BMS13 - 统计代写答疑辅导

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数学代写|matlab代写|BMS13

Posted on 2022年12月27日2022年12月27日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

statistics-lab™ 为您的留学生涯保驾护航在代写matlab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matlab代写方面经验极为丰富，各种代写matlab相关的作业也就用不着说。

我们提供的matlab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

数学代写|matlab代写|Applications of Deep Learning

Deep learning is used in many applications today. Here are a few:
Image recognition – This is arguably the best known and most controversial use of deep learning. A deep learning system is trained with pictures of people. Cameras are distributed everywhere, and images are captured. The system then identifies individual faces and matches them against its trained database. Even with variations in lighting, weather conditions, and clothing, the system can identify the people in the images.

Speech recognition – You hardly ever get a human being on the phone anymore. You are first presented with a robotic listener that can identify what you are saying, at least within the limited context of what it expects. When a human listens to another human, the listener is not just recording the speech, they are guessing what the person is going to say and filling in gaps of garbled words and confusing grammar. Robotic listeners have some of the same abilities. A robotic listener is an embodiment of the “Turing test.” Did you ever get one that you thought was a human being? Or for that matter, did you ever reach a human who you thought was a robot?

Handwriting analysis – A long time ago, you would get forms in which you had boxes in which to write numbers and letters. At first, they had to be block capitals! A robotic handwriting system could figure out the letters in those boxes reliably. Years later, though many years ago, the US Post Office introduced zip code reading systems. At first, you had to put the zip code on a specific part of the envelope. That system has evolved so that it can find zip codes anywhere. This made the zip $+4$ system valuable and a big productivity boost.

Machine translation – Google translate does a pretty good job considering it can translate almost any language in the world. It is an example of a system with online training. You see that when you type in a phrase and the translation has a checkmark next to it because a human being has indicated that it is correct. Figure $1.10$ gives an example. Google harnesses the services of free human translators to improve its product!

Targeting – By targeting, we mean figuring out what you want. This may be a movie, a clothing item, or a book. Deep learning systems collect information on what you like and decide what you would be most interested in buying. Figure $1.11$ gives an example. This is from a couple of years ago. Perhaps, ballet dancers like Star Wars!

Other applications include game playing, autonomous driving, medicine, and many others. Just about any human activity can be an application of deep learning.

数学代写|matlab代写|Organization of the Book

This book is organized around specific deep learning examples. You can jump into any chapter as they are pretty much independent. We’ve tried to present a wide range of topics, some of which, hopefully, align with your work or interests. The next chapter gives an overview of MATLAB products for deep learning. Besides the core MATLAB development environment, we only use three of their toolboxes in this book.
Each chapter except for this and the next is organized in the following order:

Modeling
Building the system
Training the system
Testing the system
Training and testing are often in the same script. Modeling varies with each chapter. For physical problems, we derive numerical models, usually sets of differential equations, and build simulations of the processes.

The chapters in this book present a range of relatively simple examples to help you learn more about deep learning and its applications. It will also help you learn the limitations of deep learning and areas for future research. All use the MATLAB Deep Learning Toolbox.

What Is Deep Learning? (this chapter).
MATLAB Machine Learning Toolboxes – This chapter gives you an introduction to MATLAB machine intelligence toolboxes. We’ll be using three of the toolboxes in this book.
Finding Circles with Deep Learning – This is an elementary example. The system will try to figure out if a figure is a circle. It will be presented with circles, ellipses, and other objects and trained to determine which are circles.
Classifying Movies – All movie databases try to guess what movies will be of most interest to their viewers to speed movie selection and reduce the number of disgruntled customers. This example creates a movie rating system and attempts to classify movies in the movie database as good or bad.
Algorithmic Deep Learning – This is an example of fault detection using a detection filter as an element of the deep learning system. It uses a custom deep learning algorithm, the only example that does not use the MATLAB Deep Learning Toolbox.
Tokamak Disruption Detection – Disruptions are a major problem with a nuclear fusion device known as a Tokamak. Researchers are using neural nets to detect disruptions before they happen so that they can be stopped. In this example, we use a simplified dynamical model to demonstrate deep learning.

matlab代写

数学代写|matlab代写|Applications of Deep Learning

深度学习在当今的许多应用中都有使用。以下是一些：
图像识别——这可以说是深度学习最著名和最具争议的用途。深度学习系统是用人物照片训练的。摄像头分布在各处，拍摄图像。然后系统识别个人面孔并将它们与训练有素的数据库进行匹配。即使光线、天气条件和衣服发生变化，系统也可以识别图像中的人物。

语音识别——你几乎再也不会有人接电话了。你首先会看到一个机器人听众，它可以识别你在说什么，至少在它所期望的有限上下文中是这样。当一个人听另一个人说话时，听众不仅仅是在记录语音，他们还在猜测这个人要说什么，并填补乱码和混乱语法的空白。机器人听众具有一些相同的能力。机器人听众是“图灵测试”的一个体现。你有没有得到一个你认为是人类的人？或者就此而言，你有没有接触过你认为是机器人的人？

手写分析——很久以前，你会得到一些表格，其中有可以用来写数字和字母的方框。起初，他们必须是大写字母！机器人手写系统可以可靠地识别出这些盒子里的字母。多年以后，虽然是很多年前，美国邮局推出了邮政编码阅读系统。起初，您必须将邮政编码放在信封的特定部分。该系统已经发展到可以在任何地方找到邮政编码。这使拉链+4系统的价值和巨大的生产力提升。

机器翻译——谷歌翻译做得很好，因为它几乎可以翻译世界上任何语言。这是一个在线培训系统的例子。当你输入一个短语时，你会看到翻译旁边有一个复选标记，因为有人已经指出它是正确的。数字1.10举个例子。Google 利用免费的人工翻译服务来改进其产品！

定位——通过定位，我们的意思是弄清楚你想要什么。这可能是一部电影、一件衣服或一本书。深度学习系统收集关于你喜欢什么的信息，并决定你最有兴趣购买什么。数字1.11举个例子。这是几年前的事了。也许，像星球大战这样的芭蕾舞演员！

其他应用包括玩游戏、自动驾驶、医学等等。几乎任何人类活动都可以是深度学习的应用。

数学代写|matlab代写|Organization of the Book

本书围绕具体的深度学习示例进行组织。您可以跳到任何章节，因为它们几乎是独立的。我们尝试介绍范围广泛的主题，希望其中一些主题符合您的工作或兴趣。下一章概述了用于深度学习的 MATLAB 产品。除了核心的 MATLAB 开发环境，我们在本书中只使用了他们的三个工具箱。
除本章和下一章外，每一章都按以下顺序组织：

造型
构建系统
训练系统
测试系统
训练和测试通常在同一个脚本中。建模因每一章而异。对于物理问题，我们推导出数值模型，通常是微分方程组，并建立过程模拟。

本书的章节提供了一系列相对简单的示例，以帮助您更多地了解深度学习及其应用。它还将帮助您了解深度学习的局限性和未来研究的领域。全部使用 MATLAB 深度学习工具箱。

什么是深度学习？（本章）。
MATLAB 机器学习工具箱——本章介绍 MATLAB 机器智能工具箱。我们将使用本书中的三个工具箱。
Finding Circles with Deep Learning——这是一个基本的例子。系统将尝试判断图形是否为圆形。它将与圆圈、椭圆和其他对象一起呈现，并接受训练以确定哪些是圆圈。
对电影进行分类——所有电影数据库都试图猜测观众最感兴趣的电影是什么，以加快电影选择速度并减少不满客户的数量。此示例创建一个电影评级系统，并尝试将电影数据库中的电影分类为好或坏。
算法深度学习——这是一个使用检测过滤器作为深度学习系统元素的故障检测示例。它使用自定义深度学习算法，这是唯一不使用 MATLAB 深度学习工具箱的示例。
托卡马克中断检测——中断是称为托卡马克的核聚变装置的主要问题。研究人员正在使用神经网络在中断发生之前检测它们，以便可以阻止它们。在此示例中，我们使用简化的动力学模型来演示深度学习。

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

数学代写|matlab代写|CSC113

Posted on 2022年12月27日2022年12月27日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|Neural Nets

Neural networks, or neural nets, are a popular way of implementing machine “intelligence.” The idea is that they behave like the neurons in a brain. In this section, we will explore how neural nets work, starting with the most fundamental idea with a single neuron and working our way up to a multi-layer neural net. Our example for this will be a pendulum. We will show how a neural net can be used to solve the prediction problem. This is one of the two uses of a neural net, prediction and classification. We’ll start with a simple classification example.

Let’s first look at a single neuron with two inputs. This is shown in Figure 1.2. This neuron has inputs $x_1$ and $x_2$, a bias $b$, weights $w_1$ and $w_2$, and a single output $z$. The activation function $\sigma$ takes the weighted input and produces the output. In this diagram, we explicitly add icons for the multiplication and addition steps within the neuron, but in typical neural net diagrams such as Figure 1.1, they are omitted.
$$
z=\sigma(y)=\sigma\left(w_1 x_1+w_2 x_2+b\right)
$$
Let’s compare this with a real neuron as shown in Figure 1.3. A real neuron has multiple inputs via the dendrités. Some of thẻse branchẻs mean thăt multiplé inputś cản connect to the cell body through the same dendrite. The output is via the axon. Each neuron has one output. The axon connects to a dendrite through the synapse.
There are numerous commonly used activation functions. We show three:
$$
\begin{aligned}
\sigma(y) & =\tanh (y) \
\sigma(y) & =\frac{2}{1-e^{-y}}-1 \
\sigma(y) & =y
\end{aligned}
$$
The exponential one is normalized and offset from zero so it ranges from $-1$ to 1 . The last one, which simply passes through the value of $\mathrm{y}$, is called the linear activation function. The following code in the script OneNeuron . m computes and plots these three activation functions for an input q. Figure $1.4$ shows the three activation functions on one plot.

数学代写|matlab代写|Types of Deep Learning

There are many types of deep learning networks. New types are under development as you read this book. One deep learning researcher joked that you will have the name for an existing deep learning algorithm if you randomly put together four letters.
The following sections briefly describe some of the major types.

A CNN has convolutional layers. It convolves a feature with the input matrix so that the output emphasizes that feature. This effectively finds patterns. For example, you might convolve an $\mathrm{L}$ pattern with the incoming data to find corners. The human eye has edge detectors, making the human vision system a convolutional neural network of sorts.

Recurrent neural networks are a type of recursive neural network. Recurrent neural networks are often used for time-dependent problems. They combine the last time step’s data with the data from the hidden or intermediate layer, to represent the current time step. A recurrent neural net has a loop. An input vector at time $k$ is used to create an output which is then passed to the next element of the network. This is done recursively in that each stage is identical to external inputs and inputs from the previous stage. Recurrent neural nets are used in speech recognition, language translation, and many other applications. One can see how a recurrent network would be useful in translation. The meaning of the latter part of an English sentence can be dependent on the beginning. Now, this presents a problem. Suppose we are translating a paragraph. Is the output of the first stage necessarily relevant to the 100 th stage? In standard estimation, old data is forgotten using a forgetting factor. In neural networks, we can use Long Short-Term Memory (LSTM) networks that have this feature.

matlab代写

数学代写|matlab代写|Neural Nets

神经网络或神经网络是实现机哭”智能”的一种流行方式。这个想法是它们的行为就像大脑中的神经元。在本节中，我们将探索神经网络的工作原理，从最基本的单个神经元概念开始，逐步发展到多层神经网络。我们的例子是一个钟摆。我们将展示如何使用神经网络来解决预测问题。这是神经网络的两种用途之一，即预测和分类。我们将从一个简单的分类示例开始。
让我们首先看一下具有两个输入的单个神经元。如图 $1.2$ 所示。这个神经元有输入 $x_1$ 和 $x_2$ ，偏差 $b$, 权重 $w_1$ 和 $w_2$ ，和一个单一的输出 $z$. 激活函数 $\sigma$ 采用加权输入并产生输出。在此图中，我们明确地为神经元内的乘法和加法步骤添加了图标，但在典型的神经网络图中（如图 1.1），它们被省略了。
$$
z=\sigma(y)=\sigma\left(w_1 x_1+w_2 x_2+b\right)
$$
让我们将其与图 $1.3$ 中所示的真实神经元进行比较。一个真正的神经元通过树突有多个输入。其中一些分支意味着多个输入可以通过相同的树突连接到细胞体。输出是通过轴突。每个神经元都有一个输出。轴突通过突触连接到树突。
有许多常用的激活函数。我们展示三个:
$$
\sigma(y)=\tanh (y) \sigma(y) \quad=\frac{2}{1-e^{-y}}-1 \sigma(y)=y
$$
指数一被归一化并从零偏移，所以它的范围是 $-1$ 到 1 。最后一个，它简单地传递了值 $\mathrm{y}$ ，称为线性激活函数。脚本 OneNeuron 中的以下代码。 $\mathrm{m}$ 为输入 $q$ 计算并绘制这三个激活函数。数字 $1.4$ 在一张图上显示了三个激活函数。

数学代写|matlab代写|Types of Deep Learning

深度学习网络有很多种。在您阅读本书时，新类型正在开发中。一位深度学习研究人员开玩笑说，如果你将四个字母随机组合在一起，你就会拥有现有深度学习算法的名称。
以下各节简要介绍了一些主要类型。

CNN 具有卷积层。它将特征与输入矩阵进行卷积，以便输出强调该特征。这有效地找到了模式。例如，您可能会卷积一个大号模式与传入的数据来寻找角落。人眼具有边缘检测器，使人类视觉系统成为一种卷积神经网络。

递归神经网络是递归神经网络的一种。递归神经网络通常用于时间相关问题。它们将最后一个时间步的数据与来自隐藏层或中间层的数据结合起来，以表示当前时间步。循环神经网络有一个循环。时间的输入向量k用于创建输出，然后将其传递到网络的下一个元素。这是递归完成的，因为每个阶段都与前一阶段的外部输入和输入相同。循环神经网络用于语音识别、语言翻译和许多其他应用。人们可以看到循环网络如何在翻译中发挥作用。英语句子后半部分的含义可能取决于开头。现在，这提出了一个问题。假设我们正在翻译一段话。第一阶段的输出是否必然与第100阶段相关？在标准估计中，使用遗忘因子遗忘旧数据。在神经网络中，我们可以使用具有此功能的长短期记忆 (LSTM) 网络。

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

数学代写|matlab代写|STA518

Posted on 2022年12月27日2022年12月27日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|Deep Learning

Deep learning is a subset of machine learning which is itself a subset of artificial intelligence and statistics. Artificial intelligence research began shortly after World War II [35]. Early work was based on the knowledge of the structure of the brain, propositional logic, and Turing’s theory of computation. Warren McCulloch and Walter Pitts created a mathematical formulation for neural networks based on threshold logic. This allowed neural network research to split into two approaches: one centered on biological processes in the brain and the other on the application of neural networks to artificial intelligence. It was demonstrated that any function could be implemented through a set of such neurons and that a neural net could learn to recognize patterns. In 1948, Norbert Wiener’s book Cybernetics was published which described concepts in control, communications, and statistical signal processing. The next major step in neural networks was Donald Hebb’s book in 1949, The Organization of Behavior, connecting connectivity with learning in the brain. His book became a source of learning and adaptive systems. Marvin Minsky and Dean Edmonds built the first neural computer at Harvard in 1950.

The first computer programs, and the vast majority now, have knowledge built into the code by the programmer. The programmer may make use of vast databases. For example, a model of an aircraft may use multidimensional tables of aerodynamic coefficients. The resulting software, therefore, knows a lot about aircraft, and running simulations of the models may present surprises to the programmer and the users since they may not fully understand the simulation, or may have entered erroneous inputs. Nonetheless, the programmatic relationships between data and algorithms are predetermined by the code.

In machine learning, the relationships between the data are formed by the learning system. Data is input along with the results related to the data. This is the system training. The machine learning system relates the data to the results and comes up with rules that become part of the system. When new data is introduced, it can come up with new results that were not part of the training set.

Deep learning refers to neural networks with more than one layer of neurons. The name “deep learning” implies something more profound, and in the popular literature, it is taken to imply that the learning system is a “deep thinker.” Figure $1.1$ shows a single-layer and multi-layer network. It turns out that multi-layer networks can learn things that single-layer networks cannot. The elements of a network are nodes, where weighted signals are combined and biases added. In a single layer, the inputs are multiplied by weights and then added together at the end, after passing through a threshold function. In a multi-layer or “deep learning” network, the inputs are combined in the second layer before being output. There are more weights and the added connections allow the network to learn and solve more complex problems.

数学代写|matlab代写|History of Deep Learning

Minsky wrote the book Perceptrons with Seymour Papert in 1969 , which was an early analysis of artificial neural networks. The book contributed to the movement toward symbolic processing in AI. The book noted that single-layer neurons could not implement some logical functions such as exclusive or (XOR) and implied that multi-layer networks would have the same issue. It was later found that three-layer networks could implement such functions. We give the XOR solution in this book.

Multi-layer neural networks were discovered in the 1960 s but not studied until the 1980 s. In the 1970 s, self-organizing maps using competitive learning were introduced [15]. A resurgence in neural networks happened in the 1980s. Knowledge-based, or “expert,” systems were also introduced in the 1980s. From Jackson [18]
An expert system is a computer program that represents and reasons with knowledge of some specialized subject to solve problems or give advice.
-Peter Jackson, Introduction to Expert Systems
Backpropagation for neural networks, a learning method using gradient descent, was reinvented in the 1980 s leading to renewed progress in this field. Studies began with both human neural networks (i.e., the human brain) and the creation of algorithms for effective computational neural networks. This eventually led to deep learning networks in machine learning applications.

Advances were made in the 1980 s as AI researchers began to apply rigorous mathematical and statistical analysis to develop algorithms. Hidden Markov Models were applied to speech. A Hidden Markov Model is a model with unobserved (i.e., hidden) states. Combined with massive databases, they have resulted in vastly more robust speech recognition. Machine translation has also improved. Data mining, the first form of machine learning as it is known today, was developed.

In the early 1990s, Vladimir Vapnik and coworkers invented a computationally powerful class of supervised learning networks known as support-vector machines (SVM). These networks could solve problems of pattern recognition, regression, and other machine learning problems.

matlab代写

数学代写|matlab代写|Deep Learning

深度学习是机器学习的一个子集，机器学习本身是人工智能和统计学的一个子集。第二次世界大战后不久就开始了人工智能研究 [35]。早期的工作是基于大脑结构、命题逻辑和图灵的计算理论的知识。Warren McCulloch 和 Walter Pitts 基于阈值逻辑为神经网络创建了一个数学公式。这使得神经网络研究分为两种方法：一种以大脑中的生物过程为中心，另一种以神经网络在人工智能中的应用为中心。事实证明，任何功能都可以通过一组这样的神经元来实现，并且神经网络可以学习识别模式。1948年，诺伯特·维纳 (Norbert Wiener) 的著作《控制论》(Cybernetics) 出版，描述了控制、通信和统计信号处理方面的概念。神经网络的下一个重要步骤是唐纳德·赫布 (Donald Hebb) 于 1949 年出版的《行为组织》(The Organization of Behavior) 一书，该书将连通性和大脑学习联系起来。他的书成为学习和自适应系统的源泉。Marvin Minsky 和 Dean Edmonds 于 1950 年在哈佛建造了第一台神经计算机。

第一批计算机程序，以及现在的绝大多数计算机程序，都由程序员将知识内置到代码中。程序员可以使用庞大的数据库。例如，飞机模型可能使用空气动力学系数的多维表。因此，由此产生的软件对飞机了解很多，并且对模型进行模拟可能会给程序员和用户带来惊喜，因为他们可能不完全理解模拟，或者可能输入了错误的输入。尽管如此，数据和算法之间的编程关系是由代码预先确定的。

在机器学习中，数据之间的关系是由学习系统形成的。数据连同与数据相关的结果一起输入。这就是系统培训。机器学习系统将数据与结果相关联，并提出成为系统一部分的规则。当引入新数据时，它可以得出不属于训练集的新结果。

深度学习是指具有不止一层神经元的神经网络。“深度学习”这个名字意味着更深刻的东西，在通俗文学中，它被用来暗示学习系统是一个“深度思考者”。数字1.1显示单层和多层网络。事实证明，多层网络可以学习单层网络无法学习的东西。网络的元素是节点，加权信号在其中组合并添加了偏差。在单层中，输入乘以权重，然后在通过阈值函数后在最后相加。在多层或“深度学习”网络中，输入在输出之前在第二层中组合。有更多的权重和增加的连接允许网络学习和解决更复杂的问题。

数学代写|matlab代写|History of Deep Learning

Minsky 于 1969 年与 Seymour Papert 合着了《感知器》一书，这是对人工神经网络的早期分析。这本书推动了 AI 中符号处理的发展。该书指出，单层神经元无法实现一些逻辑功能，例如异或（XOR），并暗示多层网络也会有同样的问题。后来发现三层网络可以实现这样的功能。我们在本书中给出了 XOR 的解决方案。

多层神经网络在 1960 年代被发现，但直到 80 年代才被研究。在 1970 年代，引入了使用竞争性学习的自组织映射 [15]。神经网络的复兴发生在 1980 年代。1980 年代还引入了基于知识的或“专家”系统。来自 Jackson [18]
专家系统是一种计算机程序，它代表和推理某些专业主题的知识以解决问题或提供建议。
-Peter Jackson，专家系统简介
神经网络的反向传播是一种使用梯度下降的学习方法，在 1980 年代被重新发明，导致该领域取得新的进展。研究始于人类神经网络（即人脑）和有效计算神经网络算法的创建。这最终导致了机器学习应用中的深度学习网络。

随着 AI 研究人员开始应用严格的数学和统计分析来开发算法，在 1980 年代取得了进展。隐马尔可夫模型应用于语音。隐马尔可夫模型是具有未观察到（即隐藏）状态的模型。与海量数据库相结合，它们产生了更加强大的语音识别。机器翻译也有所改进。数据挖掘是当今已知的第一种机器学习形式，它已经开发出来。

在 1990 年代初期，Vladimir Vapnik 及其同事发明了一种计算能力强大的监督学习网络，称为支持向量机 (SVM)。这些网络可以解决模式识别、回归和其他机器学习问题。

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

数学代写|matlab代写|May2022

Posted on 2022年12月3日2022年12月3日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|Experiment Manager

The Experiment Manager app enables you to create deep learning experiments to train networks under multiple initial conditions and compare the results. For example, you can use deep learning experiments to:

Sweep through a range of hyperparameter values or use Bayesian optimization to find optimal training options. Bayesian optimization requires Statistics and Machine Learning Toolbox ${ }^{\text {Th }}$.
Use the built-in function trainNetwork or define your own custom training function.
Compare the results of using different data sets or test different deep network architectures.
To set up your experiment quickly, you can start with a preconfigured template. The experiment templates support workflows that include image classification, image regression, sequence classification, semantic segmentation, and custom training loops.
Experiment Manager provides visualization tools such as training plots and confusion matrices, filters to refine your experiment results, and annotations to record your observations. To improve reproducibility, every time that you run an experiment, Experiment Manager stores a copy of the experiment definition. You can access past experiment definitions to keep track of the hyperparameter combinations that produce each of your results.
Experiment Manager organizes your experiments and results in a project.
You can store several experiments in the same project.
Each experiment contains a set of results for each time that you run the experiment.
Each set of results consists of one or more trials corresponding to a different combination of hyperparameters.
By default, Experiment Manager runs one trial at a time. If you have Parallel Computing Toolbox ${ }^{\mathrm{T}}$, you can configure your experiment to run multiple trials at the same time or to run a single trial at a time on multiple GPUs, on a cluster, or in the cloud. If you have MATLAB Parallel Server ${ }^{\mathrm{rm}}$, you can also offload experiments as batch jobs in a remote cluster so that you can continue working or close your MATLAB session during training. For more information, see “Use Experiment Manager to Train Networks in Parallel” and “Offload Experiments as Batch Jobs to Cluster”.

数学代写|matlab代写|Open the Experiment Manager App

Under Setup Function, click Edit. The setup function opens in MATLAB Editor. The setup function specifies the training data, network architecture, and training options for the experiment. In this experiment, the setup function has three sections.

Load Training Data defines image datastores containing the training and validation data for the experiment. The experiment uses the Digits data set, which consists of 10,000 28-by-28 pixel grayscale images of digits from 0 to 9 , categorized by the digit they represent. For more information on this data set, see “Image Data Sets”.
Define Network Architecture defines the architecture for a simple convolutional neural network for deep learning classification.
Specify Training Options defines a training0ptions object for the experiment. In this experiment, the setup function loads the values for the initial learning rate from the myInitialLearnRate entry in the hyperparameter table.
When you run the experiment, Experiment Manager trains the network defined by the setup function six times. Each trial uses one of the learning rates specifed in the hyperparameter table. By default, Experiment Manager runs one trial at a time. If you have Parallel Computing Toolbox, you can run multiple trials at the same time or offload your experiment as a batch job in a cluster.
To run one trial of the experiment at a time, on the Experiment Manager toolstrip, under Mode, select Sequential and click Run.
To run multiple trials at the same time, under Mode, select Simultaneous and click Run. If there is no current parallel pool, Experiment Manager starts one using the default cluster profile. Experiment Manager then executes multiple simultaneous trials, depending on the number of parallel workers available. For best results, before you run your experiment, start a parallel pool with as many workers as GPUs. For more information, see “Use Experiment Manager to Train Networks in Parallel” and “GPU Support by Release” (Parallel Computing Toolbox).
To offload the experiment as a batch job, under Mode, select Batch Sequential or Batch Simultaneous, specify your Cluster and Pool Size, and click Run. For more information, see “Offload Experiments as Batch Jobs to Cluster”.
A table of results displays the accuracy and loss for each trial.

matlab代写

数学代写|matlab代写|Experiment Manager

Experiment Manager 应用程序使您能够创建深度学习实验，以在多个初始条件下训练网络并比较结果。例如，您可以使用深度学习实验来：

扫描一系列超参数值或使用贝叶斯优化来找到最佳训练选项。贝叶斯优化需要 Statistics and Machine Learning Toolbox日 .
使用内置函数 trainNetwork 或定义您自己的自定义训练函数。
比较使用不同数据集或测试不同深度网络架构的结果。
要快速设置实验，您可以从预配置的模板开始。实验模板支持包括图像分类、图像回归、序列分类、语义分割和自定义训练循环在内的工作流程。
Experiment Manager 提供可视化工具，例如训练图和混淆矩阵、用于优化实验结果的过滤器以及用于记录观察结果的注释。为提高再现性，每次运行试验时，试验管理器都会存储试验定义的副本。您可以访问过去的实验定义以跟踪产生每个结果的超参数组合。
实验管理器将您的实验和结果组织到一个项目中。
您可以在同一个项目中存储多个实验。
每个实验都包含您每次运行实验的一组结果。
每组结果都包含一个或多个对应于不同超参数组合的试验。
默认情况下，Experiment Manager 一次运行一个试验。如果您有 Parallel Computing Toolbox吨，您可以将实验配置为同时运行多个试验或在多个 GPU、集群或云中一次运行单个试验。如果您有 MATLAB 并行服务器r米，您还可以将实验作为批处理作业卸载到远程集群中，以便您可以在训练期间继续工作或关闭 MATLAB 会话。有关详细信息，请参阅“使用实验管理器并行训练网络”和“将实验作为批处理作业卸载到集群”。

数学代写|matlab代写|Open the Experiment Manager App

在设置功能下，单击编辑。设置函数在 MATLAB 编辑器中打开。setup 函数指定了实验的训练数据、网络架构和训练选项。在本实验中，setup 函数分为三个部分。

加载训练数据定义包含实验训练和验证数据的图像数据存储。该实验使用数字数据集，该数据集由 0 到 9 的数字的 10,000 个 28×28 像素灰度图像组成，按它们代表的数字分类。有关此数据集的更多信息，请参阅“图像数据集”。
定义网络架构定义了用于深度学习分类的简单卷积神经网络的架构。
Specify Training Options 为实验定义了一个 training0ptions 对象。在此实验中，设置函数从超参数表中的 myInitialLearnRate 条目加载初始学习率的值。
当您运行实验时，实验管理器会对设置函数定义的网络进行六次训练。每个试验都使用超参数表中指定的学习率之一。默认情况下，Experiment Manager 一次运行一个试验。如果您有 Parallel Computing Toolbox，则可以同时运行多个试验或将试验卸载为集群中的批处理作业。
要一次运行实验的一个试验，请在实验管理器工具条上的模式下，选择顺序并单击运行。
要同时运行多个试验，请在“模式”下选择“同时”并单击“运行”。如果当前没有并行池，Experiment Manager 会使用默认集群配置文件启动一个。然后，Experiment Manager 会根据可用的并行工作人员数量同时执行多个试验。为获得最佳结果，在运行实验之前，启动一个包含与 GPU 一样多的工作线程的并行池。有关详细信息，请参阅“使用 Experiment Manager 并行训练网络”和“按版本提供的 GPU 支持”（并行计算工具箱）。
要将实验作为批处理作业卸载，请在模式下选择连续批处理或同时批处理，指定您的集群和池大小，然后单击运行。有关详细信息，请参阅“将实验作为批处理作业卸载到集群”。
结果表显示每次试验的准确性和损失。

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

数学代写|matlab代写|ENR-261

Posted on 2022年12月3日2022年12月3日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|Export Network and Generate Code

Export the network architecture created in Deep Network Designer to the workspace or Simulink and generate code to recreate the network and training.

To export the network architecture with the initial weights to the workspace, on the Designer tab, click Export. Depending on the network architecture, Deep Network Designer exports the network as a LayerGraph lgraph or as a Layer object layers.
To export the network trained in Deep Network Designer to the workspace, on the Training tab, click Export. Deep Network Designer exports the trained network architecture as a DAGNetwork object trainedNetwork. Deep Network Designer also exports the results from training, such as training and validation accuracy, as the structure array trainInfoStruct.
To export the network trained in Deep Network Designer to Simulink, on the Training tab, click Export > Export to Simulink. Deep Network Designer saves the trained network as a MAT-file and generates Simulink blocks representing the trained network. The blocks generated depend on the type of network trained.
Image Classifier – Classify data using a trained deep learning neural network.
Predict – Predict responses using a trained deep learning neural network.
Stateful Classify – Classify data using a trained recurrent neural network.
Stateful Predict – Predict responses using a trained recurrent neural network.
To recreate a network that you construct and train in Deep Network Designer, generate MATLAB code.
To recreate the network layers, on the Designer tab, select Export > Generate Code.
To recreate the network layers, including any learnable parameters, on the Designer tab, select Export > Generate Code with Initial Parameters.
To recreate the network, data import, and training, on the Training tab, select Export > Generate Code for Training.
After generating a script, you can perform the following tasks.
To recreate the network layers created in the app, run the script. If you generated the training script, running the script will also replicate the network training.
Examine the code to learn how to create and connect layers programmatically, and how to train a deep network.
To modify the layers, edit the code. You can also run the script and import the network back into the app for editing.

数学代写|matlab代写|Deep Network Quantizer

Use the Deep Network Quantizer app to reduce the memory requirement of a deep neural network by quantizing weights, biases, and activations of convolution layers to 8-bit scaled integer data types. Using this app you can:

Visualize the dynamic ranges of convolution layers in a deep neural network.
Select individual network layers to quantize.
Assess the performance of a quantized network.
Generate GPU code to deploy the quantized network using GPU Coder ${ }^{\mathrm{T}}$.
Generate HDL code to deploy the quantized network to an FPGA using Deep Learning HDL Toolbox ${ }^{\mathrm{Th}}$.
Generate C++ code to deploy the quantized network to an ARM Cortex-A microcontroller using MATLAB Coder ${ }^{T r}$.

The Deep Learning Toolbox Model Quantization Library support package is a free add-on that you can download using the Add-On Explorer. Alternatively, see Deep Learning Toolbox Model Quantization Library. To learn about the products required to quantize and deploy the deep learning network to a GPU, FPGA, or CPU environment, see “Quantization Workflow Prerequisites”.

To explore the behavior of a neural network with quantized convolution layers, use the Deep Network Quantizer app. This example quantizes the learnable parameters of the convolution layers of the squeezenet neural network after retraining the network to classify new images according to the Train Deep Learning Network to Classify New Images example.
This example uses a DAG network with the GPU execution environment.
Load the network to quantize into the base workspace.

matlab代写

数学代写|matlab代写|Export Network and Generate Code

将在 Deep Network Designer 中创建的网络架构导出到工作区或 Simulink 并生成代码以重新创建网络和训练。

要将具有初始权重的网络架构导出到工作区，请在设计器选项卡上单击导出。根据网络架构，Deep Network Designer 将网络导出为 LayerGraph lgraph 或 Layer 对象层。
要将在 Deep Network Designer 中训练的网络导出到工作区，请在“训练”选项卡上单击“导出”。Deep Network Designer 将经过训练的网络架构导出为 DAGNetwork 对象 trainedNetwork。Deep Network Designer 还将训练结果（例如训练和验证准确度）导出为结构数组 trainInfoStruct。
要将在 Deep Network Designer 中训练的网络导出到 Simulink，请在 Training 选项卡上，单击 Export > Export to Simulink。Deep Network Designer 将经过训练的网络保存为 MAT 文件，并生成表示经过训练的网络的 Simulink 模块。生成的块取决于训练的网络类型。
图像分类器——使用经过训练的深度学习神经网络对数据进行分类。
预测——使用训练有素的深度学习神经网络预测响应。
有状态分类——使用经过训练的递归神经网络对数据进行分类。
状态预测——使用训练有素的递归神经网络预测响应。
要重新创建您在 Deep Network Designer 中构建和训练的网络，请生成 MATLAB 代码。
要重新创建网络层，请在 Designer 选项卡上选择 Export > Generate Code。
要重新创建网络层，包括任何可学习的参数，请在 Designer 选项卡上选择 Export > Generate Code with Initial Parameters。
要重新创建网络、数据导入和训练，请在“训练”选项卡上选择“导出”>“为训练生成代码”。
生成脚本后，您可以执行以下任务。
要重新创建在应用程序中创建的网络层，请运行脚本。如果您生成了训练脚本，运行该脚本也会复制网络训练。
检查代码以了解如何以编程方式创建和连接层，以及如何训练深度网络。
要修改图层，请编辑代码。您还可以运行脚本并将网络导入回应用程序进行编辑。

数学代写|matlab代写|Deep Network Quantizer

使用 Deep Network Quantizer 应用程序将卷积层的权重、偏差和激活量化为 8 位缩放整数数据类型，从而减少深度神经网络的内存需求。使用此应用程序，您可以：

可视化深度神经网络中卷积层的动态范围。
选择要量化的单个网络层。
评估量化网络的性能。
使用 GPU Coder 生成 GPU 代码以部署量化网络吨.
使用 Deep Learning HDL Toolbox 生成 HDL 代码以将量化网络部署到 FPGA吨H.
使用 MATLAB Coder 生成 C++ 代码以将量化网络部署到 ARM Cortex-A 微控制器吨r.

Deep Learning Toolbox 模型量化库支持包是一个免费的附加组件，您可以使用附加组件资源管理器下载它。或者，请参阅深度学习工具箱模型量化库。要了解量化深度学习网络并将其部署到 GPU、FPGA 或 CPU 环境所需的产品，请参阅“量化工作流程先决条件”。

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

数学代写|matlab代写|CSC113

Posted on 2022年12月3日2022年12月3日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|Edit Pretrained Network for Transfer Learning

Prepare a network for transfer learning by editing it in Deep Network Designer.
Transfer learning is the process of taking a pretrained deep learning network and fine-tuning it to learn a new task. You can quickly transfer learned features to a new task using a smaller number of training images. Transfer learning is therefore often faster and easier than training a network from scratch. To use a pretrained network for transfer learning, you must change the number of classes to match your new data set.
Open Deep Network Designer with SqueezeNet.
deepNetworkDesigner(squeezenet)
To prepare the network for transfer learning, replace the last learnable layer and the final classification layer. For SqueezeNet, the last learnable layer is a 2-D convolutional layer named ‘conv10’.

Drag a new convolution2dLayer onto the canvas. Set the FilterSize property to 1,1 and the NumFilters property to the new number of classes.
Change the learning rates so that learning is faster in the new layer than in the transferred layers by increasing the WeightLearnRateFactor and BiasLearnRateFactor values.
Delete the last convolution2dLayer and connect your new layer instead.
Check your network by clicking Analyze in the Designer tab. The network is ready for training if Deep Learning Network Analyzer reports zero errors. For an example showing how to train a network to classify new images, see “Transfer Learning with Deep Network Designer”.

数学代写|matlab代写|Add Custom Layer to Network

Add layers from the workspace to a network in Deep Network Designer.
In Deep Network Designer, you can build a network by dragging built-in layers from the Layer Library to the Designer pane and connecting them. You can also add custom layers from the workspace to a network in the Designer pane. Suppose that you have a custom layer stored in the variable myCustomLayer.
1 Click New in the Designer tab.
2 Pause on From Workspace and click Import.
3 Select myCustomLayer and click $\mathbf{O K}$.
4 Click Add.
The app adds the custom layer to the top of the Designer pane. To see the new layer, zoom-in using a mouse or click Zoom in.

Connect myCustomLayer to the network in the Designer pane. For an example showing how build a network with a custom layer in Deep Network Designer, see “Import Custom Layer into Deep Network Designer”.
You can also combine networks in Deep Network Designer. For example, you can create a semantic segmentation network by combining a pretrained network with a decoder subnetwork.

matlab代写

数学代写|matlab代写|Edit Pretrained Network for Transfer Learning

通过在 Deep Network Designer 中编辑网络来准备用于迁移学习的网络。
迁移学习是采用预训练的深度学习网络并对其进行微调以学习新任务的过程。您可以使用较少数量的训练图像快速将学习到的特征转移到新任务中。因此，迁移学习通常比从头开始训练网络更快、更容易。要使用预训练网络进行迁移学习，您必须更改类数以匹配您的新数据集。
使用 SqueezeNet 打开 Deep Network Designer。
deepNetworkDesigner(squeezenet)
为迁移学习准备网络，替换最后的可学习层和最终的分类层。对于 SqueezeNet，最后一个可学习层是一个名为“conv10”的二维卷积层。

将一个新的 convolution2dLayer 拖到画布上。将 FilterSize 属性设置为 1,1，将 NumFilters 属性设置为新的类数。
通过增加 WeightLearnRateFactor 和 BiasLearnRateFactor 值来更改学习率，使新层的学习速度比转移层的学习速度更快。
删除最后一个 convolution2dLayer 并改为连接新层。
通过单击“设计器”选项卡中的“分析”来检查您的网络。如果深度学习网络分析器报告零错误，则网络已准备好进行训练。有关如何训练网络对新图像进行分类的示例，请参阅“使用 Deep Network Designer 进行迁移学习”。

数学代写|matlab代写|Add Custom Layer to Network

将工作区中的层添加到 Deep Network Designer 中的网络。
在 Deep Network Designer 中，您可以通过将内置层从 Layer Library 拖动到 Designer 窗格并连接它们来构建网络。您还可以将自定义层从工作区添加到设计器窗格中的网络。假设您有一个自定义图层存储在变量 myCustomLayer 中。
1 单击设计器选项卡中的新建。
2 暂停在 From Workspace 上并单击 Import。
3 选择 myCustomLayer 并单击欧钾.
4 单击添加。
该应用程序将自定义层添加到设计器窗格的顶部。要查看新层，请使用鼠标放大或单击放大。

将 myCustomLayer 连接到设计器窗格中的网络。有关显示如何在 Deep Network Designer 中使用自定义层构建网络的示例，请参阅“将自定义层导入 Deep Network Designer”。
您还可以在 Deep Network Designer 中组合网络。例如，您可以通过将预训练网络与解码器子网络相结合来创建语义分割网络。

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

数学代写|matlab代写|CS1132

Posted on 2022年11月11日2022年11月11日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|Editor/Debugger

MATLAB has many useful functions for debugging, and debugging can also be done through its Editor, which is more properly called the Editor/Debugger.
Typing doc debug at the prompt in the Command Window will show some of the debugging functions. Also, in the Help Documentation, typing “debugging” in the Search Documentation will display basic information about the debugging processes.

It can be seen in the previous example that the action of the if clause was executed and it printed “In middle of range”, but just from that it cannot be determined why this happened. There are several ways to set breakpoints in a file (script or function) so that the variables or expressions can be examined. These can be done from the Editor/Debugger or commands can be typed from the Command Window. For example, the following dbstop command will set a breakpoint in the sixth line of this function (which is the action of the if clause), which allows the values of variables and/or expressions to be examined at that point in the execution. The function dbcont can be used to continue the execution, and dbquit can be used to quit the debug mode.

By typing the expressions $3<\mathrm{x}$ and then $3<\mathrm{x}<6$, we can determine that the expression $3<x$ will return either 0 or 1 . Both 0 and 1 are less than 6 , so the expression will always be true, regardless of the value of $x$ ! Once in the debug mode, instead of using dbcont to continue the execution, dbstep can be used to step through the rest of the code one line at a time. Also, in debug mode, hovering over a variable in the Editor will show the value of the variable.

Breakpoints can also be set and cleared through the Editor. When a file is open in the Editor, in between the line numbers on the left and the lines of code is a thin gray strip which is the breakpoint alley. In this, there are underscore marks next to the executable lines of code (as opposed to comments, for example). Clicking the mouse in the alley next to a line will create a breakpoint at that line (and then clicking on the red dot that indicates a breakpoint will clear it). This can be seen in Fig. 6.1.

数学代写|matlab代写|Tasks in Live Scripts

In Chapter 3, we saw how to create simple live scripts using the Live Editor, which can contain text, equations, images, code, and the code results. It is also possible to embed controls in live scripts, such as sliders, drop down menus, check boxes, edit fields, and push buttons. There are also tasks that can be used to auto-generate code. There are quite a few tasks for preprocessing data sets, and also a plotting task. Depending on which toolboxes you have, there may be tasks associated with those toolboxes, also.

As an example, we will create a live script that plots $\sin (x)$, using a slider for the maximum value of $x$. The following steps were taken, once in the Live Editor.

Click on Text and write a sentence description of the script.
Click on Section Break.
Write code for the minimum and maximum possible values of $\mathrm{x}$, and run that section.
Click on Section Break.
Start an assignment statement “maxval =” and then click on Control, and then Numeric Slider. This brings up a box that allows you to choose a label for the slider (the default is the variable name), and values for the minimum, maximum, and steps for the slider. There are defaults listed for each of those, and workspace variables can also be chosen. This is seen in Fig. 6.2. What was chosen here were the variables minofx and maxofx, and $0.1$ for the step.
Add assignments for $\mathrm{x}$ and $\mathrm{y}$ variables to that section and run it.
Click on Section Break.
Click on Task, and then Create Plot. This brings up a task that allows you to choose the type of plot, and variables for $\mathrm{x}$ and $\mathrm{y}$. This is seen in Fig. 6.3.

matlab代写

数学代写|matlab代写|Editor/Debugger

MATLAB有很多有用的调试功能，也可以通过它的Editor来进行调试，更恰当地称为Editor/Debugger。
在命令行窗口的提示符下键入 doc debug 将显示一些调试功能。此外，在帮助文档中，在搜索文档中键入“调试”将显示有关调试过程的基本信息。

在前面的例子中可以看出 if 子句的动作被执行并且打印出“In middle of range”，但仅仅从这个例子中就无法确定为什么会发生这种情况。有几种方法可以在文件（脚本或函数）中设置断点，以便检查变量或表达式。这些可以从编辑器/调试器中完成，或者可以从命令行窗口输入命令。例如，下面的 dbstop 命令将在该函数的第六行设置一个断点（这是 if 子句的操作），它允许在执行的那个点检查变量和/或表达式的值。函数 dbcont 可用于继续执行，而 dbquit 可用于退出调试模式。

通过键入表达式3<X接着3<X<6，我们可以确定表达式3<X将返回 0 或 1 。0 和 1 都小于 6 ，因此无论X！一旦进入调试模式，不再使用 dbcont 继续执行，而是可以使用 dbstep 一次单步执行其余代码。此外，在调试模式下，将鼠标悬停在编辑器中的变量上将显示变量的值。

断点也可以通过编辑器设置和清除。当一个文件在编辑器中打开时，在左边的行号和代码行之间是一条灰色的细条，它是断点胡同。在此，可执行代码行旁边有下划线标记（例如，与注释相反）。在一条线旁边的小巷中单击鼠标将在该线处创建一个断点（然后单击指示断点的红点将清除它）。这可以在图 6.1 中看到。

数学代写|matlab代写|Tasks in Live Scripts

在第 3 章中，我们看到了如何使用实时编辑器创建简单的实时脚本，其中可以包含文本、方程式、图像、代码和代码结果。还可以在实时脚本中嵌入控件，例如滑块、下拉菜单、复选框、编辑字段和按钮。还有一些任务可用于自动生成代码。预处理数据集有很多任务，还有一个绘图任务。根据您拥有的工具箱，可能还有与这些工具箱相关的任务。

例如，我们将创建一个实时脚本来绘制罪⁡(X)，使用滑块获取最大值X. 在实时编辑器中执行了以下步骤。

单击文本并编写脚本的句子描述。
单击分节符。
为最小和最大可能值编写代码X，并运行该部分。
单击分节符。
启动赋值语句“maxval =”，然后单击 Control，然后单击 Numeric Slider。这会弹出一个框，允许您选择滑块的标签（默认为变量名称），以及滑块的最小值、最大值和步长的值。每个都列出了默认值，也可以选择工作区变量。如图 6.2 所示。这里选择的是变量 minofx 和 maxofx，以及0.1为步骤。
为X和是变量到该部分并运行它。
单击分节符。
单击任务，然后单击创建绘图。这会出现一个任务，允许您选择绘图类型和变量X和是. 如图 6.3 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

数学代写|matlab代写|BMS13

Posted on 2022年11月11日2022年11月11日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|MENU-DRIVEN MODULAR PROGRAM

Many longer, more involved programs that interact with the user are menudriven, which means that the program prints a menu of choices and then continues to loop to print the menu of choices until the user chooses to end the program. A modular menu-driven program would typically have a function that presents the menu and gets the user’s choice, as well as functions to implement the action for each choice. These functions may have local functions. Also, the functions would error-check all user input.

As an example of such a menu-driven program, we will write a program to explore the constant $e$.

The constant $e$, called the natural exponential base, is used extensively in mathematics and engineering. There are many diverse applications of this constant. The value of the constant $e$ is approximately $2.7183$… Raising $e$ to the power of $\mathrm{x}$, or $e^{\mathrm{x}}$, is so common that this is called the exponential function. In MATLAB, as we have seen, there is a function for this, exp.
One way to determine the value of $e$ is by finding a limit.
$$
e=\lim _{n \rightarrow \infty}\left(1+\frac{1}{n}\right)^n
$$
As the value of $n$ increases toward infinity, the result of this expression approaches the value of $e$.

An approximation for the exponential function can be found using what is called a Maclaurin series:
$$
e^x \approx 1+\frac{x^1}{1 !}+\frac{x^2}{2 !}+\frac{x^3}{3 !}+\ldots
$$
We will write a program to investigate the value of $e$ and the exponential function. It will be menu-driven. The menu options will be:

Print an explanation of $e$.
Prompt the user for a value of $n$ and then find an approximate value for $e$ using the expression $(1+1 / n)^{\mathrm{n}}$.
Prompt the user for a value for $x$. Print the value of $\exp (\mathrm{x})$ using the builtin function. Find an approximate value for $e^x$ using the Maclaurin series just given.
Exit the program.

数学代写|matlab代写|Persistent Variables

Normally, when a function stops executing, the local variables from that function are cleared. That means that every time a function is called, memory is allocated and used while the function is executing but released when it ends. With variables that are declared as persistent variables, however, the value is not cleared so the next time the function is called, the variable still exists and retains its former value.

The following program demonstrates this. The script calls a function func1, which initializes a variable counter to 0 , increments it, and then prints the value. Every time this function is called, the variable is created, initialized to 0 , changed to 1 , and then cleared when the function exits. The script then calls a function func2, which first declares a persistent variable counter. If the variable has not yet been initialized, which will be the case the first time the function is called, it is initialized to 0 . Then, like the first function, the variable is incremented and the value is printed. With the second function, however, the variable remains with its value when the function exits, so the next time the function is called the variable is incremented again.

declares the variable counter, which allocates space for it but does not initialize it. The if statement then initializes it (the first time the function is called). In many languages, variables always have to be declared before they can be used; in MATLAB, this is true only for persistent variables.

The functions can be called from the script or from the Command Window, as shown. For example, the functions are called first from the script. With the persistent variable, the value of counter is incremented. Then, funcl is called from the Command Window and func 2 is also called from the Command Window. As the value of the persistent variable had the value 2, this time it is incremented to 3 .

matlab代写

数学代写|matlab代写|MENU-DRIVEN MODULAR PROGRAM

与用户交互的更多、更复杂的程序是菜单驱动的，这意味着程序打印一个选项菜单，然后继续循环打印选项菜单，直到用户选择结束程序。模块化菜单驱动程序通常具有呈现菜单并获取用户选择的功能，以及实现每个选择的动作的功能。这些函数可能具有局部函数。此外，这些函数将对所有用户输入进行错误检查。
作为此类菜单驱动程序的示例，我们将编写一个程序来探索常量 $e$.
常数e，称为自然指数基，广泛用于数学和工程。这个常数有许多不同的应用。常数的值 $e$ 大约是 $2.7183 \ldots$ 提高 $e$ 的力量 $\mathrm{x}$ ，或者 $e^{\mathrm{x}}$ ，如此普遍，以至于这被称为指数函数。正如我们所见，在 MATLAB 中有一个函数 exp。确定值的一种方法 $e$ 是通过找到一个限制。
$$
e=\lim _{n \rightarrow \infty}\left(1+\frac{1}{n}\right)^n
$$
作为价值 $n$ 向无穷大增加，该表达式的结果接近e.
可以使用所谓的麦克劳林级数找到指数函数的近似值:
$$
e^x \approx 1+\frac{x^1}{1 !}+\frac{x^2}{2 !}+\frac{x^3}{3 !}+\ldots
$$
我们将编写一个程序来调查 $e$ 和指数函数。它将是菜单驱动的。菜单选项将是:

打印说明 $e$.
提示用户输入值 $n$ 然后找到一个近似值 $e$ 使用表达式 $(1+1 / n)^{\mathrm{n}}$.
提示用户输入值 $x$. 打印值 $\exp (\mathrm{x})$ 使用内置函数。找到一个近似值 $e^x$ 使用刚刚给出的麦克劳林级数。
退出程序。

数学代写|matlab代写|Persistent Variables

通常，当一个函数停止执行时，该函数的局部变量会被清除。这意味着每次调用函数时，都会在函数执行时分配和使用内存，但在函数结束时释放内存。但是，对于声明为持久变量的变量，该值不会被清除，因此下次调用该函数时，该变量仍然存在并保留其先前的值。

下面的程序演示了这一点。该脚本调用函数 func1，它将变量 counter 初始化为 0 ，将其递增，然后打印该值。每次调用此函数时，都会创建变量，初始化为 0 ，更改为 1 ，然后在函数退出时清除。然后该脚本调用函数 func2，该函数首先声明一个持久变量 counter。如果变量还没有被初始化，这将是第一次调用函数时的情况，它被初始化为 0 。然后，像第一个函数一样，变量递增并打印值。然而，对于第二个函数，当函数退出时，变量保持其值，因此下次调用函数时，变量会再次递增。

声明变量counter，它为它分配空间但不初始化它。然后 if 语句对其进行初始化（第一次调用该函数时）。在许多语言中，变量总是必须先声明才能使用。在 MATLAB 中，这仅适用于持久变量。

可以从脚本或命令窗口调用函数，如图所示。例如，函数首先从脚本中调用。使用持久变量，计数器的值会增加。然后，从命令行窗口调用 funcl 并从命令行窗口调用 func 2。由于持久变量的值是 2，所以这次它增加到 3。

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

数学代写|matlab代写|CSC113

Posted on 2022年11月11日2022年11月11日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|MORE TYPES OF USER-DEFINED FUNCTIONS

We have already seen how to write a user-defined function that calculates and returns one value. This is just one type of function. It is also possible to have functions that return more than one value, and functions that do not return any values. We will categorize functions as follows:

Functions that calculate and return one value
Functions that calculate and return more than one value
Functions that just accomplish a task, such as printing, without returning any values

Thus, although many functions calculate and return values, some do not. Instead, some functions just accomplish a task. There are differences between these three types of functions, including the format of the function headers and also the way in which the functions are called. Regardless of what kind of function it is, all functions must be defined, and all function definitions consist of the header and the body. Also, the function must be called for it to be utilized. Although functions can be stored in script code files, for now we will concentrate on functions that are stored in their own code files with an extension of .m. In general, any function in MATLAB consists of the following:

The function header (the first line); this has:
the reserved word function
if the function returns values, the name(s) of the output argument(s), followed by the assignment operator $(=)$
the name of the function (important: this should be the same as the name of the file in which this function is stored to avoid confusion)
the input arguments in parentheses. if there are any (separated by commas if there is more than one).
A comment that describes what the function does (this is printed if help is used).
The body of the function, which includes all statements, including putting values in all output arguments if there are any.
end at the end of the function.

数学代写|matlab代写|Functions That Return More Than One Value

Functions that return one value have one output argument, as we saw previously. Functions that return more than one value must, instead, have more than one output argument in the function header in square brackets. That means that in the body of the function, values must be put in all output arguments listed in the function header. The general form of a function definition for a function that calculates and returns more than one value looks like this:

In the vector of output arguments, the output argument names are by convention separated by commas.

Choosing New, then Function brings up a template in the Editor that can then be filled in. If this is not desired, it may be easier to start with New Script.
For example, here is a function that calculates two values, both the area and the circumference of a circle; this is stored in a file called areacirc. $m$ :

Because this function is calculating two values, there are two output arguments in the function header ( area and circum), which are placed in square brackets [ ]. Therefore, somewhere in the body of the function, values have to be stored in both.

Because the function is returning two values, it is important to capture and store these values in separate variables when the function is called. In this case, the first value returned, the area of the circle, is stored in a variable $a$ and the second value returned is stored in a variable $c$ .

matlab代写

数学代写|matlab代写|MORE TYPES OF USER-DEFINED FUNCTIONS

我们已经看到了如何编写一个计算并返回一个值的用户定义函数。这只是一种功能。也可能有返回多个值的函数，以及不返回任何值的函数。我们将函数分类如下：

计算并返回一个值的函数
计算并返回多个值的函数
仅完成任务（例如打印）而不返回任何值的函数

因此，尽管许多函数会计算并返回值，但有些函数不会。相反，某些功能只是完成一项任务。这三种类型的函数之间存在差异，包括函数头的格式以及调用函数的方式。不管是什么函数，所有的函数都必须定义，所有的函数定义都由头和函数体组成。此外，必须调用该函数才能使用它。虽然函数可以存储在脚本代码文件中，但现在我们将专注于存储在扩展名为 .m 的代码文件中的函数。通常，MATLAB 中的任何函数都包含以下内容：

函数头（第一行）；这有：
保留字函数
如果函数返回值，则为输出参数的名称，后跟赋值运算符(=)
函数的名称（重要：这应该与存储此函数的文件的名称相同，以避免混淆）
括号中的输入参数。如果有（如果有多个，用逗号分隔）。
描述函数作用的注释（如果使用帮助则打印）。
函数体，包括所有语句，包括将值放入所有输出参数（如果有）。
在函数的末尾结束。

数学代写|matlab代写|Functions That Return More Than One Value

如前所述，返回一个值的函数有一个输出参数。相反，返回多个值的函数必须在方括号中的函数头中具有多个输出参数。这意味着在函数体中，值必须放在函数头中列出的所有输出参数中。计算并返回多个值的函数的函数定义的一般形式如下所示：

在输出参数的向量中，输出参数名称按照约定用逗号分隔。

选择 New，然后 Function 在 Editor 中打开一个模板，然后可以填写该模板。如果不需要，从 New Script 开始可能会更容易。
例如，这是一个计算两个值的函数，即圆的面积和周长；这存储在一个名为 areacirc 的文件中。米 :

因为这个函数计算两个值，所以函数头中有两个输出参数（area 和circ），它们放在方括号[]中。因此，在函数体的某个地方，值必须存储在两者中。

因为函数返回两个值，所以在调用函数时捕获这些值并将其存储在单独的变量中很重要。在这种情况下，返回的第一个值，即圆的面积，存储在一个变量中一个并且返回的第二个值存储在一个变量中C .

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

数学代写|matlab代写|ENES206

Posted on 2022年8月16日2022年8月16日 by statistics-lab

如果你也在怎样代写matlab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。

MATLAB是一个编程和数值计算平台，被数百万工程师和科学家用来分析数据、开发算法和创建模型。

我们提供的matlab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

数学代写|matlab代写|TAYLOR AND LAURENT EXPANSIONS AND SINGULARITIES

In the previous section we showed what a crucial role singularities play in complex integration. Before we can find the most general way of computing a closed complex integral, our understanding of singularities must deepen. For this, we employ power series.

One reason why power series are so important is their ability to provide locally a general representation of a function even when its arguments are complex. For example, when we were introduced to trigonometric functions in high school, it was in the context of a right triangle and a real angle. However, when the argument becomes complex, this geometrical description disappears and power series provide a formalism for defining the trigonometric functions, regardless of the nature of the argument.

Let us begin our analysis by considering the complex function $f(z)$, which is analytic everywhere on the boundary, and the interior of a circle whose center is at $z=z_{0}$. Then, if $z$ denotes any point within the circle, we have from Cauchy’s integral formula that
$$
f(z)=\frac{1}{2 \pi i} \oint_{C} \frac{f(\zeta)}{\zeta-z} d \zeta=\frac{1}{2 \pi i} \oint_{C} \frac{f(\zeta)}{\zeta-z_{0}}\left[\frac{1}{1-\left(z-z_{0}\right) /\left(\zeta-z_{0}\right)}\right] d \zeta,
$$
where $C$ denotes the closed contour. Expanding the bracketed term as a geometric series, we find that
$$
f(z)=\frac{1}{2 \pi i}\left[\oint_{C} \frac{f(\zeta)}{\zeta-z_{0}} d \zeta+\left(z-z_{0}\right) \oint_{C} \frac{f(\zeta)}{\left(\zeta-z_{0}\right)^{2}} d \zeta+\cdots+\left(z-z_{0}\right)^{n} \oint_{C} \frac{f(\zeta)}{\left(\zeta-z_{0}\right)^{n+1}} d \zeta+\cdots\right]
$$
Applying Cauchy’s integral formula to each integral in Equation 1.7.2, we finally obtain
$$
f(z)=f\left(z_{0}\right)+\frac{\left(z-z_{0}\right)}{1 !} f^{\prime}\left(z_{0}\right)+\cdots+\frac{\left(z-z_{0}\right)^{n}}{n !} f^{(n)}\left(z_{0}\right)+\cdots
$$
or the familiar formula for a Taylor expansion. Consequently, we can expand any analytic function into a Taylor series. Interestingly, the radius of convergence ${ }^{6}$ of this series may be shown to be the distance between $z_{0}$ and the nearest nonanalytic point of $f(z)$.

数学代写|matlab代写|EVALUATION OF REAL DEFINITE INTEGRALS

One of the important applications of the theory of residues consists of the evaluation of certain types of real definite integrals. Similar techniques apply when the integrand contains a sine or cosine.

Example 1.9.1
Let us evaluate the integral
$$
\int_{0}^{\infty} \frac{d x}{x^{2}+1}=\frac{1}{2} \int_{-\infty}^{\infty} \frac{d x}{x^{2}+1} .
$$
This integration occurs along the real axis. In terms of complex variables, we can rewrite Equation $1.9 .1$ as
$$
\int_{0}^{\infty} \frac{d x}{x^{2}+1}=\frac{1}{2} \int_{C_{1}} \frac{d z}{z^{2}+1},
$$
where the contour $C_{1}$ is the line $\Im(z)=0$. However, the use of the residue theorem requires an integration along a closed contour. Let us choose the one pictured in Figure 1.9.1. Then
$$
\oint_{C} \frac{d z}{z^{2}+1}=\int_{C_{1}} \frac{d z}{z^{2}+1}+\int_{C_{2}} \frac{d z}{z^{2}+1},
$$
where $C$ denotes the complete closed contour and $C_{2}$ denotes the integration path along a semicircle at infinity. Clearly we want the second integral on the right side of Equation $1.9 .3$ to vanish; otherwise, our choice of the contour $C_{2}$ is poor. Because $z=R c^{\theta i}$ and $d z=i R e^{\theta i} d \theta$
$$
\left|\int_{C_{2}} \frac{d z}{z^{2}+1}\right|=\left|\int_{0}^{\pi} \frac{i R \exp (\theta i)}{1+R^{2} \exp (2 \theta i)} d \theta\right| \leq \int_{0}^{\pi} \frac{R}{R^{2}-1} d \theta
$$
which tends to zero as $R \rightarrow \infty$. On the other hand, the residue theorem gives
$$
\oint_{C} \frac{d z}{z^{2}+1}=2 \pi i \operatorname{Res}\left(\frac{1}{z^{2}+1} ; i\right)=2 \pi i \lim _{z \rightarrow i} \frac{z-i}{z^{2}+1}=2 \pi i \times \frac{1}{2 i}=\pi .
$$

matlab代写

数学代写|matlab代写|TAYLOR AND LAURENT EXPANSIONS AND SINGULARITIES

在上一节中，我们展示了奇点在复杂积分中的关键作用。在我们找到计算闭复积分的最通用方法之前，我们必须加深对奇点的理解。为此，我们采用幂级数。
幂级数如此重要的一个原因是它们能够在局部提供函数的一般表示，即使它的参数很复杂。例如，当我们在高中时接触三角函数时，它是在直角三角形和实角的背景下进行的。然而，当论证变得复杂时，这种几何描述就消失了，幂级数为定义三角函数提供了一种形式，而不管论证的性质如何。
让我们通过考虑复函数开始我们的分析 $f(z)$ ，它在边界上的任何地方都是解析的，并且中心在的圆的内部 $z=z_{0}$. 那么，如果 $z$ 表示圆内的任何点，我们从柯西的积分公式中得到
$$
f(z)=\frac{1}{2 \pi i} \oint_{C} \frac{f(\zeta)}{\zeta-z} d \zeta=\frac{1}{2 \pi i} \oint_{C} \frac{f(\zeta)}{\zeta-z_{0}}\left[\frac{1}{1-\left(z-z_{0}\right) /\left(\zeta-z_{0}\right)}\right] d \zeta
$$
在哪里 $C$ 表示闭合轮廓。将括号中的项扩展为几何级数，我们发现
$$
f(z)=\frac{1}{2 \pi i}\left[\oint_{C} \frac{f(\zeta)}{\zeta-z_{0}} d \zeta+\left(z-z_{0}\right) \oint_{C} \frac{f(\zeta)}{\left(\zeta-z_{0}\right)^{2}} d \zeta+\cdots+\left(z-z_{0}\right)^{n} \oint_{C} \frac{f(\zeta)}{\left(\zeta-z_{0}\right)^{n+1}} d \zeta+\cdots\right]
$$
将柯西积分公式应用于方程 1.7.2 中的每个积分，我们最终得到
$$
f(z)=f\left(z_{0}\right)+\frac{\left(z-z_{0}\right)}{1 !} f^{\prime}\left(z_{0}\right)+\cdots+\frac{\left(z-z_{0}\right)^{n}}{n !} f^{(n)}\left(z_{0}\right)+\cdots
$$
或熟悤的泰勒展开式。因此，我们可以将任何解析函数扩展为泰勒级数。有趣的是，收敛半径 ${ }^{6}$ 这一系列的可能被证明是之间的距离 $z_{0}$ 和最近的非解析点 $f(z)$.

数学代写|matlab代写|EVALUATION OF REAL DEFINITE INTEGRALS

余数理论的重要应用之一是评估某些类型的实定积分。当被积函数包含正弦或余弦时，类似的技术也适用。

示例 $1.9 .1$
让我们评估积分
$$
\int_{0}^{\infty} \frac{d x}{x^{2}+1}=\frac{1}{2} \int_{-\infty}^{\infty} \frac{d x}{x^{2}+1}
$$
这种整合沿实轴发生。就复变量而言，我们可以重写方程1.9.1作为
$$
\int_{0}^{\infty} \frac{d x}{x^{2}+1}=\frac{1}{2} \int_{C_{1}} \frac{d z}{z^{2}+1}
$$
轮廓在哪里 $C_{1}$ 是线 $\Im(z)=0$. 然而，使用余数定理需要沿闭合轮廓进行积分。让我们选择图 $1.9 .1$ 中所示的那个。然后
$$
\oint_{C} \frac{d z}{z^{2}+1}=\int_{C_{1}} \frac{d z}{z^{2}+1}+\int_{C_{2}} \frac{d z}{z^{2}+1},
$$
在哪里 $C$ 表示完整的闭合轮廓，并且 $C_{2}$ 表示沿无限远半圆的积分路径。显然我们想要方程右边的第二个积分 1.9.3消失; 否则，我们选择的轮廓 $C_{2}$ 很穷。因为 $z=R c^{\theta i}$ 和 $d z=i R e^{\theta i} d \theta$
$$
\left|\int_{C_{2}} \frac{d z}{z^{2}+1}\right|=\left|\int_{0}^{\pi} \frac{i R \exp (\theta i)}{1+R^{2} \exp (2 \theta i)} d \theta\right| \leq \int_{0}^{\pi} \frac{R}{R^{2}-1} d \theta
$$
趋向于零 $R \rightarrow \infty$. 另一方面，剩余定理给出
$$
\oint_{C} \frac{d z}{z^{2}+1}=2 \pi i \operatorname{Res}\left(\frac{1}{z^{2}+1} ; i\right)=2 \pi i \lim _{z \rightarrow i} \frac{z-i}{z^{2}+1}=2 \pi i \times \frac{1}{2 i}=\pi .
$$

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

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