OVERFITTING in Chinese translation

• 过度拟合
• 过拟合
• 过拟
• 的过拟合问题
• 过匹配

Examples of using Overfitting in English and their translations into Chinese

In this article, we will understand the concept of overfitting and how regularization helps in overcoming the same problem.
在本文中，我们将解释过拟合的概念以及正则化如何帮助克服过拟合问题。

Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations.
过度拟合通常发生在模型过于复杂的情况下，例如相对于观察到的训练数据的参数数量太多。

This significantly reduces overfitting and gives major improvements over other regularization methods.
这显著减轻了过拟合，并且为其他的正则化方法带来重要改进。

There are regularisation techniques like dropout that can force it to learn in a better way but overfitting also has deeper roots.
有一些像dropout一样的规范化技术能够迫使它学习得更好，不过过拟合还有更深层的原因。

To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called“dropout” that proved to be very effective.
为了减少全连接层的过拟合问题，我们使用了最近开发的正则化方法“dropout”,它被证明是非常有效的。

Based on VGG16 but modified to take account of the small dataset and reduce overfitting(probably dropout and batch normalization).
基于VGG16网络，但是由于考虑到小数据集和减少过度拟合（有可能放弃和批量标准化）而进行了修改。

This is known as overfitting, and it's a common problem in machine learning and data science.
这被称为过拟合，也是机器学习和数据科学中的常见问题。

Reduces the number of parameters and computations in the network, therefore, controlling overfitting[4].
减少网络中的参数和运算次数，因此可以控制过拟合[4].

There are regularisation techniques like dropout that can force it to learn in a better way but overfitting also has deeper roots.
有正规化技术，如丢失数据（dropout），可以强制它以更好的方式学习，但过拟合也有更深的根源。

It allows multiple layers to be trained and also includes the dropouts technique to avoid overfitting the data.
它允许训练多个层，并且还包括退出技术以避免过度拟合数据。

To prevent overfitting, we often use regularization techniques like lasso and ridge.
为了防止过拟合，我们经常使用lasso和ridge之类的规整化技术。

To reduce overfitting in the fully connected layers we employed a recently developed regularization method called"dropout" that proved to be very effective.
为了减少全连接层的过拟合问题，我们使用了最近开发的正则化方法“dropout”,它被证明是非常有效的。

Fortunately, there are other techniques which can reduce overfitting, even when we have a fixed network and fixed training data.
幸运的是，还有其他的技术能够缓解过匹配，即使我们只有一个固定的网络和固定的训练集合。

Finally you learned about the terminology of generalization in machine learning of overfitting and underfitting.
最后你学习了机器学习中的术语：泛化中的过拟合与欠拟合.

The standard methods that work for"tall" data will lead to overfitting the data, so special approaches are needed.
为了“高”工作数据的标准方法将导致过度拟合数据,所以需要特殊的方法。

Do you worry about overfitting your models, so they work for the time periods you used for model development, but not afterward?
你是否担心你们的模型过度拟合，在你们开发模型的时候奏效，但后来就不行了？?

This problem is known as overfitting, and can be especially problematic when working with small training sets.
这个问题被称为过拟合，当运作在小训练集上时尤其会有问题。

This kind of averaging scheme is often found to be a powerful(though expensive) way of reducing overfitting.
这种平均的方式通常是一种强大（尽管代价昂贵）的方式来减轻过匹配。

In this post, you discovered the use of dropout regularization for reducing overfitting and improving the generalization of deep neural networks.
在这篇文章中，你发现了使用Dropout正则化来减少过拟合，并改进深度神经网络的泛化。

Li, in her first teaching job at UIUC, had been grappling with one of the core tensions in machine learning: overfitting and generalization.
李飞飞在UIUC的第一份教学工作中，一直在努力解决机器学习中的一个核心矛盾：过拟合和泛化。