> * 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)

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

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

> * 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

\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...

        [],
        []
      ],
      '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) {

  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));

    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

        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));

      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 }]
// ]