milage,Liters,Consumtime,target
40920,8.326976,0.953952,3
14488,7.153469,1.673904,2
26052,1.441871,0.805124,1
75136,13.147394,0.428964,1
38344,1.669788,0.134296,1
72993,10.141740,1.032955,1
35948,6.830792,1.213192,3
42666,13.276369,0.543880,3
67497,8.631577,0.749278,1
35483,12.273169,1.508053,3
50242,3.723498,0.831917,1
63275,8.385879,1.669485,1
5569,4.875435,0.728658,2
51052,4.680098,0.625224,1
77372,15.299570,0.331351,1
43673,1.889461,0.191283,1
61364,7.516754,1.269164,1
69673,14.239195,0.261333,1
15669,0.000000,1.250185,2
28488,10.528555,1.304844,3
6487,3.540265,0.822483,2
37708,2.991551,0.833920,1
22620,5.297865,0.638306,2
28782,6.593803,0.187108,3
19739,2.816760,1.686209,2
36788,12.458258,0.649617,3
5741,0.000000,1.656418,2
28567,9.968648,0.731232,3
6808,1.364838,0.640103,2
41611,0.230453,1.151996,1
36661,11.865402,0.882810,3
43605,0.120460,1.352013,1
15360,8.545204,1.340429,3
63796,5.856649,0.160006,1
10743,9.665618,0.778626,2
70808,9.778763,1.084103,1
72011,4.932976,0.632026,1
5914,2.216246,0.587095,2
14851,14.305636,0.632317,3
33553,12.591889,0.686581,3
44952,3.424649,1.004504,1
17934,0.000000,0.147573,2
27738,8.533823,0.205324,3
29290,9.829528,0.238620,3
42330,11.492186,0.263499,3
36429,3.570968,0.832254,1
39623,1.771228,0.207612,1
32404,3.513921,0.991854,1
27268,4.398172,0.975024,1
5477,4.276823,1.174874,2
14254,5.946014,1.614244,2
68613,13.798970,0.724375,1
41539,10.393591,1.663724,3
7917,3.007577,0.297302,2
21331,1.031938,0.486174,2
8338,4.751212,0.064693,2
5176,3.692269,1.655113,2
18983,10.448091,0.267652,3
68837,10.585786,0.329557,1
13438,1.604501,0.069064,2
48849,3.679497,0.961466,1
12285,3.795146,0.696694,2
7826,2.531885,1.659173,2
5565,9.733340,0.977746,2
10346,6.093067,1.413798,2
1823,7.712960,1.054927,2
9744,11.470364,0.760461,3
16857,2.886529,0.934416,2
39336,10.054373,1.138351,3
65230,9.972470,0.881876,1
2463,2.335785,1.366145,2
27353,11.375155,1.528626,3
16191,0.000000,0.605619,2
12258,4.126787,0.357501,2
42377,6.319522,1.058602,1
25607,8.680527,0.086955,3
77450,14.856391,1.129823,1
58732,2.454285,0.222380,1
46426,7.292202,0.548607,3
32688,8.745137,0.857348,3
64890,8.579001,0.683048,1
8554,2.507302,0.869177,2
28861,11.415476,1.505466,3
42050,4.838540,1.680892,1
32193,10.339507,0.583646,3
64895,6.573742,1.151433,1
2355,6.539397,0.462065,2
0,2.209159,0.723567,2
70406,11.196378,0.836326,1
57399,4.229595,0.128253,1
41732,9.505944,0.005273,3
11429,8.652725,1.348934,3
75270,17.101108,0.490712,1
5459,7.871839,0.717662,2
73520,8.262131,1.361646,1
40279,9.015635,1.658555,3
21540,9.215351,0.806762,3
17694,6.375007,0.033678,2
22329,2.262014,1.022169,1
46570,5.677110,0.709469,1
42403,11.293017,0.207976,3
33654,6.590043,1.353117,1
9171,4.711960,0.194167,2
28122,8.768099,1.108041,3
34095,11.502519,0.545097,3
1774,4.682812,0.578112,2
40131,12.446578,0.300754,3
13994,12.908384,1.657722,3
77064,12.601108,0.974527,1
11210,3.929456,0.025466,2
6122,9.751503,1.182050,3
15341,3.043767,0.888168,2
44373,4.391522,0.807100,1
28454,11.695276,0.679015,3
63771,7.879742,0.154263,1
9217,5.613163,0.933632,2
69076,9.140172,0.851300,1
24489,4.258644,0.206892,1
16871,6.799831,1.221171,2
39776,8.752758,0.484418,3
5901,1.123033,1.180352,2
40987,10.833248,1.585426,3
7479,3.051618,0.026781,2
38768,5.308409,0.030683,3
4933,1.841792,0.028099,2
32311,2.261978,1.605603,1
26501,11.573696,1.061347,3
37433,8.038764,1.083910,3
23503,10.734007,0.103715,3
68607,9.661909,0.350772,1
27742,9.005850,0.548737,3
11303,0.000000,0.539131,2
0,5.757140,1.062373,2
32729,9.164656,1.624565,3
24619,1.318340,1.436243,1
42414,14.075597,0.695934,3
20210,10.107550,1.308398,3
33225,7.960293,1.219760,3
54483,6.317292,0.018209,1
18475,12.664194,0.595653,3
33926,2.906644,0.581657,1
43865,2.388241,0.913938,1
26547,6.024471,0.486215,3
44404,7.226764,1.255329,3
16674,4.183997,1.275290,2
8123,11.850211,1.096981,3
42747,11.661797,1.167935,3
56054,3.574967,0.494666,1
10933,0.000000,0.107475,2
18121,7.937657,0.904799,3
11272,3.365027,1.014085,2
16297,0.000000,0.367491,2
28168,13.860672,1.293270,3
40963,10.306714,1.211594,3
31685,7.228002,0.670670,3
55164,4.508740,1.036192,1
17595,0.366328,0.163652,2
1862,3.299444,0.575152,2
57087,0.573287,0.607915,1
63082,9.183738,0.012280,1
51213,7.842646,1.060636,3
6487,4.750964,0.558240,2
4805,11.438702,1.556334,3
30302,8.243063,1.122768,3
68680,7.949017,0.271865,1
17591,7.875477,0.227085,2
74391,9.569087,0.364856,1
37217,7.750103,0.869094,3
42814,0.000000,1.515293,1
14738,3.396030,0.633977,2
19896,11.916091,0.025294,3
14673,0.460758,0.689586,2
32011,13.087566,0.476002,3
58736,4.589016,1.672600,1
54744,8.397217,1.534103,1
29482,5.562772,1.689388,1
27698,10.905159,0.619091,3
11443,1.311441,1.169887,2
56117,10.647170,0.980141,3
39514,0.000000,0.481918,1
26627,8.503025,0.830861,3
16525,0.436880,1.395314,2
24368,6.127867,1.102179,1
22160,12.112492,0.359680,3
6030,1.264968,1.141582,2
6468,6.067568,1.327047,2
22945,8.010964,1.681648,3
18520,3.791084,0.304072,2
34914,11.773195,1.262621,3
6121,8.339588,1.443357,2
38063,2.563092,1.464013,1
23410,5.954216,0.953782,1
35073,9.288374,0.767318,3
52914,3.976796,1.043109,1
16801,8.585227,1.455708,3
9533,1.271946,0.796506,2
16721,0.000000,0.242778,2
5832,0.000000,0.089749,2
44591,11.521298,0.300860,3
10143,1.139447,0.415373,2
21609,5.699090,1.391892,2
23817,2.449378,1.322560,1
15640,0.000000,1.228380,2
8847,3.168365,0.053993,2
50939,10.428610,1.126257,3
28521,2.943070,1.446816,1
32901,10.441348,0.975283,3
42850,12.478764,1.628726,3
13499,5.856902,0.363883,2
40345,2.476420,0.096075,1
43547,1.826637,0.811457,1
70758,4.324451,0.328235,1
19780,1.376085,1.178359,2
44484,5.342462,0.394527,1
54462,11.835521,0.693301,3
20085,12.423687,1.424264,3
42291,12.161273,0.071131,3
47550,8.148360,1.649194,3
11938,1.531067,1.549756,2
40699,3.200912,0.309679,1
70908,8.862691,0.530506,1
73989,6.370551,0.369350,1
11872,2.468841,0.145060,2
48463,11.054212,0.141508,3
15987,2.037080,0.715243,2
70036,13.364030,0.549972,1
32967,10.249135,0.192735,3
63249,10.464252,1.669767,1
42795,9.424574,0.013725,3
14459,4.458902,0.268444,2
19973,0.000000,0.575976,2
5494,9.686082,1.029808,3
67902,13.649402,1.052618,1
25621,13.181148,0.273014,3
27545,3.877472,0.401600,1
58656,1.413952,0.451380,1
7327,4.248986,1.430249,2
64555,8.779183,0.845947,1
8998,4.156252,0.097109,2
11752,5.580018,0.158401,2
76319,15.040440,1.366898,1
27665,12.793870,1.307323,3
67417,3.254877,0.669546,1
21808,10.725607,0.588588,3
15326,8.256473,0.765891,2
20057,8.033892,1.618562,3
79341,10.702532,0.204792,1
15636,5.062996,1.132555,2
35602,10.772286,0.668721,3
28544,1.892354,0.837028,1
57663,1.019966,0.372320,1
78727,15.546043,0.729742,1
68255,11.638205,0.409125,1
14964,3.427886,0.975616,2
21835,11.246174,1.475586,3
7487,0.000000,0.645045,2
8700,0.000000,1.424017,2
26226,8.242553,0.279069,3
65899,8.700060,0.101807,1
6543,0.812344,0.260334,2
46556,2.448235,1.176829,1
71038,13.230078,0.616147,1
47657,0.236133,0.340840,1
19600,11.155826,0.335131,3
37422,11.029636,0.505769,3
1363,2.901181,1.646633,2
26535,3.924594,1.143120,1
47707,2.524806,1.292848,1
38055,3.527474,1.449158,1
6286,3.384281,0.889268,2
10747,0.000000,1.107592,2
44883,11.898890,0.406441,3
56823,3.529892,1.375844,1
68086,11.442677,0.696919,1
70242,10.308145,0.422722,1
11409,8.540529,0.727373,2
67671,7.156949,1.691682,1
61238,0.720675,0.847574,1
17774,0.229405,1.038603,2
53376,3.399331,0.077501,1
30930,6.157239,0.580133,1
28987,1.239698,0.719989,1
13655,6.036854,0.016548,2
7227,5.258665,0.933722,2
40409,12.393001,1.571281,3
13605,9.627613,0.935842,2
26400,11.130453,0.597610,3
13491,8.842595,0.349768,3
30232,10.690010,1.456595,3
43253,5.714718,1.674780,3
55536,3.052505,1.335804,1
8807,0.000000,0.059025,2
25783,9.945307,1.287952,3
22812,2.719723,1.142148,1
77826,11.154055,1.608486,1
38172,2.687918,0.660836,1
31676,10.037847,0.962245,3
74038,12.404762,1.112080,1
44738,10.237305,0.633422,3
17410,4.745392,0.662520,2
5688,4.639461,1.569431,2
36642,3.149310,0.639669,1
29956,13.406875,1.639194,3
60350,6.068668,0.881241,1
23758,9.477022,0.899002,3
25780,3.89
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机器学习与深度学习入门训练.zip
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深度学习使用技巧和一些模型训练,实战应用开发小系统参考资料,源码参考。 适用于初学者和有经验的开发者,能够帮助快速上手深度学习模型建立学习等
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机器学习与深度学习入门训练.zip (26个子文件)
lern_2
day01_datasets
__init__.py 9B
文本特征提取.py 702B
中文结巴分词.py 3KB
标准化.py 571B
探究用户对物品类别的喜好细分降维.py 2KB
归一化.py 503B
特征值提取.py 632B
dating.txt 25KB
低方差特征过滤.py 574B
factor_returns.csv 309KB
主成分分析计算.py 650B
数据集划分.py 1KB
股票的财务指标相关性计算.py 761B
day02_分类算法
iris_tree.dot 1KB
决策树对鸢尾花的划分.py 830B
k_近邻算法.py 873B
朴素贝叶斯新闻分类.py 1KB
莺尾花案例增加k值调优.py 1KB
day03_回归于聚类算法
逻辑回归算法.py 2KB
波士顿房价预测.py 5KB
day04_tensorflow
session_demo.py 1001B
ts_demo.py 4KB
狗图片读取.py 2KB
linear_regression.py 3KB
tensor_demo.py 2KB
events.out.tfevents.1547953147.PC-201806031341 666B
共 26 条
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