Posted on Categories:CS代写, Neural Networks, 神经网络, 计算机代写

# 计算机代写|神经网络代写Neural Networks代考|Using the Chain Rule

avatest™

神经网络Neural Networks作业代写，免费提交作业要求， 满意后付款，成绩80\%以下全额退款，安全省心无顾虑。专业硕 博写手团队，所有订单可靠准时，保证 100% 原创。my-assignmentexpert™， 最高质量的神经网络Neural Networks作业代写，服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面，考虑到同学们的经济条件，在保障代写质量的前提下，我们为客户提供最合理的价格。 由于统计Statistics作业种类很多，同时其中的大部分作业在字数上都没有具体要求，因此神经网络Neural Networks作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。

## avatest™帮您通过考试

avatest™的各个学科专家已帮了学生顺利通过达上千场考试。我们保证您快速准时完成各时长和类型的考试，包括in class、take home、online、proctor。写手整理各样的资源来或按照您学校的资料教您，创造模拟试题，提供所有的问题例子，以保证您在真实考试中取得的通过率是85%以上。如果您有即将到来的每周、季考、期中或期末考试，我们都能帮助您！

•最快12小时交付

•200+ 英语母语导师

•70分以下全额退款

## 计算机代写|神经网络代写Neural Networks代考|Using the Chain Rule

There are many different rules in Calculus to allow you to take derivatives manually. We just saw an example of the power rule. This rule states that given the equation:
$$f(x)=x^n$$
the derivative of $\mathrm{f}(\mathrm{x})$ will be as follows:
$$f^{\prime}(x)=n x^{n-1}$$
This allows you to quickly take the derivative of any power. There are many other derivative rules, and they are very useful to know. However, if you do not wish to learn manual differentiation, you can generally get by without it by using a program such as $R$.

However, there is one more rule that is very useful to know. This rule is called the chain rule. The chain rule deals with composite functions. A composite function is nothing more than when one function takes the results of a second function as input. This may sound complex, but programmers make use of composite functions all the time. Here is an example of a composite function call in Java.
System.out.printin( Math.pow $(3,2)$ );
This is a composite function because we take the result of the function pow and feed it to println.

The first step is to calculate the gradients of the neural network. The gradients are used to calculate the slope, or gradient, of the error function for a particular weight. A weight is a connection between two neurons. Calculating the gradient of the error function allows the training method to know that it should either increase or decrease the weight. There are a number of different training methods that make use of gradients. These training methods are called propagation training. This book will discuss the following propagation training methods:

• Backpropagation
• Resilient Propagation
• Quick Propagation
This chapter will focus on using the gradients to train the neural network using backpropagation. The next few chapters will cover the other propagation methods.
• First of all, let’s look at what a gradient is. Basically, training is a search. You are searching for the set of weights that will cause the neural network to have the lowest global error for a training set. If we had an infinite amount of computation resources, we would simply try every possible combination of weights and see which one provided the absolute best global error.
• Because we do not have unlimited computing resources, we have to use some sort of shortcut. Essentially, all neural network training methods are really a kind of shortcut. Each training method is a clever way of finding an optimal set of weights without doing an impossibly exhaustive search.
• Consider a chart that shows the global error of a neural network for each possible weight. This graph might look something like Figure 4.1 .

## 计算机代写|神经网络代写Neural Networks代考|Using the Chain Rule

$$f(x)=x^n$$
$\mathrm{f}(\mathrm{x})$的导数为:
$$f^{\prime}(x)=n x^{n-1}$$

## 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。