### 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

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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。