### 机器学习代写|tensorflow代写|Data representation and features

TensorFlow是一个用于机器学习和人工智能的免费和开源的软件库。它可以用于一系列的任务，但特别关注深度神经网络的训练和推理。

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 机器学习代写|tensorflow代写|Data representation and features

Data is a first-class citizen of machine learning. Computers are nothing more than sophisticated calculators, so the data we feed our machine-learning systems must be mathematical objects such as scalars, vectors, matrices, and graphs.

The basic theme in all forms of representation is features, which are observable properties of an object:

“Vectors have a flat and simple structure, and are the typical embodiments of data in most real-world machine-learning applications. A scalar is a single element in the vector. Vectors have two attributes: a natural number representing the dimension of the vector, and a type (such as real numbers, integers, and so on). Examples of 2D vectors of integers are $(1,2)$ and $(-6,0)$; similarly, a scalar could be 1 or the character $a$. Examples of $3 \mathrm{D}$ vectors of real numbers are (1.1, $2.0,3.9) \mathrm{~ a n d ~ (}$ same type. In a program that uses machine learning, a vector measures a property of the data, such as color, density, loudness, or proximity-anything you can describe with a series of numbers, one for each thing being measured.

• Moreover, a vector of vectors is a matrix. If each feature vector describes the features of one object in your dataset, the matrix describes all the objects; each item in the outer vector is a node that’s a list of features of one object.
• Graphs, on the other hand, are more expressive. A graph is a collection of objects (nodes) that can be linked with edges to represent a network. A graphical structure enables representing relationships between objects, such as in a friendship network or a navigation route of a subway system. Consequently, they’re tremendously harder to manage in machine-learning applications. In this book, our input data will rarely involve a graphical structure.

Feature vectors are practical simplifications of real-world data, which can be too complicated to deal with. Instead of attending to every little detail of a data item, using a feature vector is a practical simplification. A car in the real world, for example, is much more than the text used to describe it. A car salesman is trying to sell you the car, not the intangible words spoken or written. Those words are abstract concepts, similar to the way that feature vectors are summaries of the data.

## 机器学习代写|tensorflow代写|Distance metrics

If you have feature vectors of cars you may want to buy, you can figure out which two cars are most similar by defining a distance function on the feature vectors. Comparing similarities between objects is an essential component of machine learning. Feature vectors allow us to represent objects so that we may compare them in a variety of ways. A standard approach is to use the Euclidian distance, which is the geometric interpretation you may find most intuitive when thinking about points in space.

Suppose that we have two feature vectors, $x=\left(x_{1}, x_{2}, \ldots, x_{n}\right)$ and $y=\left(y_{1}, y_{2}, \ldots, y_{n}\right)$. The Euclidian distance $|x-y|$ is calculated with the following equation, which scholars call the $L .2$ norm.
$$\sqrt{\left(x_{1}+y_{1}\right)^{2}+\left(x_{2}-y_{2}\right)^{2}+\ldots+\left(x_{n}-y_{n}\right)^{2}}$$
The Euclidian distance between $(0,1)$ and $(1,0)$ is
\begin{aligned} &|(0,1)-(1,0)| \ &|(-(1,1))| \ &\sqrt{(-1)^{2}+1^{2}} \ &=\sqrt{2}=1.414 \ldots \end{aligned}
That function is only one of many possible distance functions, however. The L0, L1, and L-infinity norms also exist. All these norms are valid ways to measure distance. Here they are in more detail:

• The LO norm counts the total nonzero elements of a vector. The distance between the origin $(0,0)$ and vector $(0,5)$ is 1 , for example, because there’s only one nonzero element. The Lo distance between $(1,1)$ and $(2,2)$ is 2 , because neither dimension matches up. Imagine that the first and second dimensions represent username and password, respectively. If the Lo distance between a login attempt and the true credentials is 0 , the login is successful. If the distance is 1 , either the

username or password is incorrect, but not both. Finally, if the distance is 2 , neither username nor password is found in the database.
The $L 1$ norm, shown in figure $1.8$, is defined as $\sum x_{n}$. The distance between two vectors under the Ll norm is also referred to as the Manhattan distance. Imagine living in a downtown area like Manhattan, where the streets form a grid. The shortest distance from one intersection to another is along the blocks. Similarly, the Ll distance between two vectors is along the orthogonal directions. The distance between $(0,1)$ and $(1,0)$ under the Ll norm is 2. Computing the Ll distance between two vectors is the sum of absolute differences at each dimension, which is a useful measure of similarity.

## 机器学习代写|tensorflow代写|Supervised learning

By definition, a supervisor is someone higher up in the chain of command. When we’re in doubt, our supervisor dictates what to do. Likewise, supervised learning is all about learning from examples laid out by a supervisor (such as a teacher).

A supervised machine-learning system needs labeled data to develop a useful understanding, which we call its model. Given many photographs of people and their recorded corresponding ethnicities, for example, we can train a model to classify the ethnicity of a never-before-seen person in an arbitrary photograph. Simply put, a model is a function that assigns a label to data by using a collection of previous examples, called a training dataset, as reference.

A convenient way to talk about models is through mathematical notation. Let $x$ be an instance of data, such as a feature vector. The label associated with $x$ is $f(x)$, often referred to as the ground truth of $x$. Usually, we use the variable $y=f(x)$ because it’s quicker to write. In the example of classifying the ethnicity of a person through a photograph, $x$ can be a 100-dimensional vector of various relevant features, and $y$ is one of a couple of values to represent the various ethnicities. Because $y$ is discrete with few

values, the model is called a classifier. If $y$ can result in many values, and the values have a natural ordering, the model is called a regressor.

Let’s denote a model’s prediction of $x$ as $g(x)$. Sometimes, you can tweak a model to change its performance dramatically. Models have parameters that can be tuned by a human or automatically. We use the vector to represent the parameters. Putting it all together, $g(x \mid)$ more completely represents the model, read ” $g$ of $x$ given.”

## 机器学习代写|tensorflow代写|Data representation and features

“向量具有扁平而简单的结构，是大多数现实世界机器学习应用中数据的典型体现。标量是向量中的单个元素。向量有两个属性：表示向量维度的自然数和类型（如实数、整数等）。整数的二维向量的例子是(1,2)和(−6,0); 同样，标量可以是 1 或字符一个. 示例3D实数向量为 (1.1,2.0,3.9) 一个nd (同类型。在使用机器学习的程序中，向量测量数据的属性，例如颜色、密度、响度或接近度——任何你可以用一系列数字描述的东西，每个被测量的东西一个。

• 此外，向量的向量是矩阵。如果每个特征向量描述数据集中一个对象的特征，则矩阵描述所有对象；外部向量中的每个项目都是一个节点，它是一个对象的特征列表。
• 另一方面，图表更具表现力。图是对象（节点）的集合，可以与边链接以表示网络。图形结构能够表示对象之间的关系，例如在友谊网络或地铁系统的导航路线中。因此，它们在机器学习应用程序中非常难以管理。在本书中，我们的输入数据很少涉及图形结构。

## 机器学习代写|tensorflow代写|Distance metrics

(X1+是1)2+(X2−是2)2+…+(Xn−是n)2

|(0,1)−(1,0)| |(−(1,1))| (−1)2+12 =2=1.414…

• LO 范数计算向量的总非零元素。原点之间的距离(0,0)和矢量(0,5)是 1 ，例如，因为只有一个非零元素。之间的 Lo 距离(1,1)和(2,2)是 2 ，因为两个维度都不匹配。想象一下，第一维和第二维分别代表用户名和密码。如果登录尝试和真实凭据之间的 Lo 距离为 0 ，则登录成功。如果距离为 1 ，则

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## 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。