【05 计算平台 蓉荣】Flink 批处理及其应⽤0 码力 | 12 页 | 1.44 MB | 1 年前3- 深度学习与PyTorch入门实战 - 05. 手写数字问题0 码力 | 10 页 | 569.56 KB | 1 年前3
- 机器学习课程-温州大学-05深度学习-深度学习实践1 2023年03月 深度学习-深度学习实践 黄海广 副教授 2 01 数据集划分 02 数据集制作 03 数据归一化/标准化 04 正则化 05 偏差和方差 本章目录 3 训练集（Training Set）：帮助我们训练模型，简单的说就是通过 训练集的数据让我们确定拟合曲线的参数。 验证集（Validation Set）：也叫做开发集（ Dev0 码力 | 19 页 | 1.09 MB | 1 年前3
- 机器学习课程-温州大学-05机器学习-机器学习实践0 码力 | 33 页 | 2.14 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 0.25[6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') In [7]: df = pd.DataFrame(np.random.randn(6, 4) 2013-01-03 -1.323650 0.427355 0.835343 -0.000698 2013-01-04 0.509859 -2.769586 1.000521 -0.865748 2013-01-05 0.139488 -0.259328 1.082034 -0.902452 2013-01-06 -0.130327 -0.372906 1.072236 -0.424347 Creating 769586 1.000521 -0.865748 2013-01-05 0.139488 -0.259328 1.082034 -0.902452 In [14]: df.tail(3) Out[14]: A B C D 2013-01-04 0.509859 -2.769586 1.000521 -0.865748 2013-01-05 0.139488 -0.259328 1.082034 -00 码力 | 698 页 | 4.91 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 0.20.20.781836 -1.071357 0.441153 2000-01-03 2.353925 0.583787 0.221471 2000-01-04 NaN NaN NaN 2000-01-05 NaN NaN NaN 2000-01-06 NaN NaN NaN 2000-01-07 NaN NaN NaN 2000-01-08 0.901805 1.171216 0.520260 2.353925 3.550996 0.583787 1.655143 0.221471 1.504252 2000-01-04 NaN NaN NaN NaN NaN NaN 2000-01-05 NaN NaN NaN NaN NaN NaN 2000-01-06 NaN NaN NaN NaN NaN NaN 2000-01-07 NaN NaN NaN NaN NaN NaN 2000-01-08 Timestamp('20160101', tz='US/Eastern'), pd.Timestamp('20160101', tz='US/Eastern')])) Out[6]: array(['2016-01-01T05:00:00.000000000'], dtype='datetime64[ns]') # Index In [7]: pd.Index([pd.Timestamp('20160101', tz='US/Eastern')0 码力 | 1907 页 | 7.83 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 0.20.30.781836 -1.071357 0.441153 2000-01-03 2.353925 0.583787 0.221471 2000-01-04 NaN NaN NaN 2000-01-05 NaN NaN NaN 2000-01-06 NaN NaN NaN 2000-01-07 NaN NaN NaN 2000-01-08 0.901805 1.171216 0.520260 2.353925 3.550996 0.583787 1.655143 0.221471 1.504252 2000-01-04 NaN NaN NaN NaN NaN NaN 2000-01-05 NaN NaN NaN NaN NaN NaN 2000-01-06 NaN NaN NaN NaN NaN NaN 2000-01-07 NaN NaN NaN NaN NaN NaN 2000-01-08 Timestamp('20160101', tz='US/Eastern'), pd.Timestamp('20160101', tz='US/Eastern')])) Out[6]: array(['2016-01-01T05:00:00.000000000'], dtype='datetime64[ns]') # Index In [7]: pd.Index([pd.Timestamp('20160101', tz='US/Eastern')0 码力 | 2045 页 | 9.18 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 0.21.102:00 NaN NaN NaN NaN 2000-01-01 03:00 NaN NaN NaN NaN 2000-01-01 04:00 NaN NaN NaN NaN 2000-01-01 05:00 NaN NaN NaN NaN 2000-01-01 06:00 NaN NaN NaN NaN ... ... ... ... ... 2000-01-10 17:00 NaN NaN 603650 0.567011 -0.994009 2000-01-03 -2.230893 -1.635263 0.357573 2000-01-04 NaN NaN NaN 2000-01-05 NaN NaN NaN 2000-01-06 NaN NaN NaN 2000-01-07 NaN NaN NaN 2000-01-08 1.667624 1.619575 -0.948507 2.230893 0.198409 1.635263 0.000000 0.357573 1.351583 2000-01-04 NaN NaN NaN NaN NaN NaN 2000-01-05 NaN NaN NaN NaN NaN NaN 2000-01-06 NaN NaN NaN NaN NaN NaN 2000-01-07 NaN NaN NaN NaN NaN NaN 2000-01-080 码力 | 2207 页 | 8.59 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 0.19.1quantity 0 2016-05-25 13:30:00.023 MSFT 51.95 75 1 2016-05-25 13:30:00.038 MSFT 51.95 155 2 2016-05-25 13:30:00.048 GOOG 720.77 100 3 2016-05-25 13:30:00.048 GOOG 720.92 100 4 2016-05-25 13:30:00.048 ask 0 2016-05-25 13:30:00.023 GOOG 720.50 720.93 1 2016-05-25 13:30:00.023 MSFT 51.95 51.96 2 2016-05-25 13:30:00.030 MSFT 51.97 51.98 3 2016-05-25 13:30:00.041 MSFT 51.99 52.00 4 2016-05-25 13:30:00 13:30:00.048 GOOG 720.50 720.93 5 2016-05-25 13:30:00.049 AAPL 97.99 98.01 6 2016-05-25 13:30:00.072 GOOG 720.50 720.88 7 2016-05-25 13:30:00.075 MSFT 52.01 52.03 An asof merge joins on the on, typically0 码力 | 1943 页 | 12.06 MB | 1 年前3
- pandas: powerful Python data analysis toolkit - 0.19.0quantity 0 2016-05-25 13:30:00.023 MSFT 51.95 75 1 2016-05-25 13:30:00.038 MSFT 51.95 155 2 2016-05-25 13:30:00.048 GOOG 720.77 100 3 2016-05-25 13:30:00.048 GOOG 720.92 100 4 2016-05-25 13:30:00.048 In [10]: quotes Out[10]: time ticker bid ask 0 2016-05-25 13:30:00.023 GOOG 720.50 720.93 1 2016-05-25 13:30:00.023 MSFT 51.95 51.96 2 2016-05-25 13:30:00.030 MSFT 51.97 51.98 6 Chapter 1. What’s 0 3 2016-05-25 13:30:00.041 MSFT 51.99 52.00 4 2016-05-25 13:30:00.048 GOOG 720.50 720.93 5 2016-05-25 13:30:00.049 AAPL 97.99 98.01 6 2016-05-25 13:30:00.072 GOOG 720.50 720.88 7 2016-05-25 13:30:000 码力 | 1937 页 | 12.03 MB | 1 年前3
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