.9346, -4.6831, -0.3329, ..., 0.0449, -1.1165, 0.4372]]])
27: tensor([[[ 0.5150, -1.0300, 0.1991, ..., 0.2264, -0.4491, 0.3086],
[ 0.2726, -1.9265, -0.1144, ..., -0.5303, -3.5600, 0.5535],
[ 0.5457, -2.0804, 0.5186, ..., -1.0915, -1.9407, 0.1254],
...,
[-2.3325, 0.7186, -0.1483, ..., -3.5479, 0.1557, -0.4809],
[-0.1673, -3.5220, 1.6996, ..., -4.6231, 2.8415, -2.0087],
[-2.7885, -5.6649, -0.1554, ..., 0.7761, -0.5413, 0.8194]]])
28: tensor([[[ 0.5767, -0.9113, 0.1827, ..., 0.2441, -0.5849, 0.2632],
[ 0.4596, -2.2676, 0.0314, ..., -0.7782, -3.3262, 0.7418],
[ 0.3451, -2.3089, 0.6245, ..., -0.9180, -1.8190, 0.1070],
...,
[-2.9213, 0.8622, 0.0366, ..., -2.5718, 0.3982, 0.0598],
[ 0.7308, -4.1832, 0.9611, ..., -4.0884, 2.6815, -1.7256],
[-2.6173, -6.2009, 0.0124, ..., 1.0309, -0.4783, 0.7504]]])
29: tensor([[[ 0.5881, -0.8365, 0.1993, ..., 0.2294, -0.7033, 0.1812],
[ 0.8169, -2.5907, 0.3456, ..., -0.9426, -3.3235, 0.5329],
[ 0.5339, -2.1013, 0.5908, ..., -1.0304, -1.7530, 0.1138],
...,
[-3.7560, 0.8966, 1.0644, ..., -1.9264, -0.0341, -0.7098],
[ 0.5135, -4.7927, 0.8974, ..., -4.7578, 2.6969, -1.9701],
[-2.6377, -6.9491, 0.1547, ..., 1.0725, -0.8192, 0.8020]]])
30: tensor([[[ 6.6669e-01, -9.4694e-01, 2.0135e-01, ..., 1.9567e-01,
-7.3406e-01, 1.7867e-01],
[ 8.2389e-01, -2.3207e+00, 6.9412e-02, ..., -7.6211e-01,
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[ 5.5186e-01, -1.9823e+00, 4.1869e-01, ..., -8.9906e-01,
-1.8399e+00, 4.5765e-03],
...,
[-2.7189e+00, 1.5297e+00, 5.3596e-01, ..., -2.7136e+00,
5.5576e-02, -1.2131e-01],
[ 1.8410e+00, -4.2821e+00, -4.1814e-02, ..., -5.0310e+00,
2.4137e+00, -2.2572e+00],
[-2.5163e+00, -6.7883e+00, 5.6816e-02, ..., 8.0273e-01,
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31: tensor([[[ 0.6655, -0.9598, 0.2447, ..., 0.1706, -0.7776, 0.1788],
[ 0.6942, -2.3152, 0.0467, ..., -1.0002, -4.1053, 0.4592],
[ 0.4674, -1.8792, 0.4066, ..., -1.1411, -1.8999, 0.0861],
...,
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[ 2.3085, -4.3298, 0.6922, ..., -6.0489, 4.5468, -0.6909],
[-3.0517, -6.9670, 0.9329, ..., 0.4200, -0.1750, 0.6934]]])
32: tensor([[[ 0.7360, -0.9609, 0.2117, ..., 0.1602, -0.8576, 0.1393],
[ 0.6757, -2.3709, -0.2111, ..., -1.0661, -3.9552, 0.4456],
[ 0.4939, -2.4154, 0.0644, ..., -1.1614, -1.8203, 0.1791],
...,
[-3.0932, 1.8472, 1.3436, ..., -2.5856, 2.0289, 0.3730],
[ 2.8364, -4.5040, 1.2955, ..., -6.4404, 5.1860, -0.5055],
[-3.7866, -7.4020, 1.0394, ..., -0.4494, 0.4871, -0.0180]]])
33: tensor([[[ 0.7091, -0.9420, 0.2560, ..., 0.1747, -0.9458, 0.0255],
[ 0.6746, -2.3491, -0.5023, ..., -1.1778, -4.0825, 0.6369],
[ 0.3326, -2.5822, -0.0435, ..., -0.7961, -1.9972, 0.2151],
...,
[-3.1297, 1.5529, 1.2689, ..., -2.4881, 2.5778, 1.3647],
[ 2.3723, -3.8893, 1.2189, ..., -6.6839, 6.7913, -0.2089],
[-3.6101, -6.4454, 0.8870, ..., -0.4090, 1.0753, 0.1406]]])
34: tensor([[[ 0.8220, -0.9002, 0.5914, ..., 0.1699, -0.9568, -0.2156],
[ 0.8671, -2.4411, -0.2519, ..., -1.0163, -3.7759, 0.2840],
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...,
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[ 2.8076, -3.7236, 2.2608, ..., -6.4842, 7.4982, -0.3948],
[-3.2063, -5.7788, 1.3985, ..., -0.6868, 1.4435, 0.1758]]])
35: tensor([[[ 0.8000, -0.7871, 0.4189, ..., 0.1287, -1.0022, -0.4476],
[ 1.4832, -2.1899, -0.7593, ..., -0.9099, -4.0092, -0.0704],
[ 1.1589, -2.4299, -0.0361, ..., -0.9148, -2.3421, 0.3966],
...,
[-3.8838, 1.4908, 3.5305, ..., -2.2892, 2.3163, 1.9528],
[ 2.7771, -2.7134, 2.6322, ..., -6.1782, 7.0145, -0.4395],
[-3.3140, -5.5467, 1.0786, ..., -1.2398, 1.8297, -0.7322]]])
36: tensor([[[ 0.7517, -0.8398, 0.6370, ..., -0.0448, -0.7869, -0.4015],
[ 1.8548, -2.4676, -0.7095, ..., -1.1532, -4.4559, -0.9997],
[ 1.2417, -1.9971, 0.1568, ..., -1.1543, -2.6090, 0.1292],
...,
[-3.3244, 0.9927, 2.1697, ..., -1.1160, 2.7001, 2.5566],
[ 3.0938, -3.5481, 1.5167, ..., -5.0715, 7.2830, -0.2809],
[-3.8090, -5.9747, 1.6805, ..., -0.7902, 1.0881, -1.2745]]])
37: tensor([[[ 0.9170, -0.7245, 0.6230, ..., 0.2104, -0.5517, -0.6868],
[ 2.0762, -2.1682, -0.7587, ..., -1.2677, -4.5423, -1.7139],
[ 1.0674, -1.5709, 0.4484, ..., -2.0207, -2.4859, 0.1802],
...,
[-3.3346, 1.2740, 2.4560, ..., -3.8123, 2.7460, 1.8981],
[ 3.9292, -3.8988, 2.3850, ..., -5.9698, 7.0159, 0.3647],
[-4.4105, -6.6289, 2.0748, ..., -0.5029, 1.2148, -1.7233]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 2.5775, -2.4292, -0.5681, ..., -2.6297, -4.4835, -1.6905],
[ 0.5371, -1.7457, 0.3418, ..., -1.6540, -2.1705, -0.7345],
...,
[-4.0657, 2.0587, 2.2792, ..., -4.5263, 2.2500, 0.9159],
[ 4.4627, -3.1364, 2.0669, ..., -5.9639, 6.7735, -0.2473],
[-4.0757, -6.7517, 2.9971, ..., -1.4928, 1.8700, -2.1118]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 4.3342, -2.8413, -0.6374, ..., -3.1586, -6.8024, -5.8151],
[ 0.7265, -1.4031, -0.5680, ..., -0.7173, -3.7234, -1.2236],
...,
[-4.4317, 2.1029, 3.5822, ..., -4.2842, 1.3274, 1.0417],
[ 5.8361, -3.0692, 2.4456, ..., -5.5931, 4.8369, -0.0702],
[-3.6127, -6.9234, 3.7957, ..., -0.0864, 0.4049, -3.5022]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[ 0.0981, -0.3628, 0.1940, ..., -1.1206, -2.0881, -1.0290],
[-0.2762, 0.5192, -1.0364, ..., -0.4324, -0.5417, 0.2538],
...,
[-1.3268, 0.3561, -0.3508, ..., -1.4519, 0.2370, 0.4414],
[ 0.8233, -0.5830, -0.7366, ..., -1.2362, 0.7739, -0.3459],
[-1.2161, -1.2070, -0.1309, ..., -0.4295, -0.6221, -0.8686]]])
Query: - Multi-Paradigmen-Sprachen (z. B. Scala)
- Funkionale Sprachen (z. B. Elixir, Lisp, Clojure)
- Sprachen mit explizitem Speichermanagement (C)
- Systemnahe Sprachen (z. B. Go)
- Skriptsprachen (Python, Ruby)
- Domainspezifische Sprachen
- Weiterführende Konzepte von Java (z. B. Streams, Reflection)
Embeddings:
0: tensor([[[ 2.2650e-06, 3.6716e-05, 6.0701e-04, ..., 4.8399e-04,
-9.8169e-05, 3.8338e-04],
[-5.5008e-03, 1.5114e-02, -2.7969e-02, ..., 1.7380e-02,
-1.1311e-03, 9.9945e-03],
[-6.8779e-03, 4.1914e-04, 1.5930e-02, ..., -7.7248e-04,
3.9864e-03, -2.9182e-03],
...,
[-3.5645e-02, 2.0111e-02, 2.6886e-02, ..., -1.4977e-02,
-1.8372e-02, 3.5706e-03],
[-1.3863e-02, 2.3422e-03, 2.1393e-02, ..., -3.9444e-03,
1.2177e-02, 7.6714e-03],
[-9.4843e-04, -3.2687e-04, 8.7738e-03, ..., -1.9464e-03,
-1.6296e-04, -9.8586e-05]]])
1: tensor([[[ 0.0379, -0.0566, -0.0294, ..., -0.0225, -0.0262, 0.0090],
[-0.1016, -0.0195, -0.0424, ..., 0.0752, -0.0704, -0.0427],
[-0.0588, 0.0121, 0.0111, ..., 0.0136, -0.0375, -0.0212],
...,
[-0.0539, 0.0234, 0.0295, ..., 0.0394, -0.0169, 0.0320],
[-0.0528, -0.0202, 0.0342, ..., -0.0181, 0.0266, 0.0528],
[-0.0008, -0.0312, -0.0167, ..., 0.0321, -0.0076, -0.0084]]])
2: tensor([[[ 1.0109e-01, -1.8457e-01, 1.5029e-01, ..., -1.7510e-01,
-1.9697e-01, -7.8638e-02],
[-2.0358e-03, -1.0349e-01, -2.0788e-02, ..., 1.1819e-01,
4.5131e-02, 4.3589e-03],
[-3.6629e-02, -5.1585e-03, 2.2301e-02, ..., 1.6284e-02,
-7.3033e-02, -3.2806e-02],
...,
[-1.3411e-01, -1.0021e-01, 1.0940e-01, ..., 1.7196e-02,
1.2877e-01, 1.0235e-01],
[-7.0185e-02, 1.9103e-03, 2.3719e-02, ..., 3.3185e-05,
4.3885e-02, 8.4421e-02],
[ 4.0001e-02, -1.5107e-02, -3.4696e-02, ..., 1.7531e-02,
2.1363e-03, 1.5773e-02]]])
3: tensor([[[-0.1980, -0.3394, -0.0421, ..., 0.2420, -0.0018, 0.2364],
[ 0.0063, -0.1105, 0.1225, ..., 0.1197, -0.1124, -0.1199],
[ 0.0570, -0.0177, 0.2330, ..., 0.1012, -0.1598, -0.1215],
...,
[-0.0947, -0.3167, -0.0160, ..., 0.0364, 0.1614, 0.1278],
[-0.0837, -0.0379, 0.0903, ..., -0.0051, 0.0063, 0.0519],
[ 0.0990, -0.0416, -0.0142, ..., -0.0202, -0.0278, 0.0754]]])
4: tensor([[[-0.2354, -0.3767, -0.0703, ..., 0.2630, 0.0199, 0.2768],
[-0.0628, -0.1661, 0.3024, ..., 0.2507, -0.1214, -0.0883],
[ 0.0489, -0.1734, 0.2030, ..., -0.0738, -0.2820, -0.1909],
...,
[-0.0236, -0.1735, -0.0229, ..., 0.0350, 0.0865, 0.1435],
[-0.1242, 0.0061, 0.0485, ..., -0.0154, 0.0669, 0.2496],
[ 0.0378, -0.0600, -0.0195, ..., -0.0049, -0.0730, 0.1328]]])
5: tensor([[[-0.2671, -0.4367, -0.1028, ..., 0.2781, 0.0093, 0.2576],
[-0.0691, -0.2446, 0.4844, ..., 0.1594, -0.1271, 0.0336],
[ 0.1375, -0.0402, 0.2607, ..., 0.0044, -0.4515, -0.2382],
...,
[-0.3243, -0.2916, -0.0524, ..., -0.2105, 0.2149, 0.0299],
[-0.1683, -0.1164, -0.0465, ..., -0.1263, 0.0761, 0.2675],
[ 0.1324, -0.0823, -0.1249, ..., -0.1685, -0.0479, 0.2707]]])
6: tensor([[[-0.2400, -0.4693, -0.2129, ..., 0.3717, 0.0576, 0.2895],
[-0.1171, -0.3239, 0.6757, ..., 0.1786, -0.3587, -0.2343],
[-0.0033, -0.2674, 0.3796, ..., -0.0058, -0.3464, -0.3838],
...,
[-0.2527, -0.3954, 0.1298, ..., -0.2043, 0.1756, 0.0211],
[-0.1384, -0.0366, -0.1062, ..., 0.0781, 0.2537, 0.2132],
[ 0.2093, -0.0538, -0.1852, ..., -0.0557, 0.0657, 0.3098]]])
7: tensor([[[ 0.2541, -1.0928, -0.5348, ..., 0.2601, -0.0617, 0.3316],
[-0.1272, -0.4814, 0.7044, ..., 0.2241, -0.2760, -0.1436],
[-0.0632, -0.1760, 0.3805, ..., 0.1593, -0.3386, -0.4046],
...,
[-0.3154, -0.4015, -0.1204, ..., 0.0719, 0.2649, 0.0254],
[-0.1957, -0.1046, -0.2513, ..., 0.0026, 0.1645, 0.1519],
[ 0.2308, 0.0680, 0.0734, ..., 0.0991, -0.0395, 0.3916]]])
8: tensor([[[ 0.1774, -1.0197, -0.5520, ..., 0.2191, -0.0748, 0.3152],
[-0.2766, -0.5257, 0.8512, ..., -0.0022, -0.4838, -0.1323],
[-0.0459, -0.1794, 0.4229, ..., 0.3498, -0.4034, -0.4652],
...,
[-0.3510, -0.6134, -0.5410, ..., -0.1473, 0.2644, -0.0302],
[-0.0297, 0.0594, -0.5008, ..., 0.1593, 0.5762, 0.3170],
[ 0.3642, 0.1426, 0.1997, ..., 0.0183, 0.0300, 0.3661]]])
9: tensor([[[ 1.6661e-01, -1.0333e+00, -5.1397e-01, ..., 1.6799e-01,
-7.8662e-02, 3.0796e-01],
[-1.2754e-01, -7.2784e-01, 6.2181e-01, ..., -5.9251e-04,
-4.6773e-01, -2.0511e-01],
[-1.5388e-01, -3.8383e-01, 4.7796e-01, ..., 5.4677e-01,
-5.7957e-01, -3.6326e-01],
...,
[-6.1954e-01, -8.2205e-01, -1.0694e-01, ..., -4.9307e-02,
4.9293e-01, -1.2749e-01],
[ 2.9473e-02, -1.7091e-01, -6.9691e-01, ..., 1.7047e-01,
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40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
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Query: Die Studierenden sind in der Lage,
- Arbeitsbedingungen und Arbeitsmethoden eines/r Informatikers/in in der Praxis zu beschreiben,
- ihr Wissen durch Mitarbeit in einem aktuellen Projekt aus dem Informatiksektor unter Anleitung eines/r erfah-
renen Praktikers/in (Informatiker/in, Ingenieur/in, Naturwissenschaftler/in oder Mathematiker/in) anzuwenden,
- Schlüsselqualifikationen wie Teamfähigkeit, Kommunikationsfähigkeit, Kritikfähigkeit etc. einzusetzen und
weiterzuentwickeln,
- einen technischen Bericht zu erstellen.
Modulhandbuch Fakultät für Informatik
33
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[-3.6325, -0.9895, -0.3109, ..., 2.0022, -2.2564, -1.4084],
[-3.6424, 1.4958, 0.3539, ..., 1.3122, -3.3704, 0.2221]]])
38: tensor([[[ 0.8056, -0.4584, 0.9490, ..., 0.2496, -0.6052, -0.5354],
[ 1.0454, 4.6582, -2.5552, ..., -6.1068, 1.5839, -3.1076],
[-0.4167, 2.3306, 3.8001, ..., 2.5002, 1.2226, -1.7264],
...,
[-3.2565, -2.6058, 0.7800, ..., 1.0747, -2.8469, -2.8562],
[-3.4737, -1.1396, 0.1502, ..., 1.3525, -3.2863, -2.0740],
[-3.2968, 1.0202, 0.2031, ..., 0.9155, -4.0464, 0.2188]]])
39: tensor([[[ 0.2563, -0.7516, 0.9785, ..., 0.2588, 0.0239, -0.5686],
[ 1.1201, 4.0922, -3.4734, ..., -5.2470, -0.9760, -2.8315],
[-0.1410, 3.4196, 4.6781, ..., 1.8651, 0.5242, -2.1196],
...,
[-4.1437, -3.2829, -0.0823, ..., 1.4814, -4.9127, -3.0089],
[-3.9932, -2.5998, -0.3789, ..., 2.8980, -5.2200, -2.7530],
[-3.4912, 0.5698, 0.9979, ..., 1.5503, -5.1404, 0.4663]]])
40: tensor([[[-0.9813, -0.8804, -0.4755, ..., -0.3719, -0.1792, -0.4012],
[-0.2367, 0.8228, -1.0972, ..., -1.0244, -0.2596, -0.2143],
[-0.2036, 0.5696, 0.9712, ..., 0.0561, 0.0137, -0.8077],
...,
[-1.2212, -0.8229, -0.7962, ..., -0.0720, -1.5204, -0.3998],
[-1.4354, -0.8302, -0.8383, ..., 0.4408, -1.5116, -0.3558],
[-1.1722, -0.0928, -0.4849, ..., 0.1130, -1.0798, 0.3545]]])