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 $1.5$.

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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。