分类：计算机代写

计算机代写|机器学习代写machine learning代考|SME review/prototype review: Can we solve this?

Posted on 2023年5月25日2023年5月25日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写机器学习Machine Learning 这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。机器学习Machine Learning是一个致力于理解和建立 “学习 “方法的研究领域，也就是说，利用数据来提高某些任务的性能的方法。机器学习算法基于样本数据（称为训练数据）建立模型，以便在没有明确编程的情况下做出预测或决定。机器学习算法被广泛用于各种应用，如医学、电子邮件过滤、语音识别和计算机视觉，在这些应用中，开发传统算法来执行所需任务是困难的或不可行的。

机器学习Machine Learning程序可以在没有明确编程的情况下执行任务。它涉及到计算机从提供的数据中学习，从而执行某些任务。对于分配给计算机的简单任务，有可能通过编程算法告诉机器如何执行解决手头问题所需的所有步骤；就计算机而言，不需要学习。对于更高级的任务，由人类手动创建所需的算法可能是一个挑战。在实践中，帮助机器开发自己的算法，而不是让人类程序员指定每一个需要的步骤，可能会变得更加有效。

statistics-lab™ 为您的留学生涯保驾护航在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

计算机代写|机器学习代写machine learning代考|SME review/prototype review: Can we solve this?

By far the most important of the early meetings, the SME review is one you really don’t want to skip. This is the point at which a resource commit occurs. It’s the final decision on whether this project is going to happen or will be put into the backlog while a simpler problem is solved.

During this review session, the same questions should be asked as in the preceding meeting with the SME group. The only modification is that they should be tailored to answering whether the capability, budget, and desire exist for developing the full solution, now that the full scope of the work is more fully known.

The main focus of this discussion is typically on the mocked-up prototype. For our recommendation engine, the prototype may look like a synthetic wireframe of the website with a superimposed block of product image and labels associated with the product being displayed. It is always helpful, for the purposes of these demonstrations, to use real data. If you’re showing a demonstration of recommendations to a group of SME members, show their data. Show the recommendations for their account (with their permission, of course!) and gauge their responses. Record each positivebut more important, each negative-impression that they give.

计算机代写|机器学习代写machine learning代考|What if it’s terrible?

Depending on the project, the models involved, and the general approach to the ML task, the subjective rating of a prototype being “terrible” can be either trivial to fix (properly tune the model, augment the feature set, and so forth) or can be a complete impossibility (the data doesn’t exist to augment the additional feature requests, the data isn’t granular enough to solve the request, or improving the prediction to the group’s satisfaction would require a healthy dose of magic since the technology to solve that problem doesn’t exist yet).

It’s critical to quickly distill the reasons that any identified issues are happening. If the reasons are obvious and widely known as elements that can be modified by the DS team, simply answer as such. “Don’t worry, we’ll be able to adjust the predictions so that you don’t see multiple pairs of sandals right next to one another” is perfectly fine. But if the problem is of an intensely complex nature, “I really don’t want to see bohemian maxi dresses next to grunge shoes” (hopefully, you will be able to quickly search what those terms mean during the meeting), the response should be either thoughtfully articulated to the person, or recorded for a period of additional research, capped in time and effort to such research.

At the next available opportunity, the response may be along the lines of either, “We looked into that, and since we don’t have data that declares what style these shoes are, we would have to build a CNN model, train it to recognize styles, and create the hundreds of thousands of labels needed to identify these styles across our product catalog. That would likely take several years to build.” or “We looked into that, and because we have the labels for every product, we can easily group recommendations by style type to give you more flexibility around what sort of product mixing you would like.”

Make sure that you know what is and is not possible before the prototype review session. If you encounter a request that you’re not sure of, use the eight golden words of ML: “I don’t know, but I’ll go find out.”

机器学习代考

计算机代写|机器学习代写machine learning代考|SME review/prototype review: Can we solve this?

到目前为止，在早期会议中最重要的是，SME审查是您真的不想跳过的。这是发生资源提交的点。当一个更简单的问题得到解决时，它是决定这个项目是否会发生或将被放入待办事项列表的最终决定。

在这次审查会议期间，应提出与上次与中小企业小组开会时相同的问题。唯一的修改是，它们应该被裁剪，以回答是否存在开发完整解决方案的能力、预算和愿望，现在工作的全部范围已经更充分地了解了。

这个讨论的主要焦点通常是在模拟原型上。对于我们的推荐引擎，原型可能看起来像一个网站的合成线框，上面有一个叠加的产品图像块和与所显示的产品相关的标签。为了这些演示的目的，使用真实数据总是很有帮助的。如果您要向一组SME成员展示推荐演示，请显示他们的数据。向他们的账户展示推荐内容(当然，要经过他们的允许!)并评估他们的反应。记录下他们给人的每一个积极的，但更重要的是，每一个消极的印象。

计算机代写|机器学习代写machine learning代考|What if it’s terrible?

根据项目、所涉及的模型和ML任务的一般方法，对原型的主观评价“糟糕”可能是微不足道的(适当调整模型、增加功能集等)，也可能是完全不可能的(数据不存在，无法增加额外的功能请求，数据不够细粒度，无法解决请求)。或者将预测提高到团队满意的程度，需要一剂健康的魔法，因为解决这个问题的技术还不存在)。

快速提炼出任何已识别问题发生的原因是至关重要的。如果原因很明显，并且众所周知是DS团队可以修改的元素，那么就简单地回答。“别担心，我们会调整预测，这样你就不会看到多双凉鞋并排在一起了。”但如果问题非常复杂，“我真的不想看到波西米亚及地长裙和垃圾鞋放在一起”(希望你能在会议中快速搜索到这些术语的含义)，你的回答要么要深思熟虑地向对方表达，要么要记录下来，作为一段时间的额外研究，为这些研究提供时间和精力。

在下一次可用的机会中，回应可能是这样的:“我们研究了一下，因为我们没有数据表明这些鞋子是什么风格，我们必须建立一个CNN模型，训练它识别风格，并创建数十万个标签，以便在我们的产品目录中识别这些风格。”这可能需要几年的时间来建立。”或者“我们对此进行了研究，因为我们有每个产品的标签，所以我们可以很容易地根据风格类型进行分组推荐，让你更灵活地选择你喜欢的产品组合。”

确保你在原型审查会议之前知道什么是可能的，什么是不可能的。如果你遇到一个你不确定的请求，使用ML的八个黄金字:“我不知道，但我会去弄清楚的。”

计算机代写|机器学习代写machine learning代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题，以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语广义线性模型（GLM）通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归，以及方差分析和方差分析（仅含固定效应）。

有限元方法代写

有限元方法（FEM）是一种流行的方法，用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法，用于解决两个或三个空间变量的偏微分方程（即一些边界值问题）。为了解决一个问题，有限元将一个大系统细分为更小、更简单的部分，称为有限元。这是通过在空间维度上的特定空间离散化来实现的，它是通过构建对象的网格来实现的：用于求解的数值域，它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统，以模拟整个问题。然后，有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

随机分析代写

随机微积分是数学的一个分支，对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程，是依赖于参数的一组随机变量的全体，参数通常是时间。随机变量是随机现象的数量表现，其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值（如1秒，5分钟，12小时，7天，1年），因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中，往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录，以得到其自身发展的规律。

回归分析代写

多元回归分析渐进（Multiple Regression Analysis Asymptotics）属于计量经济学领域，主要是一种数学上的统计分析方法，可以分析复杂情况下各影响因素的数学关系，在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习和应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|机器学习代写machine learning代考|DEVELOPMENT SPRINT REVIEWS

Posted on 2023年5月25日2023年5月25日 by statistics-lab_01, statistics-lab_01

计算机代写|机器学习代写machine learning代考|DEVELOPMENT SPRINT REVIEWS (PROGRESS REPORTS FOR A NONTECHNICAL AUDIENCE)

Conducting recurring meetings of a non-engineering-focused bent are useful for more than just passing information from the development teams to the business. They can serve as a bellwether of the state of the project and help indicate when integration of disparate systems can begin. These meetings should still be a high-level projectfocused discussion, though.

The temptation for many cross-functional teams that work on projects like this is to turn these update meetings into either an über-retrospective or a super sprint-planning meeting. While such discussions can be useful (particularly for integration purposes among various engineering departments), those topics should be reserved for the engineering team’s meetings.

A full-team progress report meeting should make the effort to generate a currentstate demonstration of progress up to that point. Simulations of the solution should be shown to ensure that the business team and SMEs can provide relevant feedback on details that might have been overlooked by the engineers working on the project. These periodic meetings (either every sprint or every other sprint) can help prevent the aforementioned dreaded scope creep and the 11 th-hour finding that a critical component that wasn’t noticed as necessary is missing, causing massive delays in the project’s delivery.

计算机代写|机器学习代写machine learning代考|MVP REVIEW (FULL DEMO WITH UAT)

Code complete can mean different things to different organizations. In general, it is widely accepted to be a state in which

Code is tested (and passes unit/integration tests).
” The system functions as a whole in an evaluation environment using productionscale data (models have been trained on production data).
All agreed-upon features that have been planned are complete and perform as designed.

This doesn’t mean that the subjective quality of the solution is met, though. This stage simply means the system will pass recommendations to the right elements on the page for this recommendation engine example. The MVP review and the associated UAT that goes into preparing for this meeting is the stage at which subjective measures of quality are done.

What does this mean for our recommendation engine? It means that the SMEs log in to the UAT environment and navigate the site. They look at the recommendations based on their preferences and make judgments on what they see. It also means that high-value accounts are simulated, ensuring that the recommendations that the SMEs are looking at through the lens of these customers are congruous to what they know about those types of users.

For many ML implementations, metrics are a wonderful tool (and most certainly should be heavily utilized and recorded for all modeling). But the best gauge of determining whether the solution is qualitatively solving the problem is to use the breadth of knowledge of internal users and experts who can use the system before it’s deployed to end users.

At meetings evaluating the responses to UAT feedback of a solution developed over a period of months, I’ve seen arguments break out between the business and the DS team about how one particular model’s validation metrics are higher, but the qualitative review quality is much lower than the inverse situation. This is exactly why this particular meeting is so critical. It may uncover glaring issues that were missed in not only the planning phases, but in the experimental and development phases as well. Having final sanity checks on the results of the solution can only make the end result better.

There is a critical bit of information to remember about this meeting and review period dealing with estimates of quality: nearly every project carries with it a large dose of creator bias. When creating something, particularly an exciting system that has a sufficient challenge to it, the creators can overlook and miss important flaws because of familiarity with and adoration of it.
A parent can never see how ugly or stupid their children are. It’s human nature to unconditionally love what you’ve created.
-Every rational parent, ever.

机器学习代考

PMENT SPRINT REVIEWS (PROGRESS REPORTS FOR A NONTECHNICAL AUDIENCE)

定期召开以非工程为中心的会议不仅仅是将信息从开发团队传递给业务部门。它们可以作为项目状态的风向标，并帮助指示何时可以开始集成不同的系统。尽管如此，这些会议仍然应该是高层次的项目讨论。

许多从事这类项目的跨职能团队都倾向于将这些更新会议变成超级回顾会议或超级冲刺计划会议。虽然这样的讨论可能是有用的(特别是对于不同工程部门之间的集成目的)，但这些主题应该保留给工程团队的会议。

一个完整的团队进度报告会议应该努力生成到该点为止的进度的当前状态演示。应该显示解决方案的模拟，以确保业务团队和中小企业可以提供有关项目工程师可能忽略的细节的相关反馈。这些定期会议(每个sprint或每个其他sprint)可以帮助防止前面提到的可怕的范围蔓延，以及在第11个小时发现没有注意到的关键组件丢失了，从而导致项目交付的大量延迟。

计算机代写|机器学习代写machine learning代考|MVP REVIEW (FULL DEMO WITH UAT)

代码完整对于不同的组织意味着不同的东西。一般来说，它被广泛接受为一种状态

代码经过测试(并通过单元/集成测试)。
“该系统在使用生产规模数据的评估环境中作为一个整体运行(模型已经在生产数据上进行了训练)。

所有已计划的商定功能都是完整的，并按设计执行。

但是，这并不意味着解决方案的主观质量得到了满足。这个阶段仅仅意味着系统将把推荐传递给这个推荐引擎示例页面上的正确元素。MVP评审和为会议做准备的相关UAT是完成质量主观度量的阶段。

这对我们的推荐引擎意味着什么?这意味着中小企业登录到UAT环境并浏览站点。他们会根据自己的喜好查看推荐，并根据所看到的内容做出判断。这也意味着高价值的账户是模拟的，确保中小企业通过这些客户的视角看到的建议与他们对这些类型的用户的了解是一致的。

对于许多ML实现来说，度量是一个很好的工具(当然应该在所有建模中大量使用和记录)。但是，确定解决方案是否定性地解决问题的最佳标准是在将系统部署给最终用户之前使用内部用户和专家的知识广度。

在评估几个月来开发的解决方案对UAT反馈的响应的会议上，我看到业务和DS团队之间爆发了争论，争论的焦点是一个特定模型的验证度量是如何更高的，但是定性审查的质量却比相反的情况低得多。这就是为什么这次会议如此重要。它可能会发现不仅在计划阶段，而且在实验和开发阶段都被遗漏的明显问题。对解决方案的结果进行最终的完整性检查只会使最终结果更好。

关于处理质量评估的会议和审查阶段，有一点至关重要的信息需要记住:几乎每个项目都带有大量的创作者偏见。当创造某些内容时，特别是一个具有足够挑战的令人兴奋的系统，创造者可能会因为熟悉和崇拜它而忽略重要的缺陷。
父母永远看不到他们的孩子有多丑或多笨。无条件地爱自己创造的东西是人的本性。
-每一个理性的父母。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|机器学习代写machine learning代考|WHEN SHOULD WE MEET TO SHARE PROGRESS?

Posted on 2023年5月25日2023年5月25日 by statistics-lab_01, statistics-lab_01

计算机代写|机器学习代写machine learning代考|WHEN SHOULD WE MEET TO SHARE PROGRESS?

Because of the complex nature of most ML projects (particularly ones that require so many interfaces to parts of a business as a recommendation engine), meetings are critical. However, not all meetings are created equally.

While it is incredibly tempting for people to want to have cadence meetings on a certain weekly prescribed basis, project meetings should coincide with milestones associate with the project. These project-based milestone meetings should

Not be a substitute for daily standup meetings

Not overlap with team-focused meetings of individual departments

Always be conducted with the full team present

Always have the project lead present to make final decisions on contentious topics

Be focused on presenting the solution as it stands at that point and nothing else
Well-intentioned but toxic external ideation
It’s incredibly tempting for discussions to happen outside these structured presentation and data-focused meetings. Perhaps people on your team who are not involved in the project are curious and would like to provide feedback and additional brain

(continued)
storming sessions. Similarly, it could be convenient to discuss a solution to something that you’re stuck on with a small group from the larger team.

I cannot stress strongly enough how much disruption can, and likely will, arise from these outside-of-the-team discussions. Any decisions made in a large-scale project (even in the experimentation phase) by the team members should be considered sacrosanct. Involving outside voices and people who are “trying to help” erodes the inclusive communication environment that has been built collectively.

Outside ideation also typically introduces an uncontrollable chaos to the project that is difficult for everyone involved in the implementation to manage. If the DS team decides in a vacuum, for instance, to change the delivery method of the predictions (reusing a REST endpoint with additional payload data, for instance), it would affect the entire project. Even though it may save the DS team a week’s worth of work by not having to create another REST endpoint, it would be disastrous for any work that the frontend engineers are working on. This could potentially cause weeks of rework for the frontend team.

Introducing changes without notifying and discussing them in the larger group risks wasting a great deal of time and resources, which in turn erodes the confidence that the team and the business at large has in the process. It’s a fantastically effective way of having the project become shelfware or introducing silo behavior among microcosm groups of business units.

计算机代写|机器学习代写machine learning代考|But when is it going to be done?

Honesty is always the best policy. I’ve seen a lot of DS teams think that it’s wise to under-promise and over-deliver during project planning. This isn’t a wise move.

Many times, this policy of giving wiggle room to a project is employed to protect against unforeseen complexities that arise during project development. But factoring those into estimated delivery dates doesn’t do the team any favors. It’s dishonest and can erode trust that the business has in the team. The better approach is to just be honest with everyone. Let them know that ML projects have a lot of unknown factors baked into them.

The only thing this practice will result in is frustrated and angry internal business unit customers. They won’t like continually getting results weeks earlier than promised and will quickly catch on to your antics. Trust is important.

The other side of this factual omission coin relates to setting unrealistic expectations in deliveries. By not telling the business that things can go sideways during many of the phases of project work and setting an aggressive delivery date for iterative design, everyone will expect something useful to be delivered on that date. Failing to explain that these are general targets that may need slight adjustment means that the only way to accommodate unforeseen complications is by forcing the DS team to work long and grueling hours to hit those goals.

Only one result is guaranteed: team burnout. If the team is completely demotivated and exhausted from striving to meet unreasonable demands, the solution will never be very good. Details will be missed, bugs will proliferate in code, and the best members on the team will be updating their resumes to find a better job once the solution is in production.

计算机代写|机器学习代写machine learning代考|What is experimental scoping?

机器学习代考

计算机代写|机器学习代写machine learning代考|WHEN SHOULD WE MEET TO SHARE PROGRESS?

由于大多数ML项目的复杂性(特别是那些需要与业务的各个部分相连接的项目，比如推荐引擎)，会议是至关重要的。然而，并不是所有的会议都是平等的。

虽然人们想要在每周规定的基础上有节奏的会议是非常诱人的，但项目会议应该与项目相关的里程碑一致。这些基于项目的里程碑会议应该

不要代替每天的站立会议

不要与个别部门的团队会议重叠

在整个团队都在场的情况下进行

总是让项目负责人在场，对有争议的话题做出最终决定

专注于呈现当前的解决方案，而不是其他
善意但有害的外部想法
在这些结构化的演示和以数据为中心的会议之外进行讨论是非常诱人的。也许你的团队中没有参与项目的人很好奇，愿意提供反馈和额外的想法

(继续)
风暴会议。类似地，在大团队中的一个小组中讨论某个问题的解决方案也很方便。

我再怎么强调也不过分，这些团队外的讨论可能会造成多大的破坏。团队成员在大型项目(甚至在实验阶段)中做出的任何决定都应该被认为是神圣不可侵犯的。让外界的声音和“试图帮助”的人参与进来，会侵蚀集体建立起来的包容性沟通环境。

外部构思通常也会给项目带来无法控制的混乱，这对参与实施的每个人来说都是难以管理的。例如，如果DS团队在真空中决定更改预测的交付方法(例如，重用带有额外有效负载数据的REST端点)，则会影响整个项目。尽管它可以省去DS团队一周的工作，因为它不必创建另一个REST端点，但对于前端工程师正在进行的任何工作来说，这将是灾难性的。这可能会给前端团队带来数周的返工。

在没有通知和讨论的情况下引入变更可能会浪费大量的时间和资源，这反过来又会削弱团队和业务在整个过程中的信心。这是一种非常有效的方法，可以让项目成为架子软件，或者在业务单元的微观组中引入筒仓行为

计算机代写|机器学习代写machine learning代考|But when is it going to be done?

诚实永远是上策。我看到许多DS团队认为在项目规划期间少承诺和多交付是明智的。这不是明智之举。

很多时候，这种给项目留有回旋余地的策略被用来防止项目开发过程中出现的不可预见的复杂性。但是，将这些因素纳入预计的交付日期对团队没有任何好处。这是不诚实的，而且会破坏企业对团队的信任。更好的方法是对每个人都诚实。让他们知道机器学习项目有很多未知的因素。

这种做法只会导致内部业务部门的客户感到沮丧和愤怒。他们不喜欢总是比承诺提前几周得到结果，很快就会发现你的滑稽行为。信任很重要。

事实遗漏硬币的另一面与在交付中设定不切实际的期望有关。通过不告诉业务人员在项目工作的许多阶段中事情可能会出现偏差，并为迭代设计设定一个积极的交付日期，每个人都会期望在那个日期交付有用的东西。未能解释这些是可能需要稍微调整的一般目标，这意味着适应不可预见的复杂性的唯一方法是强迫DS团队长时间工作以实现这些目标。

只有一个结果是肯定的:团队倦怠。如果团队因为努力满足不合理的要求而完全失去动力和疲惫，那么解决方案永远不会很好。细节会被遗漏，代码中的bug会激增，一旦解决方案投入生产，团队中最优秀的成员会更新他们的简历，寻找更好的工作。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|数据库作业代写Database代考|DATABASE OPERATIONS

Posted on 2023年5月18日2023年5月18日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写数据库Database这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

结构化查询语言（SQL）是一种标准化的编程语言，用于管理关系型数据库并对其中的数据进行各种操作。

statistics-lab™ 为您的留学生涯保驾护航在代写数据库Database方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据库Database代写方面经验极为丰富，各种代写数据库Database相关的作业也就用不着说。

我们提供的数据库Database及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|数据库作业代写Database代考|DATABASE OPERATIONS

Eight operations were originally defined for relational databases, and they form the core of modern database operations. The following list describes those original operations:

Selection-This selects some or all of the records in a table. For example, you might want to select only the Competitors records where Event is Pyramid Luge so that you can know who to expect for that event (and how many ambulances to have standing by).

Projection-This drops columns from a table or selection. For example, when you make your list of Pyramid Luge competitors, you may want to list only their names and not their addresses, blood types, events (which you know is Pyramid Luge anyway), or next of kin.
Union-This combines tables with similar columns and removes duplicates. For example, suppose you have another table named FormerCompetitors that contains data for people who participated in previous years’ competitions. Some of these people are competing this year and some are not. You could use the union operator to build a list of everyone in either table. (Note that the operation would remove duplicates, but for these tables you would still get the same person several times with different events.)
Intersection-This finds the records that are the same in two tables. The intersection of the FormerCompetitors and Competitors tables would list those few who competed in previous years and who survived to compete again this year (i.e., the slow learners).

Difference-This selects the records in one table that are not in a second table. For example, the difference between FormerCompetitors and Competitors would give you a list of those who competed in previous years but who are not competing this year (so you can email them and ask them what the problem is).

Cartesian Product-This creates a new table containing every record in a first table combined with every record in a second table. For example, if one table contains values $1,2,3$, and a second table contains values A, B, C, then their Cartesian product contains the values $1 / \mathrm{A}$, $1 / \mathrm{B}, 1 / \mathrm{C}, 2 / \mathrm{A}, 2 / \mathrm{B}, 2 / \mathrm{C}, 3 / \mathrm{A}, 3 / \mathrm{B}$, and $3 / \mathrm{C}$.

Join-This is similar to a Cartesian product except records in one table are paired only with those in the second table if they meet some condition. For example, you might join the Competitors records with the NextOfKin records where a Competitors record’s NextOfKin value matches the NextOfKin record’s Name value. In this example, that gives you a list of the competitors together with their corresponding next of kin data.

Divide-This operation is the opposite of the Cartesian product. It uses one table to partition the records in another table. It finds all of the field values in one table that are associated with every value in another table. For example, if the first table contains the values $1 / \mathrm{A}, 1 / \mathrm{B}$, $1 / \mathrm{C}, 2 / \mathrm{A}, 2 / \mathrm{B}, 2 / \mathrm{C}, 3 / \mathrm{A}, 3 / \mathrm{B}$, and $3 / \mathrm{C}$ and a second table contains the values $1,2,3$, then the first divided by the second gives A, B, C. (Don’t worry, I think it’s pretty weird and confusing, too, so it probably won’t be on the final exam.)

计算机代写|数据库作业代写Database代考|POPULAR RDBs

There are many relational database products available for you to use. All provide the same basic features, such as the ability to build tables, perform CRUD operations, carry out the eight basic relational database operations (selection, projection, union, etc.), define indexes and keys, and so forth.
They all also provide some form of SQL. SQL is a standardized language, so many queries are the same in most RDBMSs, although there are some slight differences. For example, different systems call a 4-byte integer an INT, INTEGER, NUMBER(4), or INT4. Many of these differences affect statements that modify the database (such as adding or deleting tables) rather than queries.

You can find a good SQL tutorial at www. w3schools. com/sql.
For a catalog of SQL differences on different RDBMSs, see https : / en . wikibooks . org/wiki/ SQL_Dialects_Reference.

You can find a list of around 100 RDBMSs at https://en.wikipedia.org/wiki/List_of_ relational_database_management_systems and a similar list at https://database .guide/list-of-relational-database-management-systems-rdbms.

The following list shows the most popular RDBMSs in use as of September 2022 according to the DB-Engines page, https://db-engines.com/en/ranking/relational+dbms :

Oracle
$\mathrm{MySQL}$
Microsoft SQL Server
PostgreSQL
IBM Db2
Microsoft Access
SQLite
MariaDB
Snowflake
Microsoft Azure SQL Database
I started writing a summary of each of the products, but found that they were so similar that it wasn’t worth the effort. The following paragraphs describe some of their common features and give a few details for specific products.

数据库代考

计算机代写|数据库作业代写Database代考|DATABASE OPERATIONS

八种操作最初是为关系数据库定义的，它们构成了现代数据库操作的核心。这些原始操作描述如下:

选择—选择表中的部分或全部记录。例如，您可能希望只选择Event为Pyramid Luge的参赛者记录，这样您就可以知道谁将参加该事件(以及有多少救护车待命)。

投影—从表或选择项中删除列。例如，当你列出金字塔雪橇选手的名单时，你可能只想列出他们的名字，而不是他们的地址、血型、赛事(你知道这是金字塔雪橇)或近亲。
联合—将具有相似列的表组合起来，并删除重复列。例如，假设您有另一个名为formerrivals的表，其中包含参加前几年比赛的人的数据。其中一些人参加了今年的比赛，有些人没有。您可以使用联合操作符构建任一表中每个人的列表。(注意，该操作将删除重复项，但对于这些表，您仍然可以通过不同的事件多次获得同一个人。)
交集——查找两个表中相同的记录。“前竞争者”和“竞争者”表格的交叉点将列出前几年参加比赛的少数人，以及今年幸存下来再次参加比赛的人(即慢学习者)。

差异—选择一个表中不在另一个表中的记录。例如，“前竞争者”和“竞争者”之间的区别会给你一个前几年参加过比赛但今年没有参加比赛的人的列表(这样你就可以给他们发邮件，问他们问题出在哪里)。

笛卡尔积——这将创建一个新表，其中包含第一个表中的每条记录和第二个表中的每条记录。例如，如果一个表包含值$1,2,3$，另一个表包含值a, B, C，那么它们的笛卡尔积包含值$1 / \ mathm {a}$， $1 / \ mathm {B}， 1 / \ mathm {C}， 2 / \ mathm {a}， 2 / \ mathm {B}， 2 / \ mathm {C}， 3 / \ mathm {a}， 3 / \ mathm {B}$和$3 / \ mathm {C}$。

join—这类似于笛卡尔积，除了一个表中的记录只有在满足某些条件时才与另一个表中的记录配对。例如，你可以将竞争者记录与NextOfKin记录连接起来，其中竞争者记录的NextOfKin值与NextOfKin记录的Name值匹配。在本例中，它为您提供了竞争对手列表及其相应的近亲数据。

除法，这个运算与笛卡尔积相反。它使用一个表对另一个表中的记录进行分区。它在一个表中查找与另一个表中的每个值相关联的所有字段值。例如，如果第一个表包含值$1 / \ mathm {A}， 1 / \ mathm {B}$， $1 / \ mathm {C}， 2 / \ mathm {A}， 2 / \ mathm {B}， 2 / \ mathm {C}， 3 / \ mathm {A}， 3 / \ mathm {B}$和$3 / \ mathm {C}$，第二个表包含值$1,2,3$，那么第一个表除以第二个表就得到A, B, C。(别担心，我认为这很奇怪，也很令人困惑，所以期末考试可能不会出现。)

计算机代写|数据库作业代写Database代考|POPULAR RDBs

有许多关系数据库产品可供您使用。它们都提供相同的基本特性，例如构建表、执行CRUD操作、执行八种基本关系数据库操作(选择、投影、联合等)、定义索引和键，等等。
它们都提供了某种形式的SQL。SQL是一种标准化语言，因此大多数rdbms中的许多查询都是相同的，尽管存在一些细微的差异。例如，不同的系统将4字节整数称为INT、integer、NUMBER(4)或INT4。其中许多差异影响的是修改数据库的语句(比如添加或删除表)，而不是查询。

你可以在www上找到一个很好的SQL教程。w3schools网。com/sql。
有关不同rdbms上SQL差异的目录，请参见https: / zh。维基教科书。org/wiki/ SQL_Dialects_Reference。

您可以在https://en.wikipedia.org/wiki/List_of_ relational_database_management_systems上找到大约100个rdbms的列表，并在https://database .guide/list-of-relational-database-management-systems-rdbms上找到类似的列表。

以下列表显示了截至2022年9月使用的最流行的rdbms(根据DB-Engines页面https://db-engines.com/en/ranking/relational+dbms):

甲骨文

$ \ mathrm {MySQL} $

Microsoft SQL Server

PostgreSQL

IBM Db2

Microsoft Access

SQLite

MariaDB

雪花

Microsoft Azure SQL数据库
我开始为每个产品写一个总结，但发现它们太相似了，不值得花时间。以下段落描述了它们的一些共同特征，并给出了特定产品的一些细节。

计算机代写|数据库作业代写Database代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|数据库作业代写Database代考|KEYS

Posted on 2023年5月18日2023年5月18日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写数据库Database这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

结构化查询语言（SQL）是一种标准化的编程语言，用于管理关系型数据库并对其中的数据进行各种操作。

我们提供的数据库Database及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|数据库作业代写Database代考|Legal and Security Considerations

计算机代写|数据库作业代写Database代考|KEYS

Relational database terminology includes an abundance of different flavors of keys. (They are key terms, so you could say that relational databases have a lot of key key terms.) In the loosest sense, a key is a combination of one or more columns that you use to find rows in a table. For example, a Customers table might use CustomerID to find customers. If you know a customer’s ID, then you can quickly find that customer’s record in the table. (In fact, many ID numbers, such as employee IDs, student IDs, driver’s license numbers, and so forth, were invented just to make searching in database tables easier. My library card certainly doesn’t include a 10-digit ID number for $m y$ convenience.)
The more formal relational vocabulary includes several other more precise definitions of keys.

In general, a key is a set of one or more columns in the table that have certain properties. A compound key or composite key is a key that includes more than one column. For example, you might use the combination of FirstName and LastName to look up customers.

A superkey is a set of one or more columns in a table for which no two rows can have the exact same values. For example, in the Competitors table shown in Figure 2.1, the Name, Address, and Event columns together form a superkey because no two rows have exactly the same Name, Address, and Event values. Because superkeys define fields that must be unique within a table, they are sometimes called unique keys.
Because no two rows in the table have the same values for a superkey, a superkey can uniquely identify a particular row in the table. In other words, a program could use a superkey to find any particular record.

A candidate key is a minimal superkey. That means if you remove any of the columns from the superkey, it won’t be a superkey anymore.
For example, you already know that Name/Address/Event is a superkey for the Competitors table. If you remove Event from the superkey, you’re left with Name/Address. This is not a superkey because everyone in the table is participating in multiple events, and therefore they have more than one record in the table with the same name and address.

If you remove Name, then Address/Event is not a superkey because Dean Daring and his roommate George Foreman share the same address and are both signed up for Pyramid Luge. (They also have the same blood type. They became friends and decided to become roommates when Dean donated blood for George after a particularly flamboyant skateboarding accident.)

Finally if you remove Address, then Name/Event is still a superkey. That means Name/Address/Event is not a candidate key because it is not minimal. However, Name/Event is a candidate key because no two rows have the same Name/Event values and you can easily see neither Name nor Event is a superkey, so the pair is minimal.

计算机代写|数据库作业代写Database代考|INDEXES

An index is a database structure that makes it quicker and easier to find records based on the values in one or more fields. Indexes are not the same as keys, although the two are related closely enough that many developers confuse the two and use the terms interchangeably.

For example, suppose you have a Customers table that holds customer information: name, address, phone number, Swiss bank account number, and so forth. The table also contains a CustomerId field that it uses as its primary key.

Unfortunately, customers usually don’t remember their customer IDs (I know I don’t), so you need to be able to look them up by name or phone number. If you make Name and PhoneNumber two different keys, then you can quickly locate a customer’s record in three ways: by customer ID, by name, and by phone number.

NOTE Relational databases also make it easy to look up records based on non-indexed fields, although it may take a while. If the customer only remembers their address and not their customer ID or name, you can search for the address even if it that field isn’t part of an index. It may just take a long time. Of course, if the customer cannot remember their name, then they have bigger problems.
Building and maintaining an index takes the database some extra time, so you shouldn’t make indexes gratuitously. Place indexes on the fields that you are most likely to need to search and don’t bother indexing fields like apartment number or telephone extension, which you’re unlikely to need to search.

数据库代考

计算机代写|数据库作业代写Database代考|KEYS

关系数据库术语包括大量不同类型的键。(它们是关键术语，所以你可以说关系数据库有很多关键术语。)从最宽松的意义上讲，键是用于查找表中的行的一个或多个列的组合。例如，Customers表可能使用CustomerID来查找客户。如果您知道客户的ID，那么您可以在表中快速找到该客户的记录。(实际上，许多ID号，比如员工ID、学生ID、驾驶执照号码等等，都是为了方便在数据库表中进行搜索而发明的。为了方便起见，我的借书证上当然没有10位数的身份证号。)
更正式的关系词汇表包括其他几个更精确的键定义。

通常，键是表中具有某些属性的一个或多个列的集合。复合键或组合键是包含多个列的键。例如，您可能使用FirstName和LastName的组合来查找客户。

超级键是表中一个或多个列的集合，其中没有两行可以具有完全相同的值。例如，在图2.1所示的competers表中，Name、Address和Event列一起形成一个超级键，因为没有两行具有完全相同的Name、Address和Event值。因为超键定义的字段在表中必须是唯一的，所以它们有时被称为唯一键。
由于表中没有任何两行具有相同的超级键值，因此超级键可以唯一地标识表中的特定行。换句话说，程序可以使用超级键来查找任何特定的记录。

候选键是最小超级键。这意味着如果你从超级键中删除任何列，它就不再是超级键了。
例如，您已经知道Name/Address/Event是competers表的超级键。如果从超级键中删除Event，则只剩下Name/Address。这不是一个超级键，因为表中的每个人都参与多个事件，因此他们在表中有多个具有相同名称和地址的记录。

如果你删除了Name，那么Address/Event就不是一个超级键，因为Dean Daring和他的室友George Foreman共享同一个地址，并且都注册了Pyramid Luge。(他们也有相同的血型。他们成为了朋友，并决定成为室友，当时迪恩在一次特别引人注目的滑板事故后为乔治献血。)

最后，如果您删除地址，那么名称/事件仍然是一个超级键。这意味着Name/Address/Event不是候选键，因为它不是最小值。但是，Name/Event是候选键，因为没有两行具有相同的Name/Event值，并且您可以很容易地看到Name和Event都不是超级键，因此这对是最小的。

计算机代写|数据库作业代写Database代考|INDEXES

索引是一种数据库结构，它使查找基于一个或多个字段中的值的记录变得更快、更容易。索引与键并不相同，尽管这两者关系密切，以至于许多开发人员混淆了这两者，并交替使用这两个术语。

例如，假设您有一个保存客户信息的Customers表:姓名、地址、电话号码、瑞士银行账号等等。该表还包含一个CustomerId字段，它将其用作主键。

不幸的是，客户通常不记得他们的客户id(我知道我不记得)，所以您需要能够通过姓名或电话号码查找他们。如果您将Name和PhoneNumber设置为两个不同的键，那么您可以通过三种方式快速定位客户记录:通过客户ID、通过姓名和通过电话号码。

关系数据库也可以很容易地查找基于非索引字段的记录，尽管这可能需要一些时间。如果客户只记得他们的地址，而不记得他们的客户ID或名称，那么即使该字段不是索引的一部分，也可以搜索地址。这可能需要很长时间。当然，如果客户记不住自己的名字，那么他们就有更大的问题了。
构建和维护索引会占用数据库一些额外的时间，因此不应该无缘无故地创建索引。将索引放在最有可能需要搜索的字段上，不要为公寓号码或电话分机等不太可能需要搜索的字段建立索引。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|数据库作业代写Database代考|Legal and Security Considerations

Posted on 2023年5月18日2023年5月18日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写数据库Database这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

结构化查询语言（SQL）是一种标准化的编程语言，用于管理关系型数据库并对其中的数据进行各种操作。

我们提供的数据库Database及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|数据库作业代写Database代考|Cloud Considerations

A cloud database hosts data in the cloud so it is accessible over a network. There are two common cloud deployment models. First, you can rent space on a virtual machine and run the database there. A virtual machine (VM) is a simulation of a physical computer running on a server somewhere.

Virtual machines have the advantage that the cloud provider can move them around, possibly hosting multiple VMs on a single physical machine. Conversely, you may also be able to use multiple computers to host a single VM. Those two capabilities together make it easier to scale an application up or down as needed.

For example, suppose you write some software to schedule appointments for nail salons. Initially you serve only a few salons, so your VM uses a small fraction of one physical server. Over time, as more and more salons sign up for your service, you need more space and faster processing, so you start using more of the server. Soon your provider moves you onto faster hardware and eventually onto a small group of servers (for a price, of course).

NOT SO VIRTUAL MACHINES
Instead of renting a virtual machine, you can rent a physical machine or even buy your own and put it on your network. Then you have full use of the machine.
That approach works and has some advantages, such as giving you complete control (and commensurate responsibility) and letting you know exactly where your data is, but it isn’t really cloud computing and it doesn’t give you the same easy scaling advantages.

计算机代写|数据库作业代写Database代考|Legal and Security Considerations

I won’t talk too much about legal issues in this book, but you should determine whether you might encounter any of them. For example, I already mentioned data residency and data sovereignty earlier in this chapter. Some countries require that certain kinds of data reside physically within their borders, and you could be in big trouble if your data is stored in the cloud on foreign servers.
In addition to ensuring that your cloud servers have allowed physical locations, you need to ensure that your data is properly protected. For example, in the United States, HIPAA (which stands for the Health Insurance Portability and Accountability Act and is pronounced “hip-uh”) prohibits the disclosure of a patient’s sensitive medical information without their consent or knowledge. I don’t believe HIPAA requires data residency (but I’m not a lawyer, so don’t take my word for it), but some states have their own special requirements. For example, all 50 U.S. states plus Washington D.C., Puerto Rico, and the U.S. Virgin Islands have some sort of law requiring you to notify residents if their personal information is compromised in a security breach.

Obviously sensitive information like credit card numbers, bank account numbers, Social Security numbers, driver’s license numbers, website passwords, biometric data, business information, and other important items require top-notch security.

Certain other kinds of data are also considered personal and/or sensitive and may or may not be protected by law. Personally identifiable information $(P I I)$ is information that could be used to assist with identity theft and includes such items as a person’s name, mother’s maiden name, address and former addresses, phone numbers, and so on. Sensitive data may include gender identity, ethnic background, political or religious affiliation, union membership, and more.

数据库代考

计算机代写|数据库作业代写Database代考|Cloud Considerations

云数据库在云中托管数据，因此可以通过网络访问数据。有两种常见的云部署模型。首先，您可以在虚拟机上租用空间并在那里运行数据库。虚拟机(VM)是在某处服务器上运行的物理计算机的模拟。

虚拟机的优势在于云提供商可以移动它们，可能在单个物理机器上托管多个虚拟机。相反，您也可以使用多台计算机来托管单个VM。这两个功能结合在一起，可以更容易地根据需要向上或向下扩展应用程序。

例如，假设您编写了一些软件来安排美甲沙龙的约会。最初您只服务几个沙龙，因此VM使用一个物理服务器的一小部分。随着时间的推移，随着越来越多的沙龙注册使用您的服务，您需要更多的空间和更快的处理速度，因此您开始使用更多的服务器。很快，您的提供商将您转移到更快的硬件上，并最终转移到一小组服务器上(当然，这是有代价的)。

不是虚拟机
不需要租用虚拟机，您可以租用物理机，甚至可以购买自己的物理机并将其放在网络中。这样你就可以充分利用这台机器了。
这种方法是有效的，并且有一些优点，比如给你完全的控制(和相应的责任)，让你确切地知道你的数据在哪里，但它不是真正的云计算，它不能给你同样容易扩展的优势。

计算机代写|数据库作业代写Database代考|Legal and Security Considerations

在这本书中，我不会过多地谈论法律问题，但你应该确定自己是否会遇到这些问题。例如，我在本章前面已经提到了数据驻留和数据主权。一些国家要求某些类型的数据物理上驻留在其境内，如果您的数据存储在外国服务器上的云中，您可能会遇到大麻烦。
除了确保您的云服务器允许物理位置之外，您还需要确保您的数据得到适当保护。例如，在美国，HIPAA(代表健康保险流通与责任法案，发音为“hip-uh”)禁止在未经患者同意或不知情的情况下披露患者的敏感医疗信息。我不相信HIPAA要求数据驻留(但我不是律师，所以不要相信我的话)，但有些州有自己的特殊要求。例如，美国所有50个州以及华盛顿特区、波多黎各和美属维尔京群岛都有某种法律要求您通知居民，如果他们的个人信息在安全漏洞中受到损害。

显然，信用卡号、银行账号、社会保险号、驾照号、网站密码、生物识别数据、商业信息和其他重要信息等敏感信息需要一流的安全保护。

某些其他类型的数据也被视为个人和/或敏感数据，可能受法律保护，也可能不受法律保护。个人身份信息是可以用来协助身份盗窃的信息，包括个人姓名、母亲的婚前姓名、地址和以前的地址、电话号码等。敏感数据可能包括性别认同、种族背景、政治或宗教信仰、工会会员资格等。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|复杂网络代写complex network代考|Results

Posted on 2023年5月16日2023年5月16日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

在网络理论的背景下，复杂网络是具有非微观拓扑特征的图（网络）这些特征在格子或随机图等简单网络中不出现，但在代表真实系统的网络中经常出现。

statistics-lab™ 为您的留学生涯保驾护航在代写复杂网络complex network方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写复杂网络complex network代写方面经验极为丰富，各种代写复杂网络complex network相关的作业也就用不着说。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|Open Positions, Teams, and Candidate Pool

计算机代写|复杂网络代写complex network代考|Results

Network of words. Only about $7 \%$ (484 out of 7328$)$ of all papers are TwMLrelated. Previous studies have empirically observed that complex methods such as knowledge graphs or high-dimensional numeric embeddings are less reliable for characterizing rare concepts or terms $[15,29]$. Because of this rarity issue of TwML papers, we use a word co-occurrence network in place of more sophisticated methods. The resulting network contains 10,698 nodes and 254,347 edges.
The community detection algorithm generated 25 communities, with a modularity score of 0.33 . As given in Table 1, TwML-related words are concentrated in two communities. Among them, seven words that are mostly related to Differential Privacy (DP) separate from the rest into one community (second row in Table 1). Another community of 1127 words contains 26 other TwML-specific words. For convenience we shall refer to these communities as DP and nonDP community, respectively. The remaining 8 TwML words – which are mostly ambiguous such as ‘metric’ or ‘procedur’ or general such as ‘trustworthi’-get distributed across 6 communities.

Figure 2 visualizes the overall network, focusing on the two TwML-specific communities. We categorize the TwML words into four subject-based categories:

Privacy: ‘privaci’, ‘differenti’, ‘privat’, ‘guarantee’, ‘concern’,’preserv’,
Interpretability: ‘transpar’,’interpret’,’account’,
General: ‘trustworthi’, ‘mechan’,’algorithm’,’data’,
Fairness: all others.
From the relative position of words in each category in Fig. 2, it is evident that a number of privacy-specific and fairness-specific words cluster together, and these two clusters are well-separated from each other.

计算机代写|复杂网络代写complex network代考|Discussion

A number of interesting insights come out from the above analysis.
Network of Words. The differential distribution of TwML words within communities, as observed in Table 1, indicates that TwML papers tend to focus more on certain lines of research, methods or applications than others. In the context of ML bias and fairness, this is echoed by the review article of [16]. They observed that addressing group fairness in classification problems has received disproportionately high interest compared to other fairness categories (e.g. individual fairness, subgroup fairness) and types of methods (e.g. clustering, graph embedding); see Table 7 therein. Within the TwML words, Differential Privacy (DP)-specific words and those related to fairness and transparency group separately into two different communities. A potential reason for this may be that DP is a comparatively older research area, and has seen more theoretical developments than relatively new topics like fairness or transparency.

Paper-level Fingerprinting. All papers in Table 3 with high relevance scores are on comparatively complex algorithms. A number of these areas have been heavily researched of late, such as reinforcement learning (RL; papers 1,14,19), bandit problems $(2,4,18)$, anomaly detection $(2,9,10,11)$, representation learning $(11,13,15)$, multitask problems $(2,8,10,13)$, dirichlet process $(3,22)$, and nonconvex optimization $(24,25)$.

复杂网络代写

计算机代写|复杂网络代写complex network代考|Results

词语网络。所有论文中只有约7%(7328篇中的484篇)与twml相关。以前的研究已经通过经验观察到，复杂的方法，如知识图或高维数字嵌入，在描述罕见概念或术语方面不太可靠[15,29]。由于TwML论文的稀缺性问题，我们使用单词共现网络来代替更复杂的方法。最终的网络包含10698个节点和254347条边。
社区检测算法生成了25个社区，模块化得分为0.33。如表1所示，与twml相关的词集中在两个社区。其中，与差分隐私(DP)最相关的7个单词与其他单词分离成一个社区(表1第二行)。另一个包含1127个单词的社区包含26个其他twml特定单词。为方便起见，我们将这些社区分别称为DP和nonDP社区。剩下的8个TwML单词——大多是模棱两可的，如“度量”或“程序”，或一般的，如“值得信赖”——分布在6个社区。

图2显示了整个网络，重点关注两个特定于twml的社区。我们将TwML单词分为四个基于主题的类别:

隐私:’privaci’， ‘different ‘，’ privaci’， ‘guarantee’， ‘concern’，’ preserve ‘，

可解释性:“transpar”、“解释”、“账户”,

通用:“值得信赖”、“机制”、“算法”、“数据”、

公平:所有其他的。
从图2中每个类别中单词的相对位置可以明显看出，许多特定于隐私和特定于公平的单词聚在一起，并且这两个聚类彼此分离得很好。

计算机代写|复杂网络代写complex network代考|Discussion

从上述分析中得出了一些有趣的见解。
词语网络。如表1所示，社区内TwML单词的差异分布表明，TwML论文往往更关注某些研究、方法或应用领域。在机器学习偏见和公平性的背景下，[16]的综述文章也回应了这一点。他们观察到，与其他公平类别(如个人公平、子群体公平)和方法类型(如聚类、图嵌入)相比，在分类问题中解决群体公平问题获得了不成比例的高兴趣;见其中表7。在TwML单词中，特定于差分隐私(DP)的单词和与公平和透明度相关的单词分别分为两个不同的社区。造成这种情况的一个潜在原因可能是，DP是一个相对较老的研究领域，与公平或透明度等相对较新的主题相比，它已经看到了更多的理论发展。

Paper-level指纹。表3中相关度较高的论文都是关于比较复杂的算法。其中一些领域最近得到了大量的研究，比如强化学习(RL;论文1,14,19)，盗匪问题$(2,4,18)$，异常检测$(2,9,10,11)$，表示学习$(11,13,15)$，多任务问题$(2,8,10,13)$，dirichlet过程$(3,22)$和非凸优化$(24,25)$。

cs代写|复杂网络代写complex network代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|复杂网络代写complex network代考|Open Positions, Teams, and Candidate Pool

Posted on 2023年5月16日2023年5月16日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|Open Positions, Teams, and Candidate Pool

For each network, we run 100 trials. In each trial, we sample $10 \%, 20 \%$, and $30 \%$ of nodes randomly as open positions. To simulate the pool of candidates we consider two cases: (1) the candidate pool consists of the nodes set to open (with the same attributes), and (2) the candidate pool consists of two copies of each node set to open. The first setting corresponds to the case where a ‘batch’ of new employees has been hired, and now the employees need to be assigned to teams without considering the hiring process. The second setting corresponds to the case where we consider both hiring and assignment procedures. Moreover, the way we assign attributes to nodes ensures that changes in homophily are actually due to employee assignment, rather than changes in attributes.

The fitness function governs which candidates are suitable for which positions. For the first sets of experiments- the evaluation of FairEA- we consider two fitness functions. In $F_1$, candidates are qualified for four randomly selected positions with fitness equal to a random number in $(0,1)$. In $F_2$, candidates are fit for the four open positions closest to the position that the candidate had previously filled with fitness equal to a random number in $(0,1)$.

计算机代写|复杂网络代写complex network代考|Baseline Methods

We use three baseline methods: (1) Random, which randomly assigns qualified candidates to each open position; (2) The weighted Hungarian algorithm, where the input is a bipartite graph whose two sides correspond to open positions and candidates. An edge $\left(o_a, c_b\right)$ exists if $w_{a b}>0$, and the weight of this edge is the sum of $w_{a b}$ and the diversity score as described in Sect. 4; and (3) Optimization, which uses the IPOPT solver in the GEKKO optimization suite [1] for solving the optimization problem with the two goals of maximizing fitness and diversity. This is the simplified version of the problem where fitness is maximized, as described in Sect. 3.1 and diversity is optimized by decreasing the gap between number of neighbors from class $_i$ to number of neighbors from class $_j$ for each newly assigned position.

We report results using the following metrics:

The overall fit score is the sum of the fitness scores for each matching. Let $F S_h$ and $F S_l$ be overall fit score of the best and worst possible matching in terms of fitness of employees for the open positions respectively and $F S_a$ be the overall fit score of the network $G$ after assignment using desired method. Then we define Percentage Improvement in Fitness $=\frac{F S-F S_l}{F S_h-F S_l} \cdot 100$.
The diversity of the network is measured by the assortativity coefficient [17]. Let $A C_b$ be the assortativity coefficient of $G^{\prime}$, the subgraph of initial network $G$ consisting only of filled positions, and $A C_b$ be the (assortativity coefficient of the network $G$ after assignments are made. Then the Percentage Improvement in Assortativity $=\frac{\left|A C_b\right|-\left|A C_a\right|}{\left|A C_b\right|} \cdot 100$.
The fraction of minorities in team $i$ is $F M_i=\frac{\min \left(\left|c 1_i\right|, . .\left|c k_i\right|\right)}{\left|c 1_i\right|+\ldots\left|c k_i\right|}$ where $\left|c j_i\right|$ is the number of individuals from class $_j$ in team $i$. Isolation Score is the average fraction of minorities. Isolation Score $=\frac{1}{k} \cdot \sum_{1 \leq i \leq k} F M_i$.

复杂网络代写

计算机代写|复杂网络代写complex network代考|Open Positions, Teams, and Candidate Pool

对于每个网络，我们运行100次试验。在每次试验中，我们随机抽取$10 \%, 20 \%$和$30 \%$的节点作为开放位置。为了模拟候选池，我们考虑两种情况:(1)候选池由要打开的节点组成(具有相同的属性)，(2)候选池由每个要打开的节点集的两个副本组成。第一个设置对应于“一批”新员工被雇用的情况，现在需要将员工分配到团队中，而不考虑招聘流程。第二个设置对应于我们同时考虑雇用和分配程序的情况。此外，我们将属性分配给节点的方式确保了同质性的变化实际上是由于员工分配，而不是属性的变化。

适应度函数决定了哪个候选人适合哪个职位。对于第一组实验-公平评估-我们考虑两个适应度函数。在$F_1$中，候选人有资格获得四个随机选择的职位，适应度等于$(0,1)$中的一个随机数。在$F_2$中，候选人适合于最接近候选人之前所填补的职位的四个空缺职位，其适合度等于$(0,1)$中的一个随机数。

计算机代写|复杂网络代写complex network代考|Baseline Methods

我们使用三种基线方法:(1)随机，随机分配符合条件的候选人到每个空缺职位;(2)加权匈牙利算法，其中输入是一个二部图，其两边对应于开放位置和候选位置。如果$w_{a b}>0$存在一条边$\left(o_a, c_b\right)$，该边的权值为$w_{a b}$与第4节中描述的多样性得分之和;(3)优化，使用GEKKO优化套件[1]中的IPOPT求解器来解决以适应度最大化和多样性最大化为两个目标的优化问题。这是问题的简化版本，其中适应度最大化，如3.1节所述，并且通过减少每个新分配位置的$_i$类的邻居数量与$_j$类的邻居数量之间的差距来优化多样性。

我们使用以下指标报告结果:

总体适合度分数是每次匹配的适合度分数之和。设$F S_h$和$F S_l$分别为空缺职位的员工适合度的最佳和最差可能匹配的总体适合度得分，$F S_a$为使用期望方法分配后的网络$G$的总体适合度得分。然后我们定义健身百分比改善$=\frac{F S-F S_l}{F S_h-F S_l} \cdot 100$。

网络的多样性通过选型系数来衡量[17]。设$A C_b$为初始网络$G$只包含填充位置的子图$G^{\prime}$的选型系数，$A C_b$为分配后的网络$G$的选型系数。然后是分类性的百分比改进$=\frac{\left|A C_b\right|-\left|A C_a\right|}{\left|A C_b\right|} \cdot 100$。

团队$i$中少数族裔的比例为$F M_i=\frac{\min \left(\left|c 1_i\right|, . .\left|c k_i\right|\right)}{\left|c 1_i\right|+\ldots\left|c k_i\right|}$，其中$\left|c j_i\right|$是团队$i$中来自班级$_j$的个人人数。隔离分数是少数民族的平均分数。隔离评分$=\frac{1}{k} \cdot \sum_{1 \leq i \leq k} F M_i$。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|复杂网络代写complex network代考|On Measuring the Diversity of Organizational Networks

Posted on 2023年5月16日2023年5月16日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|On Measuring the Diversity of Organizational Networks

In order for commercial and non-profit organizations to succeed, it is important for those organizations to recruit a workforce that is not only skilled, but also diverse, as diversity has been positively associated with performance [15]. However, diversity cannot be measured only in terms of numbers: it is known that negative effects may happen when a network is structured in a way that resources are not accessible through the social capital accessible to members of a minority group [9]. Social capital consists of bridging resources from outside of an individual’s group (inter-group connections) and bonding resources from internal group connections (intra-group connections) [13].

The literature contains a number of metrics for measuring network diversity/segregation, the most prominent being assortativity [17]. However, when dealing with dynamic networks where new nodes are being added, it is useful to know not only what the diversity of a specific network snapshot is after those nodes are added, but how good it could have been. In other words, if new nodes join a network, what is the best assortativity that one could possibly achieve, given pre-existing structure of the network and restrictions on where the new nodes can join?

Our work is motivated by the example of an organization that is evaluating their hiring and employee assignment practices with respect to the diversity (gender, race, etc.) of the organizational network. When positions are open, some set of candidates apply for those positions. Each candidate has some amount (possibly zero) of suitability for each of the open positions. If one’s goal is to minimize segregation in the network while ensuring that each position is filled by a candidate who is suitable for that position, which candidate should one hire for each position? If there are significant gender disparities in applications across job categories (e.g., if software engineer candidates are disproportionately male), then it may not be possible to achieve perfect diversity in hiring and assignment; but nonetheless, it is useful to know how well one can do. One can imagine similar examples for, say, new graduate students joining an existing scientific collaboration network.

There has been a great deal of recent interest in fairness of hiring/assignment procedures (e.g., the Rooney Rule used by the American National Football League [5]). This is because one cannot simply eliminate an existing professional network and replace it with a diverse network; and moreover, at the hiring stage, the candidate pool may itself be non-diverse or exhibit correlations between protected attributes and skillsets.

计算机代写|复杂网络代写complex network代考|Related Work

The recent scientific literature contains many studies on modeling bias in human recruiting systems. For example, [20] examines strategies for hiring diverse faculty in universities, [2] shows the different likelihoods of hiring and promotion for candidates from different groups with equal skills, and [16] addresses the tradeoff between performance goals and company diversity. [15] shows a positive relationship between board member racial and gender diversity to performance of nonprofits. Unfortunately, automating recruiting systems will not necessarily solve problems of discrimination in hiring [8]. One solution is to make sure that protected attributes do not influence algorithmic decisions, but [7] shows that gender bias exists even after scrubbing gender indicators from a classifier. While traditional approaches measure diversity of organizations in terms of numbers [18], organizations are social networks, and network factors influence entrepreneurial success, mobility through occupational ladders, and access to employments [19].

Problem Formulation
We formulate this problem as a multi-objective problem in which the goal is to assign a set of newly-hired employees/employment candidates (without loss of generality, the ‘candidates’) to open positions so as to maximize (1) the fitness of employees to positions and (2) the diversity of the organizational network, under the constraint that all open positions must be filled. We compute diversity as assortativity, which measures the extent to which ‘like connect to like’. Figure 1 shows an overview of the problem.
The input for this problem consists of the following:
(1) An undirected network $G=(P, E)$, representing the professional network of an organization. Nodes represent positions ( $s$ filled, $m$ open). Each edge $\left(p_i, p_j\right)$ represents either a real or expected professional interaction between the employees who currently fill or will fill positions $p_i$ and $p_j$ (i.e., those employees do interact, or are expected to interact once the positions are filled).

(2) A set of $t$ candidates $(t \geq m$ ). If $t=m$, this problem represents the case where new employees have already been hired and need to be assigned e.g., newly hired software engineers are being assigned to teams. If $t>m$, this problem can be viewed as a combination of the hiring and assignment problems.
(3) The fitness of each candidate $c_j$ for each position $o_i$ (how well-qualified $c_j$ is for $o_i$ ). We assume that it is possible to match candidates to open positions such that each open position is filled subject to having at least one candidate with greater than zero fitness for each open position.
(4) An attribute of interest, such as gender, that divides employees/candidates into $k$ classes of attributes: class $_s, \ldots$, class $_k$. We assume that this attribute is categorical and each node can be member of just one class (e.g., minority and majority).

The output is a matching of candidates to open positions. We refer to the input and output in the rest of the paper as described in Table 1.

复杂网络代写

计算机代写|复杂网络代写complex network代考|On Measuring the Diversity of Organizational Networks

重点词汇
2667/5000
翻译
通用场景
为了使商业和非营利组织取得成功，对这些组织来说，重要的是招聘一支不仅技术熟练而且多样化的员工队伍，因为多样性与绩效呈正相关[15]。然而，多样性不能仅用数字来衡量:众所周知，当一个网络的结构方式使资源无法通过少数群体成员可以获得的社会资本获得时，可能会产生负面影响[9]。社会资本包括来自个人群体外部的桥接资源(群体间连接)和来自群体内部连接的粘合资源(群体内连接)[13]。

文献中包含了许多衡量网络多样性/隔离的指标，其中最突出的是分类性[17]。然而，在处理正在添加新节点的动态网络时，不仅要知道添加这些节点后特定网络快照的多样性，还要知道它本来可以有多好，这是很有用的。换句话说，如果新节点加入网络，在给定网络的现有结构和新节点可以加入的位置的限制下，人们可能实现的最佳分类是什么?

我们的工作是由一个组织的例子来激励的，该组织正在评估他们的招聘和员工分配实践，考虑到组织网络的多样性(性别、种族等)。当职位空缺时，一些候选人会申请这些职位。每个候选人对每个空缺职位都有一定程度(可能为零)的适合性。如果一个人的目标是尽量减少网络中的隔离，同时确保每个职位都由适合该职位的候选人填补，那么每个职位应该雇佣哪个候选人呢?如果在不同工作类别的申请中存在显著的性别差异(例如，如果软件工程师候选人是不成比例的男性)，那么在招聘和分配方面可能无法实现完美的多样性;但无论如何，知道一个人能做得多好是有用的。人们可以想象类似的例子，比如说，新的研究生加入一个现有的科学合作网络。

最近，人们对招聘/分配程序的公平性产生了极大的兴趣(例如，美国国家橄榄球联盟使用的鲁尼规则[5])。这是因为人们不能简单地消除现有的专业网络，并用一个多样化的网络取而代之;此外，在招聘阶段，候选人才库本身可能没有多样性，或者在受保护的属性和技能集之间表现出相关性。

计算机代写|复杂网络代写complex network代考|Related Work

最近的科学文献包含了许多关于人类招聘系统建模偏差的研究。例如，[20]研究了大学招聘多元化教师的策略，[2]显示了来自不同群体的具有相同技能的候选人的招聘和晋升的不同可能性，[16]解决了绩效目标和公司多样性之间的权衡。[15]显示了董事会成员种族和性别多样性对非营利组织绩效的正向关系。不幸的是，自动化招聘系统不一定能解决招聘中的歧视问题[8]。一种解决方案是确保受保护的属性不影响算法决策，但[7]表明，即使在从分类器中剔除性别指标后，性别偏见仍然存在。传统方法从数量上衡量组织的多样性[18]，但组织是社会网络，网络因素影响创业成功、通过职业阶梯的流动性和就业机会[19]。

问题表述
我们将这个问题表述为一个多目标问题，其目标是在所有空缺职位必须被填补的约束下，分配一组新雇佣的员工/就业候选人(不失去一般性，“候选人”)来开放职位，以最大限度地实现(1)员工对职位的适应性和(2)组织网络的多样性。我们将多样性作为分类性来计算，它衡量的是“相似连接到相似”的程度。图1显示了该问题的概述。
这个问题的输入包括以下内容:
(1)无向网络$G=(P, E)$，代表一个组织的专业网络。节点表示位置($s$填充，$m$打开)。每个边$\left(p_i, p_j\right)$代表当前填补或将填补职位$p_i$和$p_j$的员工之间的真实或预期的专业互动(即，这些员工进行互动，或预计一旦职位被填补)。

(2)一组$t$考生($(t \geq m$)。如果$t=m$，这个问题表示新员工已经被雇用，需要被分配的情况，例如，新雇用的软件工程师被分配到团队。如果$t>m$，这个问题可以看作是招聘和分配问题的结合。
(3)每个候选人$c_j$对每个职位的适合度$o_i$ ($c_j$对$o_i$有多适合)。我们假设有可能将候选人与空缺职位相匹配，这样每个空缺职位都有至少一个候选人对每个空缺职位具有大于零的适应度。
(4)一个感兴趣的属性，如性别，将员工/候选人划分为$k$属性类:类$_s, \ldots$，类$_k$。我们假设这个属性是分类的，每个节点只能是一个类的成员(例如，少数派和多数派)。

输出是空缺职位的候选人匹配。本文其余部分的输入和输出如表1所示。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|神经网络代写neural networks代考|Looking at the Weights

Posted on 2023年5月16日2023年5月16日 by statistics-lab_01, statistics-lab_01

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神经网络，也被称为人工神经网络（ANN）或模拟神经网络（SNN），是机器学习的一个子集，是深度学习算法的核心。它们的名称和结构受到人脑的启发，模仿了生物神经元相互之间的信号方式。

statistics-lab™ 为您的留学生涯保驾护航在代写神经网络neural networks方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写神经网络neural networks代写方面经验极为丰富，各种代写神经网络neural networks相关的作业也就用不着说。

计算机代写|神经网络代写neural networks代考|Looking at the Weights

In previous chapters, we looked at the weights of a neural network as an array of numbers. You can’t typically glance at a weight array and see any sort of meaningful pattern. However, if the weights are represented graphically, patterns begin to emerge.

One common way to view the weights of a neural network is using a special type of chart called a histogram. You’ve probably seen histograms many times before – a histogram is a chart made up of vertical bars that count the number of occurrences in a population. Figure 6.1 is a histogram showing the popularity of operating systems. The $y$-axis shows the number of occurrences of each of the groups in the x-axis.

We can use a histogram to look at the weights of a neural network. You can typically tell a trained from an untrained neural network by looking at this histogram. Figure 6.2 shows a trained neural network.

A neural network histogram uses the same concept as the operating system histogram shown earlier. The y-axis specifies how many weights fell into the ranges specified by the numbers on the x-axis. This allows you to see the distribution of the weights.

Most trained neural networks will look something like the above chart. Their weights will be very tightly clustered around zero. A trained neural network will typically look like a very narrow Gaussian curve.

计算机代写|神经网络代写neural networks代考|Range Randomization

In the last section, we saw what a trained neural network looks like in a weight histogram. Untrained neural networks can have a variety of appearances. The appearance of the weight histogram will be determined by the weight initialization method used.

Range randomization produces a very simple looking chart. The more weights there are, the flatter the top will be. This is because the random number generator should give you an even distribution of numbers. If you are randomizing to the range of -1 to 1 , you would expect to have approximately the same number of weights above zero as below.

Using Nguyen-Widrow
We will now look at the Nguyen-Widrow weight initialization method. The Nguyen-Widrow method starts out just like the range randomized method. Random values are chosen between -0.5 and +0.5 . However, a special algorithm is employed to modify the weights. The histogram of a NguyenWidrow weight initialization looks like Figure 6.4.

As you can see, the Nguyen-Widrow initialization has a very distinctive pattern. There is a large distribution of weights between -0.5 and 0.5 . It gradually rises and then rapidly falls off to around -3.0 and +3.0 .
Performance of Nguyen-Widrow
You may be wondering how much advantage there is to using NguyenWidrow. Take a look at the average number of training iterations needed to train a neural network initialized by range randomization and Nguyen-Widrow.
Average iterations needed (lower is better)
Range random: 502.86
Nguyen-Widrow: 454,88
As you can see from the above information, the Nguyen-Widrow outperforms the range randomizer.

神经网络代写

计算机代写|神经网络代写neural networks代考|C alculating th e N o d e D eltas

第一步是为神经网络中的每个节点或神经元计算一个恒定值。我们将从输出节点开始，通过神经网络往回走——这就是反向传播这个术语的由来。我们首先计算输出神经元的误差，并通过神经网络向后传播这些误差。
我们将为每个节点计算的值称为节点增量。术语“层增量”有时也用来描述这个值。层增量描述了这些增量一次计算一层的事实。计算节点增量的方法取决于计算的是输出节点还是内部节点。输出神经元显然是所有的输出节点。隐藏神经元和输入神经元是内部节点。节点delta的计算公式如式4.1所示。
式4.1:计算节点delta
$ $
\delta_i= \begin{cases}-E f_i^{\prime}， &， \text{输出节点}\ f_i^{\prime} \sum_k \omega_{h i} \delta_k &， \text{中间节点}\end{cases}
$ $
我们将计算所有隐藏和无偏差神经元的节点delta。不需要计算输入和偏置神经元的节点delta。尽管使用上述方程可以很容易地计算输入和偏倚神经元的节点delta，但梯度计算不需要这些值。你很快就会看到，权重的梯度计算只关注与该权重相连的神经元。偏差和输入神经元只是连接的起点。它们永远不是终点。

我们将从使用输出神经元的公式开始。您将注意到公式使用了一个值$\mathbf{E}$。这是输出神经元的误差。您可以从公式4.2中看到如何计算$\mathbf{E}$。
式4.2:误差函数
$ $
E =(ⅰ)
$ $
你可能还记得第2章中类似的方程2.1。这是误差函数。这里，我们用实际减去理想。对于图4.2所提供的神经网络，可以这样写:
$ $
E = 0.75 – -1.00 = -0.25
$ $
现在我们有了$\mathbf{E}$，我们可以计算第一个(也是唯一一个)输出节点的节点增量。代入式4.1，可得:
$ $
-(-0.25) * d A(1.1254)=0.185 * 0.25=0.05
$ $

计算机代写|神经网络代写neural networks代考|C alculating the Individual Gradients

现在我们可以计算单个梯度了。与节点delta不同，只使用一个方程来计算实际的梯度。梯度由式4.5计算。
方程4.5:个体梯度
$ $
\压裂{\部分E}{\部分w_ {(k)}} = \ delta_k \ cdot o_j
$ $
上面的方程计算误差(E)相对于每个单独的权重的偏导数。偏导数是梯度。第三章讨论偏导数。为了确定单个梯度，将目标神经元的节点增量乘以源神经元的权重。在上式中，$\mathbf{k}$表示目标神经元，$\mathbf{i}$表示源神经元。

要计算从$\mathbf{H 1}$到$\mathbf{O 1}$的权重梯度，将使用以下值:
$ $
开始{对齐}
& \text {output}(\ mathm {h} 1) * \text {nodeDe1ta (o1)} \
& (0.37 * 0.05)=0.01677
结束{对齐}
$ $

重要的是要注意，在上面的等式中，我们乘以隐藏1的输出，而不是总和。当直接处理导数时，应该给出和。否则，您将间接地应用激活函数两次。在上面的方程中，我们没有直接处理导数，所以我们使用常规的节点输出。节点输出已经应用了激活函数。

一旦计算出梯度，权重的单个位置就不再重要了。我们可以简单地把权重和梯度看作是单维数组。我们将看到的个别训练方法将平等地对待所有权重和梯度。权重是来自输入神经元还是输出神经元并不重要。重要的是，正确的权重与正确的梯度一起使用。这些权重和梯度数组的顺序是任意的。然而，Encog对上述神经网络使用以下顺序:
权重/梯度0:隐藏$1 \右箭头$输出1
权重/梯度1:隐藏$2 \右箭头$输出1
权重/梯度2:偏置$2 \右移$输出1
权重/梯度3:输入$1 \右箭头$隐藏1
权重/梯度4:输入$2 \右箭头$隐藏1
权重/梯度5:偏差$1->$隐藏1
权重/梯度6:输入$1->$ Hidden 2
权重/梯度7:输入$2 \右箭头$隐藏2
权重/梯度$8:$ Bias $1 \右箭头$隐藏2
权重/梯度D:隐藏1 ->输出1
权重/梯度1:隐藏2 ->输出1
权重/梯度2:偏置$2 \右移$输出1
权重/梯度3:输入$1 \右箭头

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