### 机器学习代写|tensorflow代写|TensorFlow essentials

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|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.

## 机器学习代写|tensorflow代写|Representing tensors

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。

## 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。