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Results 1 - 10 of 245 for tearney (0.38 sec)

  1. TODO/how-i-learned-to-love-parallelized-applies-with-python-pandas-dask-and-numba

    > * 原文地址:[Data Pre-Processing in Python: How I learned to love parallelized applies with Dask and Numba](https://medium.com/@ernestk.social/how-i-learned-to-love-parallelized-applies-with-python-pandas-dask-and-numba-f06b0b367138)
    > * 原文作者:[Ernest Kim](https://medium.com/@ernestk.social?source=post_header_lockup)
    > * 译文出自:[掘金翻译计划](https://github.com/xitu/gold-miner)
    Plain Text
    - Registered: 2023-02-02 14:49
    - Last Modified: 2018-03-12 04:36
    - 11.8K bytes
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  2. examples/2_BasicModels/gradient_boosted_decision_tree.py

    max_depth = 16
    
    # Fill GBDT parameters into the config proto
    learner_config = gbdt_learner.LearnerConfig()
    learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate
    learner_config.regularization.l1 = l1_regul
    learner_config.regularization.l2 = l2_regul / examples_per_layer
    learner_config.constraints.max_tree_depth = max_depth
    growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER
    learner_config.growing_mode = growing_mode
    Python
    - Registered: 2023-02-07 19:51
    - Last Modified: 2018-07-27 02:50
    - 2.9K bytes
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  3. tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py

    max_depth = 16
    
    # Fill GBDT parameters into the config proto
    learner_config = gbdt_learner.LearnerConfig()
    learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate
    learner_config.regularization.l1 = l1_regul
    learner_config.regularization.l2 = l2_regul / examples_per_layer
    learner_config.constraints.max_tree_depth = max_depth
    growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER
    learner_config.growing_mode = growing_mode
    Python
    - Registered: 2023-02-07 19:51
    - Last Modified: 2020-05-16 20:14
    - 2.9K bytes
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  4. TODO/11-things-i-learned-reading-the-flexbox-spec.md

    > * 原文地址:[11 things I learned reading the flexbox spec](https://hackernoon.com/11-things-i-learned-reading-the-flexbox-spec-5f0c799c776b)
    > * 原文作者:本文已获原作者 [David Gilbertson](https://hackernoon.com/@david.gilbertson) 授权
    > * 译文出自:[掘金翻译计划](https://github.com/xitu/gold-miner)
    > * 译者:[XatMassacrE](https://github.com/XatMassacrE)
    > * 校对者:[zaraguo](https://github.com/zaraguo),[reid3290](https://github.com/reid3290)
    
    # 读完 flexbox 细则之后学到的 11 件事
    
    Plain Text
    - Registered: 2023-02-02 14:49
    - Last Modified: 2017-06-14 02:52
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  5. Chapter15/representation_learning.tex

    \citep{Fei-Fei+al-2006}是可能的。 在\gls{transfer_learning}阶段,仅需要一个\gls{labeled}样本来推断表示空间中聚集在相同点周围许多可能测试样本的\gls{label}。 这使得在\gls{learned}的表示空间中,对应于不变性的变化因子已经与其他因子完全分离,在区分某些类别的对象时,我们可以学习到哪些因素具有决定意义。 % 529 mid 考虑一个\gls{zero_shot_learning}\gls{setting}的例子,\gls{learner}已经读取了大量文本,然后要解决\gls{object_recognition}的问题。 如果文本足够好地描述了对象,那么即使没有看到某对象的图像,也能识别出该对象的类别。 例如,已知猫有四条腿和尖尖的耳朵,那么\gls{learner}可以在没有见过猫的情况下猜测该图像中是猫。 % 529 end 只有在训练时使用了额外信息,\gls{zero_data_learning}~\citep{Larochelle2008}和\gls{zero_shot_learning}~\ci...
    Others
    - Registered: 2023-02-02 11:44
    - Last Modified: 2017-09-24 14:24
    - 74.2K bytes
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  6. test/unzip-and-zip.js

            [],
            []
          ],
          '0-tuples': [
            [[], []],
            []
          ],
          '2-tuples': [
            [['barney', 'fred'], [36, 40]],
            [['barney', 36], ['fred', 40]]
          ],
          '3-tuples': [
            [['barney', 'fred'], [36, 40], [false, true]],
            [['barney', 36, false], ['fred', 40, true]]
          ]
        };
    
        lodashStable.forOwn(object, function(pair, key) {
    JavaScript
    - Registered: 2023-02-03 12:22
    - Last Modified: 2019-02-12 17:11
    - 2.3K bytes
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  7. test/wrap.js

      it('should create a wrapped function', function() {
        var p = wrap(lodashStable.escape, function(func, text) {
          return '<p>' + func(text) + '</p>';
        });
    
        assert.strictEqual(p('fred, barney, & pebbles'), '<p>fred, barney, &amp; pebbles</p>');
      });
    
      it('should provide correct `wrapper` arguments', function() {
        var args;
    
        var wrapped = wrap(noop, function() {
          args || (args = slice.call(arguments));
    JavaScript
    - Registered: 2023-02-03 12:22
    - Last Modified: 2019-02-12 17:11
    - 1.4K bytes
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  8. backtracking/n_queens_math.py

        for col in range(n):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
    Python
    - Registered: 2023-02-02 19:18
    - Last Modified: 2023-02-01 13:14
    - 4.8K bytes
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  9. core/src/test/java/com/alibaba/druid/bvt/sql/hive/HiveInsert_0.java

            stmt.accept(visitor);
    
            assertEquals("INSERT INTO TABLE students\n" +
                    "VALUES ('fred flintstone', 35, 1.28), ('barney rubble', 32, 2.32);", SQLUtils.formatHive(sql));
    
            assertEquals("insert into table students\n" +
                    "values ('fred flintstone', 35, 1.28), ('barney rubble', 32, 2.32);", SQLUtils.formatHive(sql, SQLUtils.DEFAULT_LCASE_FORMAT_OPTION));
    
    Java
    - Registered: 2023-01-30 07:39
    - Last Modified: 2022-09-12 01:19
    - 1.6K bytes
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  10. snippets/partition.md

          return acc;
        },
        [[], []]
      );
    ```
    
    ```js
    const users = [
      { user: 'barney', age: 36, active: false },
      { user: 'fred', age: 40, active: true },
    ];
    partition(users, o => o.active);
    // [
    //   [{ user: 'fred', age: 40, active: true }],
    //   [{ user: 'barney', age: 36, active: false }]
    // ]
    Plain Text
    - Registered: 2023-02-07 11:25
    - Last Modified: 2022-12-04 20:20
    - 931 bytes
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