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  • pdf文档 深度学习与PyTorch入门实战 - 07. 创建Tensor

    0 码力 | 16 页 | 1.43 MB | 1 年前
    3

  • pdf文档 机器学习课程-温州大学-07机器学习-决策树

    0 码力 | 39 页 | 1.84 MB | 1 年前
    3

  • pdf文档 机器学习课程-温州大学-07深度学习-卷积神经网络

    0 码力 | 29 页 | 3.14 MB | 1 年前
    3

  • pdf文档 Exactly-once fault-tolerance in Apache Flink - CS 591 K1: Data Stream Processing and Analytics Spring 2020

    nlBpnsg7M04xiOlA8ogzaqzkY897bPSqNbfuzkCWiVe QGhRo9apf3X7CshilYJq3fHc1AQ5VYzgZNKN9OYUjaiA+xYKmMOshnx07IiVX6JEqULWnITP09kdNY63Ec2s6YmqFe9Kbif14nM9FkHOZgYlmy+KMkFMQqafkz5XyIwYW0KZ4vZWwoZUWZsPhUbgrf48jLxG/XLund7VmteF nlBpnsg7M04xiOlA8ogzaqzkY897bPSqNbfuzkCWiVe QGhRo9apf3X7CshilYJq3fHc1AQ5VYzgZNKN9OYUjaiA+xYKmMOshnx07IiVX6JEqULWnITP09kdNY63Ec2s6YmqFe9Kbif14nM9FkHOZgYlmy+KMkFMQqafkz5XyIwYW0KZ4vZWwoZUWZsPhUbgrf48jLxG/XLund7VmteF nlBpnsg7M04xiOlA8ogzaqzkY897bPSqNbfuzkCWiVe QGhRo9apf3X7CshilYJq3fHc1AQ5VYzgZNKN9OYUjaiA+xYKmMOshnx07IiVX6JEqULWnITP09kdNY63Ec2s6YmqFe9Kbif14nM9FkHOZgYlmy+KMkFMQqafkz5XyIwYW0KZ4vZWwoZUWZsPhUbgrf48jLxG/XLund7VmteF
    0 码力 | 81 页 | 13.18 MB | 1 年前
    3

  • pdf文档 pandas: powerful Python data analysis toolkit - 0.7.1

    2000-01-04 -0.646 -1.051 -0.716 2000-01-05 0.613 0.501 -1.380 2000-01-06 0.624 0.790 0.818 2000-01-07 1.559 0.335 0.919 2000-01-10 -1.381 0.365 -1.811 2000-01-11 -0.673 -1.968 -0.401 2000-01-12 -0.583 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046
    0 码力 | 281 页 | 1.45 MB | 1 年前
    3

  • pdf文档 pandas: powerful Python data analysis toolkit - 0.7.2

    2000-01-04 -0.646 -1.051 -0.716 2000-01-05 0.613 0.501 -1.380 2000-01-06 0.624 0.790 0.818 2000-01-07 1.559 0.335 0.919 2000-01-10 -1.381 0.365 -1.811 2000-01-11 -0.673 -1.968 -0.401 2000-01-12 -0.583 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046
    0 码力 | 283 页 | 1.45 MB | 1 年前
    3

  • pdf文档 pandas: powerful Python data analysis toolkit - 0.7.3

    2000-01-04 -0.646 -1.051 -0.716 2000-01-05 0.613 0.501 -1.380 2000-01-06 0.624 0.790 0.818 2000-01-07 1.559 0.335 0.919 2000-01-10 -1.381 0.365 -1.811 2000-01-11 -0.673 -1.968 -0.401 2000-01-12 -0.583 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046 553 -0.419 2000-01-04 0 -0.405 -0.070 2000-01-05 0 -0.112 -1.993 2000-01-06 0 0.166 0.195 2000-01-07 0 -1.224 -0.640 2000-01-10 0 1.746 -0.429 2000-01-11 0 -1.294 0.272 2000-01-12 0 -0.416 -0.046
    0 码力 | 297 页 | 1.92 MB | 1 年前
    3

  • pdf文档 pandas: powerful Python data analysis toolkit - 0.20.3

    powerful Python data analysis toolkit Release 0.20.3 Wes McKinney & PyData Development Team Jul 07, 2017 CONTENTS 1 What’s New 3 1.1 v0.20.3 (July 7, 2017) . . . . . . . . . . . . . . . . . . . pandas: powerful Python data analysis toolkit, Release 0.20.3 PDF Version Zipped HTML Date: Jul 07, 2017 Version: 0.20.3 Binary Installers: http://pypi.python.org/pypi/pandas Source Repository: http://github 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 2000-01-09 -1.197071 -1.066969 -0.303421 2000-01-10
    0 码力 | 2045 页 | 9.18 MB | 1 年前
    3

  • pdf文档 pandas: powerful Python data analysis toolkit - 0.25

    In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64 In [47]: df['F'] = s1 Setting values by label: In [48]: df.at[dates[0] Release 0.25.3 (continued from previous page) In [113]: ts Out[113]: 2012-03-06 0.198218 2012-03-07 -0.649384 2012-03-08 0.743513 2012-03-09 -0.541289 2012-03-10 -0.594013 Freq: D, dtype: float64 ts_utc = ts.tz_localize('UTC') In [115]: ts_utc Out[115]: 2012-03-06 00:00:00+00:00 0.198218 2012-03-07 00:00:00+00:00 -0.649384 2012-03-08 00:00:00+00:00 0.743513 2012-03-09 00:00:00+00:00 -0.541289
    0 码力 | 698 页 | 4.91 MB | 1 年前
    3

  • pdf文档 pandas: powerful Python data analysis toolkit - 0.20.2

    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 2000-01-09 -1.197071 -1.066969 -0.303421 2000-01-10 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 0.901805 2.098877 1.171216 2.242573 0.520260 1.803042 2000-01-09 df.tail() Out[110]: bar A B foo 2016-04-05 0.640880 0.126205 2016-04-06 0.171465 0.737086 2016-04-07 0.127029 0.369650 2016-04-08 0.604334 0.103104 2016-04-09 0.802374 0.945553 1.2. v0.20.1 (May 5
    0 码力 | 1907 页 | 7.83 MB | 1 年前
    3
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