企业客户业务话术 问答生成 机器学习 文本生成 非监督式机器学习 生成问句

 

 

借助ltp 逐步程序化实现规则库 文本生成引擎基于规则库和业务词库 去生成文本 - xl0808tx - 博客园 http://www.cnblogs.com/yuanjiangw/p/8043877.html

 

机器学习新手使用入门  |  TensorFlow https://tensorflow.google.cn/get_started/get_started_for_beginners

模型与训练

模型即特征与标签之间的关系。对于鸢尾花问题，模型定义了花萼和花瓣测量值与鸢尾花品种之间的关系。一些简单的模型可以用几行代数进行描述；比较复杂的机器学习模型则包含大量的交错数学函数和参数，以至于难以从数学角度进行总结。

您能否在不使用机器学习的情况下确定四个特征与鸢尾花品种之间的关系？也就是说，您能否使用传统编程技巧（例如大量条件语句）创建模型？或许可以。您可以反复研究数据集来确定花瓣和花萼测量值与特定品种的正确关系。不过，一个好的机器学习方法可为您确定模型。也就是说，如果您将足够多的代表性样本馈送到正确的机器学习模型类型中，该程序将确定花萼、花瓣与品种之间的关系。

训练是一种机器学习阶段，在此阶段中，模型会逐渐得到优化（不断学习）。鸢尾花问题是监督式机器学习的一个示例，即模型通过包含标签的样本加以训练。（在非监督式机器学习中，样本不包含标签。相反，模型通常会在特征中发现一些规律。）

 

Models and training

A model is the relationship between features and the label. For the Iris problem, the model defines the relationship between the sepal and petal measurements and the predicted Iris species. Some simple models can be described with a few lines of algebra, but complex machine learning models have a large number of parameters that are difficult to summarize.

Could you determine the relationship between the four features and the Iris species without using machine learning? That is, could you use traditional programming techniques (for example, a lot of conditional statements) to create a model? Maybe. You could play with the data set long enough to determine the right relationships of petal and sepal measurements to particular species. However, a good machine learning approach determines the model for you. That is, if you feed enough representative examples into the right machine learning model type, the program will determine the relationship between sepals, petals, and species.

Training is the stage of machine learning in which the model is gradually optimized (learned). The Iris problem is an example of supervised machine learning in which a model is trained from examples that contain labels. (In unsupervised machine learning, the examples don't contain labels. Instead, the model typically finds patterns among the features.)