english: 2009-07-15

本文探讨了电子邮件沟通中的一些基本礼仪,例如回复全体时应当谨慎考虑是否必要,并解释了CC和BCC的功能与适用场景。此外,文中还通过具体例子说明了如何恰当地使用这些功能。

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1、The Beatles represented part of the spirit of their age.

2、I have just finished the book. 我刚读完这本书

3、I am not really sure. 我不太确定

4、I am full. 我吃饱啦

5、I feared he drinks too much. 我担心他喝得太多了

6、A good heart can not lie.

7、i'll be happy the tried answer  question chenhao, but i don't have a lot of time, i have a meeting in a few minutes. Can you explain what you mean? What happened?  well they have a good point,when you get a message by email to announce a meeting, you should not reply to everyone else  recevied email, your response should only go to the sender. your reaction may have to do with your laid-back personality. laid-back means easy-going, no one wants to get unnessary email like the one you send. you are not fall out of other people's time. In the future think before you reply to all.oh i am sorry chenhao, but i have to go.sure i'll call you after the meeting.

Good afternoon, accounting departmen, this is chenhao, Hi chenhao,I am out of my meeting, i want to call you before i go home, you are not alone, lots of people have the same issue,CC means courtesy copy,   you use cc whenever you need transform someone  of what is being done. but they are not responsible for doing it . when your callee needs to be sure the  you know your duities, he sends the email to you,  however, he sends the copy to your boss. so he knows what is going on as well, you can see your boss has the email too. BCC stands for blind-courtesy-copy, it is used when you don't want  recesiver to know whoesle got the message. that's right, the only time that i find bcc exceptable is when you do not want to review all email addresses  of the people you  are mailing to for security purpose.

内容概要:本文档详细介绍了基于MATLAB实现的多头长短期记忆网络(MH-LSTM)结合Transformer编码器进行多变量时间序列预测的项目实例。项目旨在通过融合MH-LSTM对时序动态的细致学习和Transformer对全局依赖的捕捉,显著提升多变量时间序列预测的精度和稳定性。文档涵盖了从项目背景、目标意义、挑战与解决方案、模型架构及代码示例,到具体的应用领域、部署与应用、未来改进方向等方面的全面内容。项目不仅展示了技术实现细节,还提供了从数据预处理、模型构建与训练到性能评估的全流程指导。 适合人群:具备一定编程基础,特别是熟悉MATLAB和深度学习基础知识的研发人员、数据科学家以及从事时间序列预测研究的专业人士。 使用场景及目标:①深入理解MH-LSTM与Transformer结合的多变量时间序列预测模型原理;②掌握MATLAB环境下复杂神经网络的搭建、训练及优化技巧;③应用于金融风险管理、智能电网负荷预测、气象预报、交通流量预测、工业设备健康监测、医疗数据分析、供应链需求预测等多个实际场景,以提高预测精度和决策质量。 阅读建议:此资源不仅适用于希望深入了解多变量时间序列预测技术的读者,也适合希望通过MATLAB实现复杂深度学习模型的开发者。建议读者在学习过程中结合提供的代码示例进行实践操作,并关注模型训练中的关键步骤和超参数调优策略,以便更好地应用于实际项目中。
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