DATA NORMALIZATION 中文是什么意思

This is Data Normalization.

这就是数据标准化。

Data normalization is not necessary.

数据标准化并不容易。

What is Data Normalization.

什么是数据标准化处理.

Data normalization is used during backpropagation.

在反向传播期间要使用数据标准化。

What is meant by data normalization?

数据标准化是什么意思?

What is data normalization and why do we need it?

什么是DataNormalization以及为什么我们需要它？?

This process is called data normalization.

这个过程叫做“数据的标准化”。

Data normalization is the process of rescaling one or more attributes to the range of 0 to 1.

归一化-数据归一化是将一个或多个属性缩放至0到1的范围的过程。

When done manually, this process is referred to as mapping or data normalization.

当人工完成时,这个过程被称为映射或数据规范化。

The main motive behind data normalization is to reduce or eliminate data redundancy.

数据规范化背后的主要动机是减少或消除数据冗余。

Similarly, we still need to follow some standard principles concerning data normalization.

同样，我们仍然需要遵循一些有关数据规范化的标准原则。

This is a bit like data normalization, making sure all of our data is on a similar scale and position.

这有点像数据标准化，确保我们所有的数据都处于类似的规模和位置。

This is an extremely powerful classification machine thatcan be applied to a wide range of data normalization problems.

它是一个非常强大的分类机器，可以应用于各种数据规范化问题。

This model is simple to learn, it doesn't require data normalization and can help to solve multiple types of problems.

该模型易于学习，不需要数据规范化，可以帮助解决多种类型的问题。

Data normalization- neural networks consist of various layers of perceptrons linked together by weighted connections.

数据标准化--神经网络由多层感知器组成，感知器由经过加权的连接相互连接。

Credibility of data has three key factors:reliability of data sources, data normalization, and the time when the data are produced.

数据的可信性由三个因素决定：数据来源的权威性、数据的规范性、数据产生的时间。

Data normalization can help you avoid getting stuck in a local optima during the training process(in the context of neural networks).

数据规范化可以帮您避免在训练过程中卡在局部最优值上(在神经网络环境中)。

A related task is data normalization, which restructures data to reduce redundancy and improve data integrity.

与之相关的任务是数据规范化，这需要重构数据以减少冗余和提高数据完整性。

Data normalization is a very important preprocessing step that is used to re-scale values to ensure better convergence during back propagation.

数据规范化是非常重要的预处理步骤，用于重新调整数值的范围，以确保在反向传播期间具有更好的收敛。

Data normalization, removal of redundant information, and outlier removal should all be performed to improve the probability of good neural network performance.

数据标准化、冗余信息消除和异常点移除都应该被用以提高性能良好的神经网络的可能性。

Data normalization is very important preprocessing step, used to rescale values to fit in a specific range to assure better convergence during backpropagation.

数据标准化是一个非常重要的预处理步骤，用来调整数值以适应特定的范围，以确保在反向传播过程中更好地收敛。

Features include flexible device modeling, device configuration, communication between devices and applications,data validation and normalization, long-term data storage and data retrieval.

它的功能包括灵活的设备模块化、设备配置、在设备和应用间的通信、数据校验和标准化、长期数据存储和数据恢复功能。

Normalization data attempts to give equal weight to all attributes.

DataNormalization使所有特征均等加权。

Normalization data attempts to give equal weight to all attributes.

规范化数据试图赋予所有属性相等的权重。

Figure 8| Data exchange and normalization.

图8|数据交换和规范化.

Data structure and normalization through multiple tables.

通过多个表让数据结构化和规范化.

This will enable software applications to interpret data meaning without normalization.

这将使软件应用程序能够解释数据意义,而不需要数据的规范化。

For instance, data element normalization is the process of organizing data elements within a data store to minimize redundancy and dependency.

例如,数据元素规范化是在数据存储中组织数据元素以减少冗余和依赖的过程。

Development DBAs need tobe skilled in the process of data modeling and normalization to ensure that databases are designed to promote data integrity.

开发DBA们需要数据模型和规范化方面的技能，以保证设计出来的数据库符合数据完整性。

Data standardization(normalization) processing is a basic work of data mining.

数据规范化数据规范化（归一化）处理是数据挖掘的一项基础工作。