正则化 - 翻译成英语

• regularization
• regularized

在 中文 中使用 正则化 的示例及其翻译为 英语

黑塞特征映射也称作基于黑塞的LLE:HLLE）是解决LLE正则化问题的另一种方法。
Hessian Eigenmapping(also known as Hessian-based LLE: HLLE) is another method of solving the regularization problem of LLE.

希望现在你对正则化技术以及怎样在深度学习模型中实现它有了一定的了解。
I hope that now you have an understanding of regularization and the different techniques required to implement it in deep learning models.

BatchNormalization也是一种正则化形式，有助于最小化过拟合。
Batch Normalization also acts as a form of regularization that helps to minimize overfitting.

E早期停止法（earlystopping）一种正则化方法，在训练损失完成下降之前停止模型训练过程。
Early stopping is a regularization method that involves ending model training before training loss finishes decreasing.

无论是在训练过程中添加正则化，还是在模型本身中加入正则化，这些都是改进机器学习的方法。
Whether the regularization is added during training or baked into the model itself, this is how improvements in machine learning are made.

通常，正则化方法也是一个超参数，这意味着它可以通过交叉验证进行调优。
Oftentimes, the regularization method is a hyperparameter as well, which means it can be tuned through cross-validation.

接下来，我们讨论神经网络训练的各种正则化方法，及方法之间的关系。
Next we discuss various approaches to regularization of neural network training and the relationships between them.

一个理论是它们想要噪声，以便正则化，因为我们的参数超过数据量。
One theory is that they want to be noisy so as to regularize, because we have many more parameters than we have data points.

然后，试着增加正则化的强度，这样应该会增加损失。
After this, try increasing the regularization strength which should increase the loss.

一种正则化方法，涉及在训练损失仍可以继续减少之前结束模型训练。
Early stopping is a regularization method that involves ending model training before training loss finishes decreasing.

换句话说，权重衰减不仅仅是正则化：它减小了模型的训练错误。
In other words, weight decay here is not merely a regularizer: it reduces the model's training error.

一种标量值，以lambda表示，用于指定正则化函数的相对重要性。
A scalar value, represented as lambda, specifying the relative importance of the regularization function.

因此本书的剩余部分我们将频繁地使用正则化。
And so through the remainder of the book we will make frequent use of regularization.

尽管很多教科书都把逐步搜索算法作为一个有效的方法，但它所表现出来的性能总是不及其它监督式方法，比如正则化。
Despite many textbooks listing stepwise search as a valid option, it almost always underperforms other supervised methods such as regularization.

实际观察结果表明，现代深度网络都没有得到充分正则化：.
In practice, a number of observations suggest that modern deep networks are under-regularized.

在下一篇文章（课程2）中，我们将看到如何通过超参数调整、正则化和优化来改进深度神经网络。
In the next article(which will cover course 2), we will see how we can improve deep neural networks by hyperparameter tuning, regularization and optimization.

这其中的一些分类方法，特别是线性SVM、正则化的logistic回归及线性回归方法等等，都是当今最有效的方法之一。
Some of these methods, in particular linear SVMs, regularized logistic regression and regularized linear regression, are among the most effective known methods….

所有选项的共同之处在于您需要添加一些超参数的配置，即“高级”参数，如问题类型、特征选择或正则化等级。
All options have in common that you need to add configuration of some hyper-parameters, i.e.“high-level” parameters like problem type, feature selection, or regularization level.

KDnuggets编辑给你“20个问题来分辨真假数据科学家”的答案，包括什么是正则化、我们崇拜的数据科学家、模型验证等等。
KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.

特别是神经网络层、损失函数、优化器、初始化方法、激活函数、正则化方法，它们都是可以结合起来构建新模型的模块。
In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.