What is the translation of " THE COST FUNCTION " in Chinese?

• 代价函数
• 成本函数
• cost function
• 损失函数

Examples of using The cost function in English and their translations into Chinese

This depends on the cost function and the model.
这取决于成本函数和模型。

The cost function must be able to be written as an average.
成本函数必须能够写成平均值.

Therefore, we define the cost function as follows:.
因此我们将他的CostFunction定义为下式：.

In our case, we are looking for the minimum of the cost function.
在我们的例子中，我们在寻找最小的成本函数。

Now let's look at the cost function for logistic regression.
现在让我们看看Logistic回归的成本函数。

For logistic regression, we will use the cost function:.
同时，在LogisticRegression中，定义CostFunction:.

To minimize the cost function, you need to iterate through your data set many times.
为了最小化成本函数，你需要多次迭代你的数据集。

But we will make one change to the cost function: Adding weight decay.
但此时我们需要对代价函数做一个改动：加入权重衰减。

Training a neural network basically refers to minimizing the cost function.
神经网络的训练目标实际上就是最小化某个损失函数。

But we will make one change to the cost function: Adding weight decay.
我们可以对代价函数做一个改动：加入权重衰减(weightdecay)。

A very simple example of this is Sammon's Mapping, defined by the cost function:.
一个非常简单的例子是萨蒙的映射，由代价函数定义：.

Now plot the cost function, J(θ) over the number of iterations of gradient descent.
现在小区的成本函数，J（θ）在梯度下降迭代次数。

In our case, we are looking for the minimum of the cost function.
在我们的例子中，我们寻找的是代价函数的最小值。

This will lead the cost function to be very sensitive in some directions and insensitive in other directions.
这将导致损失函数在某些方向上非常敏感，而在其他方向不敏感。

Unlike L1 and L2 regularization, dropout doesn't rely on modifying the cost function.
和L1、L2规范化不同，弃权技术并不依赖对代价函数的修改。

The cost function is the average of the Loss function over the entire training set.
代价函数是整个训练集的损失函数的平均值。

In the chapter on Logistic Regression, the cost function is this:.
同时，在LogisticRegression中，定义CostFunction:.

The cost function is the average of the Loss function over the entire training set.
另一方面，成本函数是整个训练数据集的平均损失。

Regularization methods like L1 and L2 reduce overfitting by modifying the cost function.
像L1和L2这样的正则化技术通过修改代价函数来减少过拟合….

When we are minimizing it, we may also call it the cost function, loss function, or error function..
当我们队其进行最小化时，我们也把他称为代价函数，损失函数或误差函数。

In this notation, is called the objective function(it is also sometimes called the cost function).
在标准式中，f称为目标函数（有时也称为损失函数）。

The cost function is the measure of“how good” a neural network did for it's given training input and the expected output.
成本函数是神经网络做的“有多好”的量度，在给定训练样本和预期输出方面。

In the chapter on Logistic Regression, the cost function is this:.
我们先以logisticsregression为例，它的costfunction是：.

The above figure shows the cost function to optimize here, mostly to illustrate the level of complexity data science sometimes brings with it.
上图显示的是用于优化这一方法的成本函数，主要是为了说明数据科学有时具有的复杂程度。

So the equation for the overall error(also called the cost function) will be:.
最终，得出我们的目标函数（也称为代价函数）为：.

The cost function of the network is used to generate a measure of deviation between the network's predictions and the actual observed training targets.
该网络的损失函数主要是用于生成网络预测与实际观察到的训练目标之间的偏差值。

In this video, we will figure out a slightly simpler way to write the cost function than we have been using so far.
在这段视频中，我们将会找出一种稍微简单一点的方法来写代价函数，来替换我们现在用的方法。

If the scatter points are close to the regression line, then the residual will be small and hence the cost function.
若是一条线接近这些点，残差将很小，因而成本函数。

However, there are many other approaches to optimizing the cost function, and sometimes those other approaches offer performance superior to mini-batch stochastic gradient descent.
然而，还有很多其他的观点来优化代价函数，有时候，这些方法能够带来比minibatch随机梯度下降更好的效果。

As discussed above, it's not possible to say precisely what it means to use the"same" learning rate when the cost function is changed.
正如之前所讨论的，当代价函数改变后，我们不能精确地定义什么是「相同的」学习率。