## 机器学习代写|tensorflow代写|Polynomial modelUsing regression for call-center volume prediction

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。

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

• 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|Cleaning the data for regression

First, download this data-a set of phone calls from the summer of 2014 from the New York City 311 service-from http://mng.bz/P16w. Kaggle has other 311 datasets, but you’ll use this particular data due to its interesting properties. The calls are formatted as a comma-separated values (CSV) file that has several interesting features, including the following:

• A unique call identifier showing the date when the call was created
” The location and ZIP code of the reported incident or information request
• The specific action that the agent on the call took to resolve the issue
• What borough (such as the Bronx or Queens) the call was made from
• The status of the call
This dataset contains lot of useful information for machine learning, but for purposes of this exercise, you care only about the call-creation date. Create a new file named 311.py. Then write a function to read each line in the CSV file, detect the week number, and sum the call counts by week.

Your code will need to deal with some messiness in this data file. First, you aggregate individual calls, sometimes hundreds in a single day, into a seven-day or weekly bin, as identified by the bucket variable in listing 4.1. The freq (short for frequency) variable holds the value of calls per week and per year. If the $311 \mathrm{CSV}$ contains more than a year’s worth of data (as other 311 CSVs that you can find on Kaggle do), gin up your code to allow for selection by year of calls to train on. The result of the code in listing $4.1$ is a freq dictionary whose values are the number of calls indexed by year and by week number via the period variable. The $t$. tm_year variable holds the parsed year resulting from passing the call-creation-time value (indexed in the CSV as date_idx, an integer defining the column number where the date field is located) and the date_parse format string to Python’s time library’s strptime (or string parse time) function. The date parse format string is a pattern defining the way the date appears as text in the CSV so that Python knows how to convert it to a datetime representation.

## 机器学习代写|tensorflow代写|What’s in a bell curve? Predicting Gaussian distributions

A bell or normal curve is a common term to describe data that we say fits a normal distribution. The largest $Y$ values of the data occur in the middle or statistically the mean $\mathrm{X}$ value of the distribution of points, and the smaller $Y$ values occur on the early and tail X values of the distribution. We also call this a Gaussian distribution after the famous German mathematician Carl Friedrich Gauss, who was responsible for the Gaussian function that describes the normal distribution.

We can use the NumPy method np.random.normal to generate random points sampled from the normal distribution in Python. The following equation shows the Gaussian function that underlies this distribution:
$$e^{\frac{\left(-(x-\mu)^{2}\right)}{2 \sigma^{2}}}$$
The equation includes the parameters $\mu$ (pronounced $m u$ ) and $\sigma$ (pronounced sigma), where $m u$ is the mean and sigma is the standard deviation of the distribution, respectively. Mu and sigma are the parameters of the model, and as you have seen, TensorFlow will learn the appropriate values for these parameters as part of training a model.

To convince yourself that you can use these parameters to generate bell curves, you can type the code snippet in listing $4.3$ into a file named gaussian.py and then run it to produce the plot that follows it. The code in listing $4.3$ produces the bell curve visualizations shown in figure 4.4. Note that I selected values of mu between $-1$ and 2 . You should see center points of the curve in figure 4.4, as well as standard deviations (sigma) between 1 and 3 , so the width of the curves should correspond to those values inclusively. The code plots 120 linearly-spaced points with $\mathrm{X}$ values between $-3$ and 3 and $\mathrm{Y}$ values between 0 and 1 that fit the normal distribution according to $\mathrm{mu}$ and sigma, and the output should look like figure 4.4.

## 机器学习代写|tensorflow代写|Training your call prediction regressor

Now you are ready to use TensorFlow to fit your NYC 311 data to this model. It’s probably clear by looking at the curves that they seem to comport naturally with the 311 data, especially if TensorFlow can figure out the values of mu that put the center point of the curve near spring and summer and that have a fairly large call volume, as well as the sigma value that approximates the best standard deviation.

Listing $4.4$ sets up the TensorFlow training session, associated hyperparameters, learning rate, and number of training epochs. I’m using a fairly large step for learning rate so that TensorFlow can appropriately scan the values of mu and sig by taking bigenough steps before settling down. The number of epochs-5,000-gives the algorithm enough training steps to settle on optimal values. In local testing on my laptop, these hyperparameters arrived at strong accuracy $(99 \%)$ and took less than a minute. But I could have chosen other hyperparameters, such as a learning rate of $0.5$, and given the training process more steps (epochs). Part of the fun of machine learning is hyperparameter training, which is more art than science, though techniques such as meta-learning and algorithms such as HyperOpt may ease this process in the future. A full discussion of hyperparameter tuning is beyond the scope of this chapter, but an online search should yields thousands of relevant introductions.

When the hyperparameters are set up, define the placeholders $\mathrm{X}$ and $\mathrm{Y}$, which will be used for the input week number and associated number of calls (normalized), respectively. Earlier, I mentioned normalizing the Y values and creating the ny_train variable in listing $4.2$ to ease learning. The reason is that the model Gaussian function that we are attempting to learn has $\mathrm{Y}$ values only between 0 and 1 due to the exponent e. The model function defines the Gaussian model to learn, with the associated variables mu and sig initialized arbitrarily to 1. The cost function is defined as the L2 norm, and the training uses Gradient descent. After training your regressor for 5,000 epochs, the final steps in listing $4.4$ print the learned values for mu and sig.

## 机器学习代写|tensorflow代写|Polynomial model

F(X)=在nXn+…+在1X+在0

## 机器学习代写|tensorflow代写|Application of linear regression

• 马萨诸塞大学阿默斯特分校在 https://scholarworks.umass.edu/data 提供各种类型的小型数据集。
• Kaggle 在 https://www.kaggle.com/datasets 为机器学习竞赛提供所有类型的大规模数据。
= Data.gov (https://catalog.data.gov) 是美国政府的一项开放数据计划，其中包含许多有趣且实用的数据集。

import csv import time
def read(filename, date_idx, date_parse, year, bucket=7)=
days_in_year=365

if t.tm_year == year and吨.tm_yday<(days_in_year-1):

return freq

## 有限元方法代写

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。

## 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。

## 机器学习代写|tensorflow代写|Polynomial model

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。

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

• 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|Polynomial model

Linear models may be an intuitive first guess, but real-world correlations are rarely so simple. The trajectory of a missile through space, for example, is curved relative to the observer on Earth. Wi-Fi signal strength degrades with an inverse square law. The change in height of a flower over its lifetime certainly isn’t linear.

When data points appear to form smooth curves rather than straight lines, you need to change your regression model from a straight line to something else. One such approach is to use a polynomial model. A polynomial is a generalization of a linear function. The $n$th degree polynomial looks like the following:
$$f(x)=w_{n} x^{n}+\ldots+w_{1} x+w_{0}$$
NOTE When $n=1$, a polynomial is simply a linear equation $f(x)=w_{1} x+\mathrm{w}_{0}$.
Consider the scatter plot in figure $3.10$, showing the input on the $x$-axis and the output on the y-axis. As you can tell, a straight line is insufficient to describe all the data. A polynomial function is a more flexible generalization of a linear function.

## 机器学习代写|tensorflow代写|Regularization

Don’t be fooled by the wonderful flexibility of polynomials, as shown in section $3.3$. Just because higher-order polynomials are extensions of lower ones doesn’t mean that you should always prefer the more flexible model.

In the real world, raw data rarely forms a smooth curve mimicking a polynomial. Suppose that you’re plotting house prices over time. The data likely will contain fluctuations. The goal of regression is to represent the complexity in a simple mathematical equation. If your model is too flexible, the model may be overcomplicating its interpretation of the input.

Take, for example, the data presented in figure 3 .12. You try to fit an eighth-degree polynomial into points that appear to follow the equation $y=x^{2}$. This process fails miserably, as the algorithm tries its best to update the nine coefficients of the polynomial.

To influence the learning algorithm to produce a smaller coefficient vector (let’s call it $w$ ), you add that penalty to the loss term. To control how significantly you want to weigh the penalty term, you multiply the penalty by a constant non-negative number, $\lambda$, as follows:
$$\operatorname{Cost}(X, Y)=\operatorname{Loss}(X, Y)+\lambda$$
If $\lambda$ is set to 0 , regularization isn’t in play. As you set $\lambda$ to larger and larger values, parameters with larger norms will be heavily penalized. The choice of norm varies case by case, but parameters are typically measured by their Ll or L2 norm. Simply put, regularization reduces some of the flexibility of the otherwise easily tangled model.

To figure out which value of the regularization parameter $\lambda$ performs best, you must split your dataset into two disjointed sets. About $70 \%$ of the randomly chosen input/output pairs will consist of the training dataset; the remaining $30 \%$ will be used for testing. You’ll use the function provided in listing $3.4$ for splitting the dataset.

## 机器学习代写|tensorflow代写|Application of linear regression

Running linear regression on fake data is like buying a new car and never driving it. This awesome machinery begs to manifest itself in the real world! Fortunately, many datasets are available online to test your newfound knowledge of regression:

• The University of Massachusetts Amherst supplies small datasets of various types at https://scholarworks.umass.edu/data.
• Kaggle provides all types of large-scale data for machine-learning competitions at https://www.kaggle.com/datasets.
= Data.gov (https://catalog.data.gov) is an open data initiative by the US government that contains many interesting and practical datasets.

A good number of datasets contain dates. You can find a dataset of all phone calls to the 311 nonemergency line in Los Angeles, California, for example, at https://www .dropbox.com/s/naw774olqkve7sc/311.csv?dl=0. A good feature to track could be the frequency of calls per day, week, or month. For convenience, listing $3.6$ allows you to obtain a weekly frequency count of data items.

import csv import time
def read(filename, date_idx, date_parse, year, bucket $=7)=$
days_in_year $=365$
freq $={} \quad \mid$ Sets up initial frequency map
for period in range $(0$, int(days_in year / bucket)):
freq [period] $=0$
With open(filename, “rb’) as csvfile: csvreader = csv. reader (csvfile) csvreader. next() $\quad$ Reads data and aggregates count per period
if $\operatorname{row}\left[\right.$ date_idx] $=={ }^{\prime}=$
continue
$t=$ time.strptime (row [date_idx], date_parse)
if t.tm_year == year and $t .$ tm_yday $<$ (days_in_year-1):
freq[int(t.tm_yday / bucket)] $+=1$
return freq
This code gives you the training data for linear regression. The freq variable is a dictionary that maps a period (such as a week) to a frequency count. A year has 52 weeks, so you’ll have 52 data points if you leave bucket=7 as is.

## 机器学习代写|tensorflow代写|Polynomial model

F(X)=在nXn+…+在1X+在0

## 机器学习代写|tensorflow代写|Application of linear regression

• 马萨诸塞大学阿默斯特分校在 https://scholarworks.umass.edu/data 提供各种类型的小型数据集。
• Kaggle 在 https://www.kaggle.com/datasets 为机器学习竞赛提供所有类型的大规模数据。
= Data.gov (https://catalog.data.gov) 是美国政府的一项开放数据计划，其中包含许多有趣且实用的数据集。

import csv import time
def read(filename, date_idx, date_parse, year, bucket=7)=
days_in_year=365

if t.tm_year == year and吨.tm_yday<(days_in_year-1):

return freq

## 有限元方法代写

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。

## 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。

## 机器学习代写|tensorflow代写|Putting it all together: The TensorFlow system

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。

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

• 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|architecture and API

I have not delved into everything that TensorFlow can do-the rest of the book is for those topics-but I have illustrated its core components and the interfaces between them. The collection of these components and interfaces makes up the TensorFlow architecture, shown in figure 2.9.

The TensorFlow 2 version of the listings incorporates new features, including always eager execution and updated package names for the optimizers and training. The new listings work well in Python 3; I welcome your feedback on them if you give them a try. You can find the TensorFlow 2 listing code at https:// github.com/ chrismattmann/MLwithTensorFlow2ed/tree/master/TFv2.

One reasons why it’s so hard to pick a version of the framework to depend on and go with it is that software changes so quickly. The author of this book found that out when trying to run some listings from the first edition. Though the concepts and

architecture and system itself remained the same and are close to what would actually run, even in two years, TensorFlow changed a great deal, with more than 20 versions released since TensorFlow $1.0$ and the current $1.15 .2$ version-notably, the last $1 . x$ release. Parts of these changes had to do with breaking changes in the l.x version of the system, but other parts had to do with a more fundamental architectural understanding gained by performing some of the suggested examples at the end of each chapter, stumbling, and then realizing that TensorFlow has code and interfaces to tackle the problem. As Scott Penberthy, head of applied AI at Google and TensorFlow guru, states in the foreword, chasing TensorFlow versions isn’t the point; the details, architecture, cleaning steps, processing, and evaluation techniques will withstand the test of time while the great software engineers improve the scaffolding around tensors.
Today, TensorFlow $2.0$ is attracting a lot of attention, but rather than chase the latest version, which has some fundamental (breaking) changes from the $1 . x$ version, I want to deliver a core understanding that will last beyond (breaking) changes and enshrine the fundamentals of machine learning and concepts that make TensorFlow so special.

## 机器学习代写|tensorflow代写|Formal notation

If you have a hammer, every problem looks like a nail. This chapter demonstrates the first major machine-learning tool, regression, and formally defines it by using precise mathematical symbols. Learning regression first is a great idea, because many of the skills you’ll develop will carry over to other types of problems in future chapters. By the end of this chapter, regression will become the “hammer” in your box of machinelearning tools.

## 机器学习代写|tensorflow代写|How do you know the regression algorithm is working

• 方差表示预测对所使用的训练集的敏感程度。理想情况下，您如何选择训练集并不重要，这意味着需要较低的方差。学习算法过拟合数据。在这种情况下，最佳拟合曲线将与图一致3.3理想情况下，最佳拟合曲线可以很好地拟合训练数据，但它的表现可能很糟糕-在训练数据和测试数据上。如果在测试数据和训练数据上进行评估时，有一个机会数据（见图 3.3）。
在光谱的另一端，a 在测试数据上表现不佳，但在如此灵活的模型上表现良好，可能会更好地泛化到过度拟合。

## 有限元方法代写

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。

## 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。

## 机器学习代写|tensorflow代写|Understanding code as a graph

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。

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

• 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|Understanding code as a graph

Consider a doctor who predicts the expected weight of a newborn to be $7.5$ pounds. You’d like to figure out how that prediction differs from the actual measured weight. Being an overly analytical engineer, you design a function to describe the likelihood of all possible weights of the newborn. A weight of 8 pounds is more likely than 10 pounds, for example.

You can choose to use the Gaussian (otherwise known as normal) probability distribution function. This function takes a number as input and outputs a non-negative number describing the probability of observing the input. This function shows up all the time in machine learning and is easy to define in TensorFlow. It uses multiplication, division, negation, and a couple of other fundamental operators.

Think of every operator as being a node in a graph. Whenever you see a plus symbol (+) or any mathematical concept, picture it as one of many nodes. The edges between these nodes represent the composition of mathematical functions. Specifically, the negative operator we’ve been studying is a node, and the incoming/outgoing edges of this node are how the Tensor transforms. A tensor flows through the graph, which is why this library is called TensorFlow.

Here’s a thought: every operator is a strongly typed function that takes input tensors of a dimension and produces output of the same dimension. Figure $2.3$ is an example of how the Gaussian function can be designed with TensorFlow. The function is represented as a graph in which operators are nodes and edges represent interactions between nodes. This graph as a whole represents a complicated mathematical function (specifically, the Gaussian function). Small segments of the graph represent simple mathematical concepts, such as negation and doubling.

TensorFlow algorithms are easy to visualize. They can be described simply by flowcharts. The technical (and more correct) term for such a flowchart is a dataflow graph. Every arrow in a dataflow graph is called an edge. In addition, every state of the dataflow graph is called a node. The purpose of the session is to interpret your Python code into a dataflow graph and then associate the computation of each node of the graph to the CPU or GPU.

## 机器学习代写|tensorflow代写|Setting session configurations

You can also pass options to tf.Session. TensorFlow automatically determines the best way to assign a GPU or CPU device to an operation, for example, depending on what’s available. You can pass an additional option, log_device_placement=True, when creating a session. Listing $2.7$ shows you exactly where on your hardware the computations are evoked.

import tensorflow as tf
options = tf.RunOptions (output partition_graphs=True)
result = sess . run (negMatrix, options=options, run_metadata=metadata) \& _ Prints the resulting value
This code outputs info about which CPU/GPU devices are used in the session for each operation. Running listing $2.7$ results in traces of output like the following to show which device was used to run the negation op:
Sessions are essential in TensorFlow code. You need to call a session to “run” the math. Figure $2.4$ maps out how the components on TensorFlow interact with the machinelearning pipeline. A session not only runs a graph operation, but also can take placeholders, variables, and constants as input. We’ve used constants so far, but in later sections, we’ll start using variables and placeholders. Here’s a quick overview of these three types of values:

• Placeholder-A value that’s unassigned but will be initialized by the session wherever it’s run. Typically, placeholders are the input and output of your model.
• Variable-A value that can change, such as parameters of a machine-learning model. Variables must be initialized by the session before they’re used.
• Constant-A value that doesn’t change, such as a hyperparameter or setting.
The entire pipeline for machine learning with TensorFlow follows the flow of figure 2.4. Most of the code in TensorFlow consists of setting up the graph and session. After you design a graph and hook up the session to execute it, your code is ready to use.

## 机器学习代写|tensorflow代写|Writing code in Jupyter

Because TensorFlow is primarily a Python library, you should make full use of Python’s interpreter. Jupyter is a mature environment for exercising the interactive nature of the language. It’s a web application that displays computation elegantly so that you can share annotated interactive algorithms with others to teach a technique or demonstrate code. Jupyter also easily integrates with visualization libraries like Python’s Matplotlib and can be used to share elegant data stories about your algorithm, to evaluate its accuracy, and to present results.

You can share your Jupyter notebooks with other people to exchange ideas, and you can download their notebooks to learn about their code. See the appendix to get started installing the Jupyter Notebook application.

From a new terminal, change the directory to the location where you want to practice TensorFlow code, and start a notebook server:
\$cd -/MyTensorFlowStuff$\$$jupyter notebook Running this command should launch a new browser window with the Jupyter notebook’s dashboard. If no window opens automatically, you can navigate to http://localhost:8888 from any browser. You’ll see a web page similar to the one in figure 2.5. TIP The jupyter notebook command didn’t work? Make sure that your PYTHONPATH environment variable includes the path to the jupyter script created when you installed the library. Also, this book uses both Python 3.7 (recommended) and Python 2.7 examples (due to the BregmanToolkit, which you’ll read about in chapter 7). For this reason, you will want to install Jupyter with Python kernels enabled. For more information, see https://ipython readthedocs.io/en/stable/install/kernel_install.html. ## tensorflow代考 ## 机器学习代写|tensorflow代写|Understanding code as a graph 考虑一位医生预测新生儿的预期体重为7.5磅。您想弄清楚该预测与实际测量的重量有何不同。作为一名过度分析的工程师，您设计了一个函数来描述新生儿所有可能体重的可能性。例如，8 磅的重量比 10 磅更有可能。 您可以选择使用高斯（也称为正态）概率分布函数。此函数将一个数字作为输入，并输出一个描述观察输入的概率的非负数。这个函数在机器学习中一直出现，并且在 TensorFlow 中很容易定义。它使用乘法、除法、否定和其他一些基本运算符。 将每个运算符视为图中的一个节点。每当您看到加号 (+) 或任何数学概念时，将其想象为许多节点之一。这些节点之间的边代表数学函数的组成。具体来说，我们一直在研究的负算子是一个节点，这个节点的入/出边就是 Tensor 的变换方式。一个张量流过图，这就是为什么这个库被称为 TensorFlow。 这里有一个想法：每个运算符都是一个强类型函数，它接受一个维度的输入张量并产生相同维度的输出。数字2.3是如何使用 TensorFlow 设计高斯函数的示例。该函数表示为一个图，其中算子是节点，边表示节点之间的交互。该图作为一个整体表示一个复杂的数学函数（特别是高斯函数）。图中的小段代表简单的数学概念，例如否定和加倍。 TensorFlow 算法易于可视化。它们可以简单地用流程图来描述。这种流程图的技术（更正确）术语是数据流图。数据流图中的每个箭头都称为边。此外，数据流图的每个状态都称为一个节点。会话的目的是将您的 Python 代码解释为数据流图，然后将图的每个节点的计算与 CPU 或 GPU 相关联。 ## 机器学习代写|tensorflow代写|Setting session configurations 您还可以将选项传递给 tf.Session。TensorFlow 会自动确定将 GPU 或 CPU 设备分配给操作的最佳方式，例如，取决于可用的设备。您可以在创建会话时传递一个附加选项 log_device_placement=True。清单2.7向您准确显示在硬件上引发计算的位置。 将 tensorflow 导入为 tf options = tf.RunOptions (output partition_graphs=True) metadata = tf.RunMetadata () result = sess 。run (negMatrix, options=options, run_metadata=metadata) \& _ 打印结果值 print (result) print (metadata.partition_graphs) 此代码输出有关每个操作的会话中使用哪些 CPU/GPU 设备的信息。运行列表2.7产生如下输出痕迹，以显示用于运行否定操作的设备： Neg:/job:localhost/replica:0/task:0/cpu:0 会话在 TensorFlow 代码中是必不可少的。你需要调用一个会话来“运行”数学。数字2.4绘制出 TensorFlow 上的组件如何与机器学习管道交互。会话不仅运行图形操作，还可以将占位符、变量和常量作为输入。到目前为止，我们已经使用了常量，但在后面的部分中，我们将开始使用变量和占位符。以下是这三种类型值的快速概览： • 占位符 – 一个未分配的值，但无论在何处运行，会话都会对其进行初始化。通常，占位符是模型的输入和输出。 • 变量 – 可以更改的值，例如机器学习模型的参数。变量在使用之前必须由会话初始化。 • 常量 – 不会改变的值，例如超参数或设置。 使用 TensorFlow 进行机器学习的整个管道遵循图 2.4 的流程。TensorFlow 中的大部分代码都包括设置图和会话。设计图表并连接会话以执行它之后，您的代码就可以使用了。 ## 机器学习代写|tensorflow代写|Writing code in Jupyter 因为 TensorFlow 主要是一个 Python 库，所以你应该充分利用 Python 的解释器。Jupyter 是一个成熟的环境，用于锻炼语言的交互性。它是一个优雅地显示计算的 Web 应用程序，因此您可以与他人共享带注释的交互式算法来教授技术或演示代码。Jupyter 还可以轻松地与 Python 的 Matplotlib 等可视化库集成，并可用于分享有关您的算法的优雅数据故事、评估其准确性并呈现结果。 您可以与其他人共享您的 Jupyter 笔记本以交流想法，也可以下载他们的笔记本以了解他们的代码。请参阅附录以开始安装 Jupyter Notebook 应用程序。 在新终端中，将目录更改为您要练习 TensorFlow 代码的位置，然后启动笔记本服务器：  cd -/MyTensorFlowStuff jupyter notebook 运行这个命令应该会启动一个带有 Jupyter notebook 仪表板的新浏览器窗口。如果没有自动打开窗口，您可以从任何浏览器导航到 http://localhost:8888。你会看到一个类似于图中的网页2.5. 提示 jupyter notebook 命令不起作用？确保您的 PYTHONPATH 环境变量包含安装库时创建的 jupyter 脚本的路径。此外，本书同时使用 Python3.7（推荐）和 Python2.7示例（由于 BregmanToolkit，您将在第 7 章中了解它）。出于这个原因，您需要在启用 Python 内核的情况下安装 Jupyter。有关更多信息，请参阅 https://ipython readthedocs.io/en/stable/install/kernel_install.html。 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。 ## 机器学习代写|tensorflow代写|TensorFlow essentials 如果你也在 怎样代写tensorflow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。 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。 statistics-lab™ 为您的留学生涯保驾护航 在代写tensorflow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tensorflow代写方面经验极为丰富，各种代写tensorflow相关的作业也就用不着说。 我们提供的tensorflow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 ## 机器学习代写|tensorflow代写|Representing tensors Now that you know how to import TensorFlow into a Python source file, let’s start using it! As discussed in chapter 1, a convenient way to describe an object in the real world is to list its properties or features. You can describe a car, for example, by its color, model, engine type, mileage, and so on. An ordered list of features is called a feature vector, and that’s exactly what you’ll represent in TensorFlow code. Feature vectors are among the most useful devices in machine learning because of their simplicity; they’re lists of numbers. Each data item typically consists of a feature vector, and a good dataset has hundreds, if not thousands, of feature vectors. No doubt you’ll often deal with more than one vector at a time. A matrix concisely represents a list of vectors, in which each column of a matrix is a feature vector. The syntax to represent matrices in TensorFlow is a vector of vectors, all of the same length. Figure 2.1 is an example of a matrix with two rows and three columns, such as [[1,2,3],[4,5,6]]. Notice that this vector contains two elements and that each element corresponds to a row of the matrix. The first variable (\mathrm{m} 1) is a list, the second variable (\mathrm{m} 2) is an ndarray from the NumPy library, and the last variable (\mathrm{m} 3) is TensorFlow’s constant Tensor object, which you initialize by using t f. constant. None of the three ways to specify a matrix is necessarily better than any another, but each way does give you a raw set of list values (m1), a typed NumPy object (m2), or an initialized data flow operation: a tensor (m3). All operators in TensorFlow, such as negative, are designed to operate on tensor objects. A convenient function you can sprinkle anywhere to make sure that you’re dealing with tensors as opposed to the other types is tf.convert_to_tensor (\ldots). Most functions in the TensorFlow library already perform this function (redundantly), even if you forget to do so. Using tf.convert_to_tensor (…) is optional, but we show it here because it helps demystify the implicit type system being handled across the library and overall as part of the Python programming language. Listing 2.3 outputs the following three times:TIP To make copying and pasting easier, you can find the code listings on the book’s GitHub site: https:// github.com/chrismattmann/MLwithTensorFlow2ed. You will also find a fully functional Docker image that you can use with all the data, and code and libraries to run the examples in the book. Install it, using docker pull chrismattmann/mitf2, and see the appendix for more details. Let’s take another look at defining tensors in code. After importing the TensorFlow library, you can use the t f. constant operator as follows. Listing 2.3 shows a couple of tensors of various dimensions. ## 机器学习代写|tensorflow代写|Creating operators Now that you have a few starting tensors ready to be used, you can apply more interesting operators, such as addition and multiplication. Consider each row of a matrix representing the transaction of money to (positive value) and from (negative value) another person. Negating the matrix is a way to represent the transaction history of the other person’s flow of money. Let’s start simple and run a negation op (short for operation) on the \mathrm{m} 1 tensor from listing 2.3. Negating a matrix turns the positive numbers into negative numbers of the same magnitude, and vice versa. Negation is one of the simplest operations. As shown in listing 2.4, negation takes only one tensor as input and produces a tensor with every element negated. Try running the code. If you master defining negation, you can generalize that skill for use in all other TensorFlow operations. NOTE Defining an operation, such as negation, is different from running it. So far, you’ve defined how operations should behave. In section 2.4, you’ll evaluate (or run) them to compute their values. ## 机器学习代写|tensorflow代写|Executing operators within sessions A session is an environment of a software system that describes how the lines of code should run. In TensorFlow, a session sets up how the hardware devices (such as CPU and GPU) talk to one another. That way, you can design your machine-learning algorithm without worrying about micromanaging the hardware on which it runs. Later, you can configure the session to change its behavior without changing a line of the machine-learning code. To execute an operation and retrieve its calculated value, TensorFlow requires a session. Only a registered session may fill the values of a Tensor object. To do so, you must create a session class by using t f. Session () and tell it to run an operator, as shown in listing 2.5. The result will be a value you can use for further computations later.Congratulations! You’ve written your first full TensorFlow code. Although all that this code does is negate a matrix to produce [[-1,-2]], the core overhead and framework are the same as everything else in TensorFlow. A session not only configures where your code will be computed on your machine, but also crafts how the computation will be laid out to parallelize computation. Every Tensor object has an eval () function to evaluate the mathematical operations that define its value. But the eval () function requires defining a session object for the library to understand how to best use the underlying hardware. In listing 2.5, we used sess.run (…), which is equivalent to invoking the Tensor’s eval () function in the context of the session. When you’re running TensorFlow code through an interactive environment (for debugging or presentation purposes or when using Jupyter, as described later in the chapter), it’s often easier to create the session in interactive mode, in which the session is implicitly part of any call to eval (). That way, the session variable doesn’t need to be passed around throughout the code, making it easier to focus on the relevant parts of the algorithm, as shown in listing 2.6. ## tensorflow代考 ## 机器学习代写|tensorflow代写|Representing tensors 现在您已经知道如何将 TensorFlow 导入 Python 源文件，让我们开始使用它吧！如第 1 章所述，描述现实世界中对象的一种方便方法是列出其属性或特征。例如，您可以通过颜色、型号、发动机类型、里程等来描述汽车。特征的有序列表称为特征向量，这正是您将在 TensorFlow 代码中表示的内容。 特征向量是机器学习中最有用的设备之一，因为它们很简单；它们是数字列表。每个数据项通常由一个特征向量组成，一个好的数据集有成百上千个特征向量。毫无疑问，您经常会一次处理多个向量。矩阵简洁地表示向量列表，其中矩阵的每一列都是一个特征向量。 在 TensorFlow 中表示矩阵的语法是向量的向量，所有向量的长度都相同。数字2.1是具有两行三列的矩阵的示例，例如[[1,2,3],[4,5,6]]. 请注意，此向量包含两个元素，并且每个元素对应于矩阵的一行。 第一个变量(米1)是一个列表，第二个变量(米2)是 NumPy 库中的一个 ndarray，最后一个变量(米3)是 TensorFlow 的常量 Tensor 对象，您可以使用它来初始化它吨F. 持续的。指定矩阵的三种方法都不一定比另一种更好，但每种方法都为您提供了一组原始列表值 (m1)、一个类型化的 NumPy 对象 (m2) 或一个初始化的数据流操作：张量 (立方米）。 TensorFlow 中的所有算子，例如负数，都是为了对张量对象进行操作而设计的。tf.convert_to_tensor 是一个方便的函数，您可以在任何地方使用以确保您处理的是张量而不是其他类型(…). TensorFlow 库中的大多数函数已经（冗余地）执行此函数，即使您忘记这样做。使用 tf.convert_to_tensor (…) 是可选的，但我们在这里展示它是因为它有助于揭开整个库以及作为 Python 编程语言的一部分处理的隐式类型系统的神秘面纱。清单2.3输出如下 3 次：TIP 为了让复制和粘贴更容易，你可以在本书的 GitHub 网站上找到代码清单：https://github.com/chrismatmann/MLwithTensorFlow2ed。您还将找到一个功能齐全的 Docker 镜像，您可以使用它与所有数据、代码和库一起运行本书中的示例。使用 docker pull chrismatmann/mitf2 安装它，有关详细信息，请参阅附录。 让我们再看一下在代码中定义张量。导入 TensorFlow 库后，您可以使用吨F. 常量运算符如下。清单2.3显示了几个不同维度的张量。 ## 机器学习代写|tensorflow代写|Creating operators 现在您已经准备好使用一些起始张量，您可以应用更多有趣的运算符，例如加法和乘法。考虑一个矩阵的每一行，表示货币与（正值）和来自（负值）另一个人的交易。对矩阵求反是表示对方资金流向的交易历史的一种方式。让我们从简单开始，在 米1列表中的张量2.3. 对矩阵求反会将正数转换为相同大小的负数，反之亦然。 求反是最简单的操作之一。如清单 2.4 所示，否定只接受一个张量作为输入，并产生一个每个元素都取反的张量。尝试运行代码。如果您掌握了定义否定，您可以推广该技能以用于所有其他 TensorFlow 操作。 注意 定义一个操作，例如否定，不同于运行它。到目前为止，您已经定义了操作的行为方式。在 2.4 节中，您将评估（或运行）它们以计算它们的值。 ## 机器学习代写|tensorflow代写|Executing operators within sessions 会话是描述代码行应该如何运行的软件系统环境。在 TensorFlow 中，会话设置硬件设备（例如 CPU 和 GPU）如何相互通信。这样，您就可以设计您的机器学习算法，而不必担心对其运行的硬件进行微观管理。稍后，您可以配置会话以更改其行为，而无需更改机器学习代码行。 要执行操作并检索其计算值，TensorFlow 需要一个会话。只有注册的会话可以填充张量对象的值。为此，您必须使用创建会话类吨F. Session() 并告诉它运行一个操作符，如清单所示2.5. 结果将是您以后可以用于进一步计算的值。恭喜！您已经编写了第一个完整的 TensorFlow 代码。尽管这段代码所做的只是对要产生的矩阵求反[[−1,−2]]，核心开销和框架与 TensorFlow 中的其他所有内容相同。会话不仅配置您的代码将在您的机器上计算的位置，而且还精心设计计算将如何布局以并行化计算。 每个张量对象都有一个 eval () 函数来评估定义其值的数学运算。但是 eval() 函数需要为库定义一个会话对象，以了解如何最好地使用底层硬件。在上市2.5，我们使用了sess.run(…)，相当于在session的上下文中调用了Tensor的eval()函数。 当您通过交互式环境运行 TensorFlow 代码时（出于调试或演示目的或使用 Jupyter，如本章后面所述），在交互模式下创建会话通常更容易，其中会话隐含地是任何调用 eval()。这样，会话变量就不需要在整个代码中传递，从而更容易专注于算法的相关部分，如清单 2.6 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 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。 ## 机器学习代写|tensorflow代写| TensorFlow 如果你也在 怎样代写tensorflow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。 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。 statistics-lab™ 为您的留学生涯保驾护航 在代写tensorflow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tensorflow代写方面经验极为丰富，各种代写tensorflow相关的作业也就用不着说。 我们提供的tensorflow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 ## 机器学习代写|tensorflow代写|TensorFlow Google open-sourced its machine-learning framework, TensorFlow, in late 2015 under the Apache 2.0 license. Before that, it was used proprietarily by Google in speech recognition, Search, Photos, and Gmail, among other applications. The library is implemented in \mathrm{C}++ and has a convenient Python API, as well as a lessappreciated C++ API. Because of the simpler dependencies, TensorFlow can be quickly deployed to various architectures. Similar to Theano-a popular numerical computation library for Python that you may be familiar with (http:// deeplearning.net/software/theano)-computations are described as flowcharts, separating design from implementation. With little to no hassle, this dichotomy allows the same design to be implemented on mobile devices as well as large-scale training systems with thousands of processors. The single system spans a broad range of platforms. TensorFlow also plays nicely with a variety of newer, similarly-developed ML libraries, including Keras (TensorFlow 2.0 is fully integrated with Keras), along with libraries such as PyTorch (https://pytorch.org), originally developed by Facebook, and richer application programming interfaces for ML such as Fast.Ai. You can use many toolkits to do ML, but you’re reading a book about TensorFlow, right? Let’s focus on it! One of the fanciest properties of TensorFlow is its automatic differentiation capabilities. You can experiment with new networks without having to redefine many key calculations. NOTE Automatic differentiation makes it much easier to implement backpropagation, which is a computationally-heavy calculation used in a branch of machine learning called neural networks. TensorFlow hides the nitty-gritty details of backpropagation so you can focus on the bigger picture. Chapter 11 covers an introduction to neural networks with TensorFlow. All the mathematics is abstracted away and unfolded under the hood. Using TensorFlow is like using WolframAlpha for a calculus problem set. Another feature of this library is its interactive visualization environment, called TensorBoard. This tool shows a flowchart of the way data transforms, displays summary logs over time, and traces performance. Figure 1.11 shows what TensorBoard looks like; chapter 2 covers using it. ## 机器学习代写|tensorflow代写|Overview of future chapters Chapter 2 demonstrates how to use various components of TensorFlow (see figure 1.12). Chapters 3-10 show how to implement classic machine-learning algorithms in TensorFlow, and chapters 11-19 cover algorithms based on neural networks. The algorithms solve a wide variety of problems, such as prediction, classification, clustering, dimensionality reduction, and planning. • TensorFlow has become the tool of choice among professionals and researchers for implementing machine-learning solutions. • Machine learning uses examples to develop an expert system that can make useful statements about new inputs.A key property of ML is that performance tends to improve with more training data. • Over the years, scholars have crafted three major archetypes that most problems fit: supervised learning, unsupervised learning, and reinforcement learning. Meta-learning is a new area of ML that focuses on exploring the entire space of models, solutions, and tuning tricks automatically. • After a real-world problem is formulated in a machine-learning perspective, several algorithms become available. Of the many software libraries and frameworks that can accomplish an implementation, we chose TensorFlow as our silver bullet. Developed by Google and supported by its flourishing community, TensorFlow gives us a way to implement industry-standard code easily. ## 机器学习代写|tensorflow代写|Computing the inner product That’s too much code to calculate the inner product of two vectors (also known as the dot product). Imagine how much code would be required for something more complicated, such as solving linear equations or computing the distance between two vectors, if you still lacked TensorFlow and its friends, like the Numerical Python (NumPy) library. When installing the TensorFlow library, you also install a well-known, robust Python library called NumPy, which facilitates mathematical manipulation in Python. Using Python without its libraries (NumPy and TensorFlow) is like using a camera without an autofocus mode; sure, you gain more flexibility, but you can easily make careless mistakes. (For the record, we have nothing against photographers who micromanage aperture, shutter, and ISO-the so-called “manual” knobs used to prepare your camera to take an image.) It’s easy to make mistakes in machine learning, so let’s keep our camera on autofocus and use TensorFlow to help automate tedious software development. The following code snippet shows how to write the same inner product concisely using NumPy: import numpy as np revenue = np. dot (prices, amounts) Python is a succinct language. Fortunately for you, this book doesn’t have pages and pages of cryptic code. On the other hand, the brevity of the Python language also implies that a lot is happening behind each line of code, which you should study carefully as you follow along in this chapter. You will find that this is a core theme for TensorFlow, something that it balances elegantly as an add-on library to Python. TensorFlow hides enough of the complexity (like autofocus) but also allows you to turn those magical configurable knobs when you want to get your hands dirty. Machine-learning algorithms require many mathematical operations. Often, an algorithm boils down to a composition of simple functions iterated until convergence. Sure, you may use any standard programming language to perform these computations, but the secret to both manageable and high-performing code is the use of a well-written library, such as TensorFlow (which officially supports Python, C++, JavaScript, Go, and Swift). ## tensorflow代考 ## 机器学习代写|tensorflow代写|TensorFlow 谷歌于 2015 年底在 Apache 下开源了其机器学习框架 TensorFlow2.0执照。在此之前，它被谷歌专有用于语音识别、搜索、照片和 Gmail 等应用程序中。 该库在C++并且有一个方便的 Python API，以及一个鲜为人知的 C++ API。由于更简单的依赖关系，TensorFlow 可以快速部署到各种架构中。 与 Theano 类似——你可能熟悉的一个流行的 Python 数值计算库 (http://deeplearning.net/software/theano)——计算被描述为流程图，将设计与实现分开。毫不费力地，这种二分法允许在移动设备以及具有数千个处理器的大规模培训系统上实施相同的设计。单一系统跨越广泛的平台。TensorFlow 还可以很好地与各种更新的、类似开发的 ML 库配合使用，包括 Keras（TensorFlow2.0完全集成 与 Keras），以及最初由 Facebook 开发的 PyTorch (https://pytorch.org) 等库，以及用于 ML 的更丰富的应用程序编程接口，例如 Fast.Ai。您可以使用许多工具包来进行 ML，但您正在阅读一本关于 TensorFlow 的书，对吧？让我们专注于它！ TensorFlow 最奇特的特性之一是其自动微分功能。您可以试验新网络，而无需重新定义许多关键计算。 注意 自动微分使反向传播的实现变得更加容易，反向传播是一种计算量很大的计算，用于称为神经网络的机器学习分支。TensorFlow 隐藏了反向传播的细节，因此您可以专注于更大的图景。第 11 章介绍了使用 TensorFlow 的神经网络。 所有的数学都被抽象出来并在引擎盖下展开。使用 TensorFlow 就像使用 WolframAlpha 来处理微积分问题集。 这个库的另一个特点是它的交互式可视化环境，称为 TensorBoard。此工具显示数据转换方式的流程图，显示随时间推移的摘要日志以及跟踪性能。数字1.11显示 TensorBoard 的外观；第 2 章介绍了如何使用它。 ## 机器学习代写|tensorflow代写|Overview of future chapters 第 2 章演示了如何使用 TensorFlow 的各种组件（见图 1.12）。章节3−10展示如何在 TensorFlow 中实现经典的机器学习算法，第 11-19 章介绍了基于神经网络的算法。这些算法解决了各种各样的问题，例如预测、分类、聚类、降维和规划。 • TensorFlow 已成为专业人士和研究人员实施机器学习解决方案的首选工具。 • 机器学习使用示例来开发一个专家系统，该系统可以对新输入做出有用的陈述。ML 的一个关键特性是，随着训练数据的增加，性能往往会提高。 • 多年来，学者们精心设计了适合大多数问题的三种主要原型：监督学习、无监督学习和强化学习。元学习是 ML 的一个新领域，专注于自动探索模型、解决方案和调整技巧的整个空间。 • 从机器学习的角度阐述现实世界的问题后，可以使用几种算法。在可以完成实现的众多软件库和框架中，我们选择了 TensorFlow 作为我们的灵丹妙药。TensorFlow 由 Google 开发并得到其蓬勃发展的社区的支持，为我们提供了一种轻松实现行业标准代码的方法。 ## 机器学习代写|tensorflow代写|Computing the inner product 计算两个向量的内积（也称为点积）的代码太多。想象一下，如果您仍然缺少 TensorFlow 及其朋友，例如 Numerical Python (NumPy) 库，那么更复杂的事情将需要多少代码，例如求解线性方程或计算两个向量之间的距离。 在安装 TensorFlow 库时，您还安装了一个名为 NumPy 的知名、强大的 Python 库，它有助于在 Python 中进行数学运算。使用没有库（NumPy 和 TensorFlow）的 Python 就像使用没有自动对焦模式的相机一样；当然，你获得了更多的灵活性，但你很容易犯粗心的错误。（作为记录，我们并不反对对光圈、快门和 ISO 进行微观管理的摄影师——所谓的“手动”旋钮，用于准备你的相机拍摄图像。）机器学习很容易出错，所以让我们继续我们的相机自动对焦，并使用 TensorFlow 来帮助自动化繁琐的软件开发。 以下代码片段展示了如何使用 NumPy 简洁地编写相同的内积： import numpy as np 收入 = np。dot (prices, amount) Python 是一门简洁的语言。对你来说幸运的是，这本书没有一页又一页的神秘代码。另一方面，Python 语言的简洁性也意味着每一行代码背后都发生了很多事情，在本章中你应该仔细研究这些内容。你会发现这是 TensorFlow 的核心主题，它作为 Python 的附加库优雅地平衡了一些东西。TensorFlow 隐藏了足够多的复杂性（如自动对焦），但还允许您在想弄脏双手时转动那些神奇的可配置旋钮。 机器学习算法需要许多数学运算。通常，算法归结为简单函数的组合，迭代直到收敛。当然，您可以使用任何标准的编程语言来执行这些计算，但可管理和高性能代码的秘诀在于使用编写良好的库，例如 TensorFlow（它正式支持 Python、C++、JavaScript、Go、和斯威夫特）。 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。 ## 机器学习代写|tensorflow代写|Unsupervised learning 如果你也在 怎样代写tensorflow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。 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。 statistics-lab™ 为您的留学生涯保驾护航 在代写tensorflow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tensorflow代写方面经验极为丰富，各种代写tensorflow相关的作业也就用不着说。 我们提供的tensorflow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 ## 机器学习代写|tensorflow代写|Unsupervised learning Unsupervised learning is about modeling data that comes without corresponding labels or responses. The fact that we can make any conclusions at all on raw data feels like magic. With enough data, it may be possible to find patterns and structure. Two of the most powerful tools that machine-learning practitioners use to learn from data alone are clustering and dimensionality reduction. Clustering is the process of splitting the data into individual buckets of similar items. In a sense, clustering is like classifying data without knowing any corresponding labels. When organizing your books on three shelves, for example, you likely place similar genres together, or maybe you group them by the authors’ last names. You might have a Stephen King section, another for textbooks, and a third for anything else. You don’t care that all the books are separated by the same feature, only that each book has something unique that allows you to organize it into one of several roughly equal, easily identifiable groups. One of the most popular clustering algorithms is k-means, which is a specific instance of a more powerful technique called the E-M algorithm. Dimensionality reduction is about manipulating the data to view it from a much simpler perspective-the ML equivalent of the phrase “Keep it simple, stupid.” By getting rid of redundant features, for example, we can explain the same data in a lowerdimensional space and see which features matter. This simplification also helps in data visualization or preprocessing for performance efficiency. One of the earliest algorithms is principle component analysis (PCA), and a newer one is autoencoders, which are covered in chapter 7 . ## 机器学习代写|tensorflow代写|Reinforcement learning Supervised and unsupervised learning seem to suggest that the existence of a teacher is all or nothing. But in one well-studied branch of machine learning, the environment acts as a teacher, providing hints as opposed to definite answers. The learning system receives feedback on its actions, with no concrete promise that it’s progressing in the right direction, which might be to solve a maze or accomplish an explicit goal. Unlike supervised learning, in which training data is conveniently labeled by a “teacher,” reinforcement learning trains on information gathered by observing how the environment reacts to actions. Reinforcement learning is a type of machine learning that interacts with the environment to learn which combination of actions yields the most favorable results. Because we’re already anthropomorphizing algorithms by using the words environment and action, scholars typically refer to the system as an autonomous agent. Therefore, this type of machine learning naturally manifests itself in the domain of robotics. To reason about agents in the environment, we introduce two new concepts: states and actions. The status of the world frozen at a particular time is called a state. An agent may perform one of many actions to change the current state. To drive an agent to perform actions, each state yields a corresponding reward. An agent eventually discovers the expected total reward of each state, called the value of a state. Like any other machine-learning system, performance improves with more data. In this case, the data is a history of experiences. In reinforcement learning, we don’t know the final cost or reward of a series of actions until that series is executed. These situations render traditional supervised learning ineffective, because we don’t know exactly which action in the history of action sequences is to blame for ending up in a low-value state. The only information an agent knows for certain is the cost of a series of actions that it has already taken, which is incomplete. The agent’s goal is to find a sequence of actions that maximizes rewards. If you’re more interested in this subject, you may want to check out another topical book in the Manning Publications family: Grokking Deep Reinforcement Learning, by Miguel Morales (Manning, 2020; https://www .manning.com/books/grokking-deep-reinforcement-learning). ## 机器学习代写|tensorflow代写|Meta-learning Relatively recently, a new area of machine learning called meta-learning has emerged. The idea is simple. Data scientists and ML experts spend a tremendous amount of time executing the steps of ML, as shown in figure 1.7. What if those steps-defining and representing the problem, choosing a model, testing the model, and evaluating the model-could themselves be automated? Instead of being limited to exploring only one or a small group of models, why not have the program itself try all the models? Many businesses separate the roles of the domain expert (refer to the doctor in figure 1.7), the data scientist (the person modeling the data and potentially extracting or choosing features that are important, such as the image RGB pixels), and the ML engineer (responsible for tuning, testing, and deploying the model), as shown in figure 1.10 \mathrm{a}. As you’ll remember from earlier in the chapter, these roles interact in three basic areas: data cleaning and prep, which both the domain expert and data scientist may help with; feature and model selection, mainly a data-scientist job with a little help from the ML engineer; and then train, test, and evaluate, mostly the job of the ML engineer with a little help from the data scientist. We’ve added a new wrinkle: taking our model and deploying it, which is what happens in the real world and is something that brings its own set of challenges. This scenario is one reason why you are reading the second edition of this book; it’s covered in chapter 2, where I discuss deploying and using TensorFlow. What if instead of having data scientists and ML engineers pick models, train, evaluate, and tune them, we could have the system automatically search over the space of possible models, and try them all? This approach overcomes limiting your overall ML experience to a small number of possible solutions wherein you’ll likely choose the first one that performs reasonably. But what if the system could figure out which models are best and how to tune the models automatically? That’s precisely what you see in figure 1.10 \mathrm{~b} : the process of meta-learning, or AutoML. ## tensorflow代考 ## 机器学习代写|tensorflow代写|Unsupervised learning 无监督学习是关于对没有相应标签或响应的数据进行建模。我们可以根据原始数据得出任何结论的事实感觉就像魔术一样。有了足够的数据，就有可能找到模式和结构。机器学习从业者用来从数据中学习的两个最强大的工具是聚类和降维。 聚类是将数据拆分为相似项目的单个存储桶的过程。从某种意义上说，聚类就像在不知道任何相应标签的情况下对数据进行分类。例如，在三个书架上组织您的书籍时，您可能会将类似的类型放在一起，或者您可能按作者的姓氏对它们进行分组。您可能有一个斯蒂芬金部分，另一个用于教科书，第三个用于其他任何内容。您并不关心所有书籍都由相同的功能分隔，只是每本书都有独特的东西，可以让您将其组织成几个大致相等且易于识别的组之一。最流行的聚类算法之一是 k-means，它是一种称为 EM 算法的更强大技术的特定实例。 降维是关于操纵数据以从更简单的角度查看数据 – ML 相当于短语“保持简单，愚蠢”。例如，通过去除冗余特征，我们可以在低维空间中解释相同的数据，看看哪些特征很重要。这种简化还有助于数据可视化或预处理以提高性能。最早的算法之一是主成分分析（PCA），一种较新的算法是自动编码器，它们将在第 7 章中介绍。 ## 机器学习代写|tensorflow代写|Reinforcement learning 有监督和无监督学习似乎表明老师的存在是全部或全部。但在机器学习的一个经过充分研究的分支中，环境就像老师一样，提供提示而不是明确的答案。学习系统会收到对其行为的反馈，但没有具体承诺它会朝着正确的方向前进，这可能是解决迷宫或实现明确的目标。 与监督学习不同，其中训练数据由“老师”方便地标记，强化学习通过观察环境对行动的反应来训练收集的信息。强化学习是一种机器学习，它与环境交互以了解哪种动作组合产生最有利的结果。因为我们已经通过使用环境和动作这两个词来拟人化算法，所以学者们通常将系统称为自主代理。因此，这种类型的机器学习自然而然地表现在机器人领域。 为了推理环境中的代理，我们引入了两个新概念：状态和动作。在特定时间冻结的世界的状态称为状态。代理可以执行许多操作之一来更改当前状态。为了驱动代理执行动作，每个状态都会产生相应的奖励。代理最终会发现每个状态的预期总奖励，称为状态的价值。 与任何其他机器学习系统一样，性能会随着数据的增加而提高。在这种情况下，数据是经验的历史。在强化学习中，我们不知道一系列动作的最终成本或回报，直到该系列被执行。这些情况使传统的监督学习变得无效，因为我们不确切知道动作序列历史中的哪个动作应该归咎于最终处于低价值状态。代理人唯一确定的信息是它已经采取的一系列行动的成本，这是不完整的。智能体的目标是找到使奖励最大化的一系列动作。如果您对这个主题更感兴趣，您可能需要查看 Manning Publications 系列中的另一本专题书籍：Grokking Deep Reinforcement Learning，作者为 Miguel Morales（Manning，2020；https://www . ## 机器学习代写|tensorflow代写|Meta-learning 最近，出现了一个称为元学习的新机器学习领域。这个想法很简单。数据科学家和机器学习专家花费大量时间执行机器学习的步骤，如图 1.7 所示。如果这些步骤（定义和表示问题、选择模型、测试模型和评估模型）本身可以自动化呢？为什么不让程序本身尝试所有模型，而不是仅限于探索一个或一小组模型？ 许多企业将领域专家（参见图 1.7 中的医生）、数据科学家（对数据进行建模并可能提取或选择重要特征，例如图像 RGB 像素）和 ML 工程师的角色分开（负责调优、测试和部署模型），如图 1.10\mathrm{a} 所示。正如您将在本章前面所记得的那样，这些角色在三个基本领域相互作用：数据清理和准备，领域专家和数据科学家都可以提供帮助；特征和模型选择，主要是数据科学家的工作，在 ML 工程师的帮助下；然后在数据科学家的帮助下训练、测试和评估，主要是 ML 工程师的工作。我们增加了一个新问题：采用我们的模型并部署它，这就是现实世界中发生的事情，并且会带来一系列挑战。这种情况是您阅读本书第二版的原因之一。它在第 2 章中进行了介绍，在那里我讨论了部署和使用 TensorFlow。1.10a。正如您将在本章前面所记得的那样，这些角色在三个基本领域相互作用：数据清理和准备，领域专家和数据科学家都可以提供帮助；特征和模型选择，主要是数据科学家的工作，在 ML 工程师的帮助下；然后在数据科学家的帮助下训练、测试和评估，主要是 ML 工程师的工作。我们增加了一个新问题：采用我们的模型并进行部署，这就是现实世界中发生的事情，也带来了一系列挑战。这种情况是您阅读本书第二版的原因之一。它在第 2 章中进行了介绍，在那里我讨论了部署和使用 TensorFlow。 如果不是让数据科学家和 ML 工程师挑选、训练、评估和调整模型，而是让系统自动搜索可能模型的空间，然后全部尝试，会怎样？这种方法克服了将您的整体 ML 体验限制为少数可能的解决方案的问题，在这些解决方案中您可能会选择第一个执行合理的解决方案。但是，如果系统能够找出最好的模型以及如何自动调整模型呢？这正是您在图 1.10 \mathrm{~b} 中看到的：元学习或 AutoML 的过程。1.10 b：元学习或 AutoML 的过程。 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。 ## 机器学习代写|tensorflow代写|Data representation and features 如果你也在 怎样代写tensorflow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。 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。 statistics-lab™ 为您的留学生涯保驾护航 在代写tensorflow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tensorflow代写方面经验极为丰富，各种代写tensorflow相关的作业也就用不着说。 我们提供的tensorflow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 ## 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

## 有限元方法代写

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。

## 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。

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。

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

• 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|A machine-learning odyssey

Have you ever wondered whether there are limits to what computer programs can solve? Nowadays, computers appear to do a lot more than unravel mathematical equations. In the past half-century, programming has become the ultimate tool for automating tasks and saving time. But how much can we automate, and how do we go about doing so?

Can a computer observe a photograph and say, “Aha-I see a lovely couple walking over a bridge under an umbrella in the rain”? Can software make medical decisions as accurately as trained professionals can? Can software make better predictions about stock market performance than humans could? The achievements of the past decade hint that the answer to all these questions is a resounding yes and that the implementations appear to have a common strategy.

Recent theoretical advances coupled with newly available technologies have enabled anyone with access to a computer to attempt their own approach to solving these incredibly hard problems. (Okay, not just anyone, but that’s why you’re reading this book, right?)

A programmer no longer needs to know the intricate details of a problem to solve it. Consider converting speech to text. A traditional approach may involve understanding the biological structure of human vocal cords to decipher utterances by using many hand-designed, domain-specific, ungeneralizable pieces of code. Nowadays, it’s possible to write code that looks at many examples and figures out how to solve the problem, given enough time and examples.

Take another example: identifying the sentiment of text in a book or a tweet as positive or negative. Or you may want to identify the text even more granularly, such as text that implies the writer’s likes or loves, things that they hate or is angry or sad about. Past approaches to performing this task were limited to scanning the text in question, looking for harsh words such as ugly, stupid, and miserable to indicate anger or sadness, or punctuation such as exclamation marks, which could mean happy or angry but not exactly in-between.

Algorithms learn from data, similar to the way that humans learn from experience. Humans learn by reading books, observing situations, studying in school, exchanging conversations, and browsing websites, among other means. How can a machine possibly develop a brain capable of learning? There’s no definitive answer, but world-class researchers have developed intelligent programs from different angles. Among the implementations, scholars have noticed recurring patterns in solving these kinds of problems that led to a standardized field that we today label machine learning (ML).
As the study of ML has matured, the tools for performing machine learning have become more standardized, robust, high-performing, and scalable. This is where TensorFlow comes in. This software library has an intuitive interface that lets programmers dive into using complex ML ideas.

## 机器学习代写|tensorflow代写|Machine-learning fundamentals

Have you ever tried to explain to someone how to swim? Describing the rhythmic joint movements and fluid patterns is overwhelming in its complexity. Similarly, some software problems are too complicated for us to easily wrap our minds around. For this task, machine learning may be the tool to use.

Handcrafting carefully tuned algorithms to get the job done was once the only way of building software. From a simplistic point of view, traditional programming assumes a deterministic output for each input. Machine learning, on the other hand, can solve a class of problems for which the input-output correspondences aren’t well understood.
Machine learning is characterized by software that learns from previous experiences. Such a computer program improves performance as more and more examples are available. The hope is that if you throw enough data at this machinery, it’ll learn patterns and produce intelligent results for newly-fed input.

Another name for machine learning is inductive learning, because the code is trying to infer structure from data alone. The process is like going on vacation in a foreign country and reading a local fashion magazine to figure out how to dress. You can develop an idea of the culture from images of people wearing local articles of clothing. You’re learning inductively.

You may never have used such an approach when programming, because inductive learning isn’t always necessary. Consider the task of determining whether the sum of two arbitrary numbers is even or odd. Sure, you can imagine training a machine-learning algorithm with millions of training examples (outlined in figure 1.1), but you certainly know that this approach would be overkill. A more direct approach can easily do the trick.

## 机器学习代写|tensorflow代写|Learning and inference

Suppose that you’re trying to bake desserts in an oven. If you’re new to the kitchen, it can take days to come up with both the right combination and perfect ratio of ingredients to make something that tastes great. By recording a recipe, you can remember how to repeat a dessert.

Machine learning shares the idea of recipes. Typically, we examine an algorithm in two stages: learning and inference. The objective of the learning stage is to describe the data, which is called the feature vector, and summarize it in a model. The model is our recipe. In effect, the model is a program with a couple of open interpretations, and the data helps disambiguate it.
NOTE A feature vector is a practical simplification of data. You can think of it as a sufficient summary of real-world objects in a list of attributes. The learning and inference steps rely on the feature vector instead of the data directly.
Similar to the way that recipes can be shared and used by other people, the learned model is reused by other software. The learning stage is the most time-consuming. Running an algorithm may take hours, if not days or weeks, to converge into a useful model, as you will see when you begin to build your own in chapter 3. Figure $1.4$ outlines the learning pipeline.

The inference stage uses the model to make intelligent remarks about neverbefore-seen data. This stage is like using a recipe you found online. The process of inference typically takes orders of magnitude less time than learning; inference can be fast enough to work on real-time data. Inference is all about testing the model on new data and observing performance in the process, as shown in figure $1.5$.

## 有限元方法代写

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。

## 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。